Machine learning and deep learning based predictive quality in manufacturing: a systematic review

Tercan, Hasan; Meisen, Tobias Article — Published Version Machine learning and deep learning based predictive quality in manufacturing: a systematic review Journal of Intelligent Manufacturing Provided in Cooperation with: Springer Nature Suggested Citation: Tercan, Hasan; Meisen, Tobias (2022) : Machine learning and deep learning based predictive quality in manufacturing: a systematic review, Journal of Intelligent Manufacturing, ISSN 1572-8145, Springer US, New York, NY, Vol. 33, Iss. 7, pp. 1879-1905, https://doi.org/10.1007/s10845-022-01963-8 This Version is available at: https://hdl.handle.net/10419/311761 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Intelligent Manufacturing (2022) 33:1879–1905 https://doi.org/10.1007/s10845-022-01963-8 Machine learning and deep learning based predictive quality in manufacturing: a systematic review Hasan Tercan1 ·Tobias Meisen1 Received: 22 December 2021 / Accepted: 5 May 2022 / Published online: 28 May 2022 © The Author(s) 2022 Abstract With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements, there is enormous potential to use machine learning and deep learning techniques for quality assurance. In this context, predictive quality enables manufacturing companies to make data-driven estimations about the product quality based on process data. In the current state of research, numerous approaches to predictive quality exist in a wide variety of use cases and domains. Their applications range from quality predictions during production using sensor data to automated quality inspection in the field based on measurement data. However, there is currently a lack of an overall view of where predictive quality research stands as a whole, what approaches are currently being investigated, and what challenges currently exist. This paper addresses these issues by conducting a comprehensive and systematic review of scientific publications between 2012 and 2021 dealing with predictive quality in manufacturing. The publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use. In this process, key insights into the scope of this field are collected along with gaps and similarities in the solution approaches. Finally, open challenges for predictive quality are derived from the results and an outlook on future research directions to solve them is provided. Keywords Industry 4.0 ·Predictive quality ·Machine learning ·Deep learning ·Manufacturing ·Quality assurance ·Artificial intelligence Introduction In the present era of Industry 4.0 and the digitization of the manufacturing industry, new technological possibilities are emerging that contribute to strengthen companies’ competitiveness. Especially the combinations of new communication technologies with state-of-the-art methods from the fields of machine learning (ML) and deep learning (DL) enable promising applications for data-driven, smarter manufacturing (Shang & You, 2019; Tao et al., 2018). One area that can strongly benefit from these developments is quality assurance. It involves activities across the product lifecycle to ensure that the requirements on the quality of produced prodBHasan Tercan [email protected] Tobias Meisen [email protected] 1University of Wuppertal, Rainer-Gruenter-Strasse 21, Wuppertal, Germany ucts are met (Hehenberger, 2020; Pfeifer & Schmitt, 2021). ML and DL methods offer ways to support these activities by enabling data-driven, automated quality analyses. Their application in this field is referred to as predictive quality (Nalbach et al., 2018; Schmitt et al., 2020b). Predictive quality solutions are built upon data from the manufacturingprocess.Byextractingrecurringpatternsfrom the data and relating them to quality measurements, predictive quality enables the data-driven estimation of the product quality based on process data. The estimations serve as a decision-making basis for quality enhancing measures, such as adjusting the process parameters for avoiding rejects (Schmitt et al., 2020b). The common approach to predictive quality includes four main steps: the formulation of the manufacturing process and target quality, the selection and collection of process and quality data, the training of a ML/DL model, and the use of the model for estimations as a basis for decisions (schematically illustrated in Fig. 1). In thiscontext,predictive quality mainlycomprises methods for supervised ML. 123 1880 Journal of Intelligent Manufacturing (2022) 33:1879–1905 Fig. 1 Predictive quality approach: for a selected manufacturing process (1), relevant process and quality data is collected (2) and used as a basis for training a ML model (3). The trained model is used to perform quality estimations for decision support (4) In the current manufacturing research, there exist many examples that demonstrate the feasibility of ML or deep learning based predictive quality, ranging from inline fault predictions (Mayr et al., 2019) to automated quality inspections (Schmitt et al., 2020a). The addressed manufacturing processes and quality criteria are manifold, such as the prediction of part cracks in deep drawing (Meyes et al., 2019), the estimation of roughness in laser cutting (Tercan et al., 2017;Zhang&Lei,2017),orthedetectionofporositydefects in additive manufacturing (Zhang et al., 2019a). Though these use cases are different, their solution approaches have similarities in terms of the data and methods used. However, itis noticeable that often theusecases are considered in isolation, making it difficult to compare the proposed approaches. Hence, it is not clear where the overall predictive quality researchcurrently stands, which methods are currently investigated, what the limitations are, and in which directions the research should go. In this paper, we address these issues by conducting a systematic review of the publications that address the field of predictive quality. We see the timing for suchareviewasappropriatebecause,ontheonehand,thereis a sufficiently large amount of published papers from which we can draw these insights. On the other hand, there are currently no adequate papers dealing with this topic in its entirety. Although studies with similar investigations exist (see “Related survey paper” section), they either cover a broader scope (e.g. applications of ML for the production context in general (Fahle et al., 2020; Sharp et al., 2018)or they are no longer up-to-date as their publication dates back several years (Köksal et al., 2011; Rostami et al., 2015). Due to these observations, we define the primary goal of this review: providing a comprehensive overview of scientific publications from 2012 to 2021 that address predictive quality approaches in manufacturing. Our perspective on this field focuses on its common concepts depicted in Fig. 1. After collecting the relevant publications and building a corresponding corpus, we extract information about their use cases, the manufacturing processes and quality criteria they address and the data bases and ML methods they use. The goal is to categorize the publications along these concepts and to answer the following three driving questions •Q1: What are the addressed manufacturing processes and quality criteria? Our goal is on the one hand to discover the scope of the field and the possible applications of predictive quality, and on the other hand to identify similarities and gaps in the domains. •Q2: What are the characteristics of the data used for model training? Predictive quality is based on process data. We aim to find out the common data sources used in the publications, the variables selected for ML model training, and the modality of the data. •Q3: Which machine learning models of supervised learning are commonly trained? We aim to discover which supervised learning problems are addressed, which models of ML and DL are trained for the quality estimations, and if they are compared with each other in the publications. From these questions, we derive key insights and open challenges for predictive quality and provide an outlook on future 123 Journal of Intelligent Manufacturing (2022) 33:1879–1905 1881 research directions that we believe will increase its prevalence in research and industry. The paper is structured as follows. In “Predictive quality” section, we introduce predictive quality by first defining the term and then describing the underlying machine learning models and tasks. In “Related survey paper” section, we provide an overview of related survey papers that deal with similar topics. We then present the methodology underlying our study in “Study methodology” section. Here, we define the categories by which we evaluate the publications and formulate the search queries and criteria for selecting them. “Results” section presents the result of our study with regard to the stated questions. On the basis of these results, “Key insights and challenges” section provides key findings and identified gaps in predictive quality research. In “Future research directions” section, we envision future research directions to master them. “Summary” section summarizes this review. Predictive quality Regarding the use of data-driven methods with reference to product and process quality in production, different terms and terminologies are used to describe them in the state of research and technology. Examples are data analytics, predictive analytics,machine learning for quality prediction (Mayr et al., 2019)orpredictive model-based quality inspection (Schmitt et al., 2020a). Predictive quality is another term that covers this broad field of research. The definitions by Schmitt et al. (2020b) and Nalbach et al. (2018) highlight two key aspects of predictive quality: product-related quality as well as the data-driven prediction of it. Schmitt et al. (2020b) define predictive quality as “enabling the user to makeadata-drivenpredictionofproduct-andprocess-related quality” with the goal of “acting prescriptively on the basis of predictive analyses”. Nalbach et al. (2018) take a similar view – their definition of predictive quality includes methods that use data to “identify statistical patterns to foresee future developmentsconcerningthequality of a product”. Although thetermprediction isalsoverybroad,itlimitstherangeofuse cases of data-driven methods. For example, it does not cover use cases for ML based quality inspection where the goal is to detect faults that have already occurred in the process rather than providing future-oriented quality predictions. Withregardtodata-drivenmethods,itisalsothecasethatparticularly machine learning and deep learning methods have been the central focus of research in recent years. In our consideration, we therefore also concentrate on these two fields. Relying on these definitions, we define the term predictive quality as the basis for our literature search and specify the terminologies as well as their scope in the context of this paper: Predictive quality comprises the use of machine learninganddeeplearningmethodsinproductiontoestimate product-related quality based on process and product data with the goal of deriving quality-enhancing insights. The following remarks provide further concretization of the definition: •The term estimation includes prediction as well as classification of quality. •The product-related quality can be a fixed quality parameter as well as known product faults. •By process and product data, we mean product characteristics,process parameters, process states,and planning information By our definition, predictive quality is not limited to the production phase of a product, but also is applied in the product or process planning phase - for example, in the design of a production process using test trials or a DoE-based feasibility analysis using process simulations. It should also be noted that our definition does not cover the notions of anomalies and anomaly detection. Anomalies in manufacturing are events that differ from normal behavior and as such are not initially associated with a known defect or quality degradation (Lopez et al., 2017). Anomaly detection therefore involves different methods than the ones considered in the context of this paper. For the sake of limiting the scope of our study, we exclude publications that deal with anomalies and anomaly detection. As predictive quality involves the detection or prediction of quality, it mainly comprises methods of supervised learning. Supervised learning has the goal of estimating a numerical or categorical target variable on the basis of selected input variables (regression respectively classification). For supervised learning, methods of both fields machine learning and deep learning can be used: •Machine learning methods such as regression analysis that detect linear and quadratic correlations and models that can handle complex and non-linear estimations such as support vector machine (SVM) and feed-forward artificial neural network (ANN) (also called multilayer perceptron (MLP)), and interpretable graphical models such as decision trees. In addition, a variety of ensemble methods (e.g. random forest) exist that comprise several individual models. •Deep learning models that are based on deep ANNs and that have marked major milestones in the AI research in recent years. These include, for example, convolutional neural networks (CNNs) which are established in computer vision and image recognition (Redmon et al., 123 1882 Journal of Intelligent Manufacturing (2022) 33:1879–1905 2016; Szegedy et al., 2015), as well as recurrent neural networks such as long short-term memory (LSTM) (Hochreiter & Schmidhuber, 1997) or transformer networks, which represent the state of the art in natural language processing areas such as speech recognition or machine translation (Vaswani et al., 2017; Xiong et al., 2016). The learning methods used for predictive quality are diverse and often depend on the purpose of the application. Following the discussions by Köksal et al. (2011) and Rostamietal.(2015) on the purposes of data-driven quality assurance, we list three tasks of machine and deep learning for predictive quality: •Quality description The identification, evaluation and interpretation of relationships between process variables and product quality. The primary goal is to gain insights about interrelationships in the process. •Quality prediction The model-driven estimation of a numerical quality variable on the basis of process variables. The goal here is the prediction of product quality, either for decision support or for automation. •Quality classification Analogous to quality prediction, this involves model-driven estimation of categorical (binary or nominal) quality variables. An example is the prediction of certain product defect types. In this paper, we study publications that primarily address these three tasks, focusing especially on quality prediction and classification. The use of such model estimations can be very diverse and range from pure knowledge gain for the user to automated feedback into the system. Generally, a process improvement is initiated which leads to the fulfillment of the specified product quality or its improvement. Possible improvements include the reduction of the reject rate through early intervention in the production process, the stabilization of the process for production in tighter tolerances (Schmitt et al., 2020a), or the optimization of process parameters (Weichert et al., 2019). Related survey paper A review similar to ours is provided by Köksal et al. (2011). The authors conducted an extensive literature review of data mining applications for quality improvement tasks in manufacturing and categorized them according the stated predictive quality tasks. Rostami et al. (2015) proposed a similar approach with a focus on applications of SVMs. Both studies date back several years and thus do not cover recent advances in this field. More recently, Weichert et al. (2019) reviewed machine learning applications for production process optimization with regard to productor process-specific metrics. The study shows that optimization approaches are mostly based on root-cause analysis and fault diagnosis in production plants or by the combination of ML methods with optimization methods. Although the study has overlaps with our work, the authors mainly address approaches to process optimization. In our study, we focus as previously described onapproachesforqualityestimationandevaluatethem based on the data and methods used. In the course of our literature search, we also identified survey papers that conduct similar investigations on machine learning applications but with a different scope. For example, there already exist extensive studies on the use of artificial intelligence and machine learning techniques in the production and manufacturing context (Fahle et al., 2020; Mayr et al., 2019; Shang & You, 2019; Sharp et al., 2018). Shang and You (2019) provided an overview of recent advances in data analytics for different application task areas such process monitoring and optimization. They also discussed the works in terms of usability and interpretability for control tasks. Fahle et al. (2020) and Mayr et al. (2019) studied machine learning applications in different task scenarios such as process planning and control. Sharp et al. (2018) focused on cross-domain applications in the product lifecycle. There are also surveys that deal with related research fields such as ML-based predictive maintenance (Dalzochio et al., 2020; Zonta et al., 2020), condition monitoring (Serin et al., 2020b) and machine fault diagnosis (Ademujimi et al., 2017). Our paper clearly differs from the mentioned papers as it reviews approaches that primarily address the quality of the products produced. Study methodology Our review is based on the guiding questions defined in “Introduction” section. First, we translated the questions into several categories which we used to categorize and summarize the publications. The categories are listed in Table 1. In the next step, we performed a literature search in the databases of Web of Science and ScienceDirect. As shown in “Predictive quality” section, there are different terms and terminologies for predictive quality that are used in publications. The same holds for the very broad application domain of manufacturing. In order to cover this broad scope in the search, we used different terms for the search queries and divided them into three categories: the predictive quality terminology, the machine learning field, and the manufacturing domain. Table 2lists all defined search terms. We formulated search queries to find publications that contain at least one term from each of the three categories. In addition, we filtered the results based on the publication year from 2012 to 2021 (we performed the search on June 29, 2021). This 123 Journal of Intelligent Manufacturing (2022) 33:1879–1905 1883 Table 1 Categories by which the publications are reviewed Question Category Description Q1 Use case Main use case of paper and purpose of predictive quality Process Addressed manufacturing process (e.g. laser welding, deep drawing) Category Category of process according to DIN 8580 (e.g. cutting, forming) Criterion Estimated quality criterion (e.g. product dimensions, OK/NOK quality) Q2 Data source Main source of process data (e.g. running production, simulation) Input variables Data parameters used for the model training (e.g. sensor data) Data modality Data types of gathered data (e.g. time series, categorical data) Data amount Number of observations used for model training Q3 Learning task Formulated learning task (e.g. classification, regression) Prime model Primarily used (or best performing) ML/DL model (e.g. SVM, CNN) Baselines ML/DL-models used for comparison to prime model (e.g. SVM, CNN) Table 2 Terms used for literature search Category Search terms Predictive Quality Predictive quality, predictive analytics, fault prediction, fault classification, defect prediction, quality prediction, smart manufacturing Learning Deep learning, neural network, machine learning Domain Manufacturing, production, industrial, engineering, automation, assembly Fig. 2 Methodology of the literature search literature search resulted in a large list of 1.261 (potentially relevant) publications for our review (see Fig. 2). With the aim of identifying only publications that fit the scope of the paper, we first screened the publications based on their title and abstract. In the course of this screening, many publications were discarded as they lay in the fields of predictive maintenance, fault diagnosis, remaining useful lifetime prediction, software defect prediction, water quality prediction, process engineering or civil engineering. The result of this pre-selection were 144 remaining publications. We then read through the remaining publications in detail and categorized them according to the defined categories in Table 1. During this process, we excluded publications from our consideration that met the following exclusion criteria: •Publications that do not contain information about the addressed manufacturing process or the data basis •Survey papers and literature studies •Publicationsthatdonotperformanydevelopment,implementation or evaluation of methods •Publications that are not accessible to us After all, there were 81 publications which were selected and considered for our study. Table 10 in Appendix A lists all of them, sorted by publication year. The majority (69%) are published in journals. 31% are published in the scope of scientific conferences. Figure 3shows the number of publications per year. It clearly illustrates that the number of publications has been constantly increasing over the last years. It can also be assumed that this trend will continue in the further course of 2021 and 2022. Results In this chapter, we present the results of our study along the guiding questions formulated in “Introduction” section. 123 1884 Journal of Intelligent Manufacturing (2022) 33:1879–1905 Fig. 3 Number of publications per year. The literature search was performed on June 29, 2021 First, we begin by categorizing the publications according to the manufacturing processes and quality criteria addressed (“Manufacturing process types and quality criteria” section). Then, we look at the data bases used for quality estimations (“Data Bases and Characteristics” section) and what learning models are trained and investigated (“Machine learning methods” section). Manufacturing process types and quality criteria Predictivequalityapproachesarebeinginvestigatedinawide variety of manufacturing processes. We therefore perform a categorization of the processes based on DIN 8580:2003-09 (2003). In the norm, the processes are divided into six main groups: primary shaping,forming,cutting,joining,coating and changing of material properties. Since there are also publications dealing with additive manufacturing,assembly processes or multi-stage processes, we add these three categories to our review. Considering how much each process category is represented in predictive quality research, there are significant differences and a large imbalance between them. Figure 4shows the number of publication per category. While cutting comprises the largest group with 26 publications, there is no publication that primarily addresses processes for changing material properties. In the following, we will focus the categories in detail. For each of the categories, we also analyze which quality criteria are used in the publications as the estimated target variables. Cutting Cutting includes a variety of manufacturing processes in which a commonly metallic workpiece is fractured by separating a portion from it. Table 3lists the addressed cutting processes and quality criteria. It can be seen that most research aims to determine quality characteristics that reflect the shape of the finished product, while the surface roughness represents the overwhelming majority of them. In turning applications, for example, some approaches used ML methods such as multivariate regression or ANNs to Fig. 4 Number of publications for each manufacturing process type estimate the roughness based on gathered sensor data (Du et al., 2021; Elangovan et al., 2015; Moreira et al., 2019) and/or machine parameters (Acayaba & de Escalona, 2015). Tušar et al. (2017) developed an automated quality control of a turning and soldering process, predicting both the roughness as well as several soldering defects based on recorded camera images. Vrabel et al. (2016) also proposed an inline process monitoring of the surface roughness quality. Other applications of predicting the roughness are found in the field ofmilling(Hossain&Ahmad,2014;Serinetal.,2020a),honing (Gejji et al., 2020; Klein et al., 2020), diamond wire saw cutting (Kayabasi et al., 2017), or laser cutting (Tercan et al., 2016,2017; Zhang & Lei, 2017). An important quality criterion in drilling processes is the hole diameter, which can also be predicted based on sensor data (Neto et al., 2013; Schorr et al., 2020a,b). Other than that, Nguyen et al. (2020) predicted the waviness of the kerf in laser cutting by training an MLP on process parameters (e.g. gas pressure and laser power). Furthermore, it was shown that ML is capable of predicting the material removal rate in cylindrical grinding of hardened steel (Varma et al., 2017) and chemical mechanical polishing (Yu et al., 2019). Joining Joining processes comprise the second largest field of addressed manufacturing processes (14 publications) . Table 3shows that mainly applications in welding were investigated. In laser welding, machine learning models (commonly ANNs) were trained on welding parameters (e.g. laser power, welding speed) or sensor data (e.g. light intensity) to predict quality values such as the tensile strength (Yu et al., 2016), weld bead dimensions (Ai et al., 2016;Lei et al., 2019), residual stress (Dhas & Kumanan, 2014)orto classify quality types captured with camera images (Yu et al., 2020). Similar approaches were conducted in spot welding (Hamidinejad et al., 2012; Martín et al., 2016) and ultrasonic welding (Li et al., 2020b; Natesh et al., 2019). Li et al. (2020a) and Gyasi et al. (2019) presented ANN-based inline quality control systems in welding processes. Goldman et al. (2021) conducted interpretability analysis of CNNs trained 123 Journal of Intelligent Manufacturing (2022) 33:1879–1905 1885 Table 3 Considered cutting and joining processes and quality criteria (in parenthesis: number of publications) Category Process Quality criteria Cutting Turning (6) Surface roughness (Acayaba & de Escalona, 2015;Duet al., 2021; Elangovan et al., 2015; Moreira et al., 2019; Tušar et al., 2017), machinability (Lutz et al., 2020) Drilling (6) Diameter (Neto et al., 2013), Schorr et al. 2020a,2020b, surface roughness (Vrabel et al., 2016), hole defects (Jiao et al., 2020), surface gap (Bustillo et al., 2018) Laser cutting (4) Surface roughness (Tercan et al., 2016,2017; Zhang & Lei, 2017), kerf waviness (Nguyen et al., 2020) Milling (3) Surface roughness (Hossain & Ahmad, 2014; Serin et al., 2020a), geometric deviation (de Oliveira Leite et al., 2015) Honing (2) Curface roughness (Gejji et al., 2020; Klein et al., 2020) C. M. polishing (1) Material removal rate (Yu et al., 2019) Diamond wire cutting (1) Surface roughness (Kayabasi et al., 2017) Grinding (1) Surface roughness (Varma et al., 2017) Laser micro grooving (1) Groove geometry (Zahrani et al., 2020) Laser machining (1) Dimensions (McDonnell et al., 2021) Joining Laser welding (4) Weld bead dimensions (Ai et al., 2016; Lei et al., 2019), tensile strength (Yu et al., 2016), quality types (Yu et al., 2020) Resistance spot welding (3) Tensile shear strength (Hamidinejad et al., 2012), tensile shear load bearing (Martín et al., 2016), welding deformation (Li et al., 2020a) Ultrasonic welding (3) Quality types (Goldman et al., 2021;Lietal.,2020b), tensile strength (Natesh et al., 2019) Gas metal arc welding (2) Weld penetration (Gyasi et al., 2019), weld bead dimensions (Wang et al., 2021) Gluing (1) Glue volume (Dimitriou et al., 2020) Welding (1) Residual stress (Dhas & Kumanan, 2014) on welding sensor data. In contrast Wang et al. (2021) trained a CNN on line camera images for quality estimation. Other than welding, Dimitriou et al. (2020) estimated the glue volume based on 3D laser topology scans in a gluing process. Primary shaping Primary shaping involves processes in which a body with a defined shape is produced from a shapeless material. Ten publications deal with this field (see table 4). The ones that lie in the field of casting proposed approaches for detecting casting defects on the product, such as by training CNNs on X-ray images (Ferguson et al., 2018) or MLPs on sensor data (Kim et al., 2018; Lee et al., 2018). In injection molding, data from machine parameters (e.g. temperature, packing pressure) was used for predicting quality values such as the product dimensions (Ke & Huang, 2020) or product weights (Ge et al., 2012). Alvarado-Iniesta et al. (2012) used a recurrent neural network to make warpage estimations for new parameter combinations. Garcia et al. (2019) predicted future product geometries of plastics tubes in a plastics extrusion process. Furthermore, two works lie in the field of spinning, where the goals were to predict the yarn quality in form of the count-strength-product (Nurwaha & Wang, 2012) or the leveling action point (Abd-Ellatif, 2013) with MLPs. Forming Forming involves manufacturing processes in which raw parts are transformed into a different shape withoutmaterialbeingaddedorremoved.Amongthe10 publications that lie in this field (see table 4), the ones addressing metal rolling processes noticeably differ from the works mentioned so far, as most of them aimed at in-line quality estimations in the rolling process. For example, Yun et al. (2020), Li et al. (2018) and Liu et al. (2021) proposed CNNbasedqualityinspectionsystemsbydetectingandclassifying surface defects in line camera images. Ståhl et al. (2019)used inline geometry measurements to train LSTM networks and Lieber et al. (2013) made inline NOK quality predictions based on ultrasonic measurements. In sheet metal forming, Meyes et al. (2019) investigated LSTMs on sensor data for the inline prediction of part defects, while Essien and Giannetti (2020) trained them to estimate the machine speed. In contrast, Dib et al. (2020) made use of simulated experiments for ML-based part defect estimation. Other approaches were 123 1886 Journal of Intelligent Manufacturing (2022) 33:1879–1905 Table 4 Considered primary shaping, forming and additive manufacturing processes and quality criteria (in parenthesis: number of publications) Process type Process Quality criteria Primary shaping Casting (3) Casting defects (Ferguson et al., 2018; Kim et al., 2018; Lee et al., 2018) Injection molding (3) Product dimensions (Ke & Huang, 2020), product weight (Ge et al., 2012), warpage (Alvarado-Iniesta et al., 2012) Plastics extrusion (2) Product geometry (Garcia et al., 2019), yield stress (Mulrennan et al., 2018) Spinning (2) Yarn strength (Nurwaha & Wang, 2012), sliver evenness (Abd-Ellatif, 2013) Forming Metal rolling (5) Surface defects (Li et al., 2018;Lieberet al., 2013; Liu et al., 2021; Yun et al., 2020), slab geometry (Ståhl et al., 2019) Sheet metal forming (3) Part defects (Dib et al., 2020; Meyes et al., 2019), machine speed (Essien & Giannetti, 2020) Forging (1) Process feasibility (Ciancio et al., 2015) Textile draping (1) Shear deformation (Zimmerling et al., 2020) Additive manuf. Laser powder bed fusion (4) Geometric deviation (Zhu et al., 2020), inherent strain (Li & Anand, 2020), structural defects (Bartlett et al., 2020), single-track width (Gaikwad et al., 2020) Direct metal deposition (1) Volume porosity (Zhang et al., 2019a) Fused deposition modeling (2) Tensile strength (Zhang et al., 2018, 2019b) PLA 3D printing (1) Surface roughness (Li et al., 2019) investigated in simulations of impression-die forging (Ciancio et al., 2015) or textile draping (Zimmerling et al., 2020). Additive manufacturing Eight publications deal with predictive quality in additive manufacturing processes (see table 4). As additive manufacturing enables rapid prototyping, two of them are located in the design phase of products. Here, process simulations were used to train ANNs for fast predicting the inherent strain (Li & Anand, 2020) or geometric deviations (Zhu et al., 2020) of the product. Beyond that, ML can also be used in the realization phase, for example to make quality predictions based on optical measurements (Gaikwad et al., 2020; Bartlett et al., 2020) or machine sensors (e.g. IR, vibration) (Li et al., 2019; Zhang et al., 2018, 2019b). An in-line capability of quality monitoring in the process was demonstrated by Zhang et al. (2019a). Assembly Regarding the 5 publications dealing with assembly (see Table 5), it becomes apparent that they are mainlyconcernedwithML-basedclassification of successful and unsuccessful assembly tasks. Examples are the detection of functioning products in manual assembly (Wagner et al., 2020) or correct positioning in SMT assembly (Schmitt et al., 2020a) by using virtual quality inspection systems. The assembled products as well as the data used may also be very different. While Sarivan et al. (2020) used acoustic signals to make a quality prediction for the connection of wire plugs, Martinez et al. (2020) used line camera images for detecting correctly fastened screws. Lastly, Doltsinis et al. (2020) detected successfull operations on the basis of robotic force signatures and machine sensors. Coating In coating (4 publications, Table 5), manufacturing processes are involved to apply an adhesive layer of shapeless material to the surface of a certain workpiece. In dispensing, for example. Oh et al. (2019) proposed a SVMbased defect detection method for realtime visual quality inspection. Hsu and Liu (2021) trained CNNs on machine sensor data for OK/NOK classification of electric wafer quality. In contrast, some approaches are trained only on parameterizations of the process, which was shown for lacquering (Thomas et al., 2018) and car bodywork painting (Kebisek et al., 2020). Multi-stage There are also publications which are not concerned with a single manufacturing process but multistage processes that comprise several types (4 publications, see Table 5). From a machine learning perspective, the challenge here is handling the increased complexity and number of data sources. For example, Liu et al. (2020b) investigated DL-methods (e.g. LSTMs) for quality prediction of a larger production line based on multimodal sensor data. The data 123 Journal of Intelligent Manufacturing (2022) 33:1879–1905 1893 Table 9 Overview of addressed learning tasks and used prime models in all publications Learning task ML-model Publications Classification CNN Ferguson et al. (2018), Goldman et al. (2021), Hsu and Liu (2021), Jun et al. (2021), Li et al. (2018), Liu et al. (2021), Martinez et al. (2020), Sarivan et al. (2020), Yun et al. (2020), Zhang et al. (2019a) Decision tree Tercan et al. (2016,2017) Ensemble model Gejji et al. (2020), Kim et al. (2018), Thomas et al. (2018) K-NN Lieber et al. (2013) MLP Bustillo et al. (2018), Dib et al. (2020), Ke and Huang (2020), Kebisek et al. (2020), Lee et al. (2018), Wagner et al. (2020), Yu et al. (2020) Naive Bayes Bartlett et al. (2020) Random forest Zahrani et al. (2020) RNN Liu et al. (2020b), Meyes et al. (2019) SVM Doltsinis et al. (2020), Oh et al. (2019), Schmitt et al. (2020a) Regression ANFIS Hossain and Ahmad (2014), Moreira et al. (2019), Varma et al. (2017), Zhang and Lei (2017) CNN Dimitriou et al. (2020), Wang et al. (2021), Zhu et al. (2020), Zimmerling et al. (2020) Ensemble model Li et al. (2019) Extra tree Schorr et al. (2020a) EML Nguyen et al. (2020) GA-BPNN Ai et al. (2016) Linear regression Elangovan et al. (2015) MLP Abd-Ellatif (2013), Acayaba and de Escalona (2015), Ciancio et al. (2015), Du et al. (2021), Gyasi et al. (2019), Hamidinejad et al. (2012), Jiao et al. (2020), Kayabasi et al. (2017), Lei et al. (2019), de Oliveira Leite et al. (2015), Li et al. (2020a,2020b), Li and Anand (2020), Lutz et al. (2020), McDonnell et al. (2021), Natesh et al. (2019), Neto et al. (2013), Nurwaha and Wang (2012), Papananias et al. (2019), Serin et al. (2020a), Turetskyy et al. (2021), Vrabel et al. (2016), Yu et al. (2016) NN-GA-PSO Dhas and Kumanan (2014) Quadratic regression Martín et al. (2016) Random forest Klein et al. (2020), Mulrennan et al. (2018), Schorr et al. (2020b), Tušar et al. (2017), Yu et al. (2019) Relevance vector machine Ge et al. (2012) RNN Alvarado-Iniesta et al. (2012), Essien and Giannetti (2020), Ståhl et al. (2019), Zhang et al. (2018), Zhang et al. (2019b) SeDANN Gaikwad et al. (2020) SVM Garcia et al. (2019) Fig. 7 Proportions of ML models (prime) used in publications in 2020 and 2021 For example, Jun et al. (2021) showed that a combination of CNNs with convolutional variational autoencoders (CVAE) provides better performances for class-imbalanced data than variations without CVAE. Yun et al. (2020) included wellknown deep learning models such as AlexNet, VGG-16, and ResNet-50 in their comparisons. Liu et al. (2021)alsodeveloped their own architecture called TruingDet based on a Faster R-CNN and deformable convolutions and compared it to other state-of-the-art R-CNN models. In addition, other methods used for comparison with CNNs are MLP and SVM (Lietal.,2018), several computer vision algorithms (Jun et al., 2021), and shapelet forests (Hsu & Liu, 2021). Recurrent neural network (RNN) Sevenpublicationuse a recurrent neural network architecture as a prime model for quality estimations. Since RNNs are suitable for application 123 1894 Journal of Intelligent Manufacturing (2022) 33:1879–1905 on time-dependent or sequential data, they are commonly applied on gathered time series data (Meyes et al., 2019; Essien & Giannetti, 2020; Ståhl et al., 2019; Zhang et al., 2018,2019b). Some of the publications use them for the binary classification of defects (Liu et al., 2020b; Meyes et al., 2019), others for the regression of quantities such as material warpage (Alvarado-Iniesta et al., 2012), machine speed (Essien & Giannetti, 2020), or tensile strength (Zhang et al., 2018,2019b). Regarding the type of RNNs, the focus of the publications is clearly on LSTM network architectures (Essien & Giannetti, 2020; Liu et al., 2020b; Meyes et al., 2019; Ståhl et al., 2019; Zhang et al., 2018,2019b). For example, Meyes et al. (2019) used bidirectional LSTM which allows a time series to be processed in a forward run andabackwardruntogainbetterclassificationresults.Essien and Giannetti (2020) used a convolutional LSTM encoderdecoder architecture to forecast future values of the series. The authors therefore compared their approach with another CNNarchitectureand with an ARIMAmodel.Otherbaseline models used for the comparison with RNNs are SVM (Liu et al., 2020b; Ståhl et al., 2019; Zhang et al., 2018,2019b), random forest (Ståhl et al., 2019; Zhang et al., 2018,2019b), XGBoost (Liu et al., 2020b), polynomial regression (Zhang et al., 2018) and/or logistic regression (Ståhl et al., 2019). Non-linear ML models Some publications used traditional machine learning methods that are well suited for nonlinear decision making. These include SVMs for the classification of defects (Oh et al., 2019) and operation success (Doltsinis et al., 2020; Schmitt et al., 2020a) as well as for the numerical estimation of product geometries (Garcia et al., 2019), relevance vector machine (RVM) for estimating product weights (Ge et al., 2012), decision trees (Tercan et al.,2016,2017)andquadratic regression(Martínet al.,2016) for interpretable quality estimations, and both K-NN (Lieber et al., 2013) and naive bayes (Bartlett et al., 2020) for defect classification. With regard to their evaluation, these models are typically compared to each other (Garcia et al., 2019; Lieber et al., 2013; Oh et al., 2019; Schmitt et al., 2020a) and with other models such as MLPs (Oh et al., 2019; Garcia et al.,2019;Martín et al., 2016), gradient boosted trees (Schmitt et al., 2020a), or generalized additive models (GAM) (Martín et al., 2016). Ensembles Ensemble methods involve the combination of multiple learning models, thereby aggregating their decisions to make a prediction. In some cases, extensive comparisons were conducted to show that ensembles can perform better than single models (Gejji et al., 2020; Kim et al., 2018; Thomas et al., 2018; Li et al., 2019). Probably the most popular ensemble method is the random forest, which is used for classification (Zahrani et al., 2020) and regression (Klein et al., 2020; Mulrennan et al., 2018; Schorr et al., 2020b; Tušar et al., 2017; Yu et al., 2019). The random forest is compared with single decision trees (Tušar et al., 2017), bagged trees (Mulrennan et al., 2018) and models such as MLP, CNN and SVM (Schorr et al., 2020b). Variants and hybrid models with neural networks Some publications used methods that are hybrids or variants of artificial neural networks. These include ANFIS, which were proposed to estimate the surface roughness in cutting processes (Hossain & Ahmad, 2014; Moreira et al., 2019; Varma et al., 2017; Zhang & Lei, 2017), ANN variants such as sequential decision analysis neural network (SeDANN) (Gaikwad et al., 2020) and extreme machine learning (Nguyen et al., 2020), and hybrid models of neuFig. 8 Occurrences of ML models as baselines in all publications 123 Journal of Intelligent Manufacturing (2022) 33:1879–1905 1895 ral networks and evolutionary computation methods such as genetic algorithm optimized neural network (GA-BPNN) (Ai et al., 2016) and neuro evolutionary hybrid model with genetic algorithm and particle swarm optimization (NN-GA-PSO) (Dhas & Kumanan, 2014). Accordingly, the approaches were also frequently compared with regular MLPs (Hossain & Ahmad, 2014; Nguyen et al., 2020;Varma et al., 2017; Zhang & Lei, 2017). Gaikwad et al. (2020) additionallycomparedtheSeDANNmodelwithotherestablished models such as CNN, LSTM, CART, and general linear model (GLM). Key insights and challenges The previous chapter presented the results of our extensive literature review on predictive quality in manufacturing, in whichwe categorizedthe publications alongthe three dimensions of manufacturing processes and criteria, data basis for model training, and learning models used. Based on the obtained results, we will provide in the following our main findings and identified gaps with respect to the posed driving questions. Scenarios and manufacturing domains Applications of predictive quality All in all, the reviewed publications show that machine and deep learning methods prove to be versatile and powerful tools for data-driven quality estimations. In this context, the methods are very often validated with respect to their prognostic quality and accuracy for the particular use cases. The results show that predictive quality has great potential value for quality assurance and manufacturing process improvement. Although many publications do not clearly formulate how the proposed methods are intended to be used in the manufacturing process,we have identified threeapplicationscenariosof predictivequality. The firstisprocessdesignsupportandprocess optimization based on simulation data or process parameters. Here, predictive quality methods are used to estimate product qualitybased on setting parameters.The estimates could then be used either to gain knowledge for the process designer or, in combination with optimization methods, to automate the design of the process. The second scenario is in-line quality prediction during the manufacturing of a product based on process and sensor data. The predictions could then be used to initiate a quality-improving action in the manufacturing process to avoid faults or meet manufacturing tolerances. Third, predictive quality is being investigated for visual quality inspection using ML/DL methods. The methods are used to detect rare product defects in image data or to classify certain defect types. They therefore offer great potential to automate manual and costly visual inspections. Manufacturing processes Looking at the study results in terms of the manufacturing processes and quality scenarios addressed, there are many similarities (e.g. prediction of the same quality criteria, similar research of approaches for defect detection), but also large imbalances between the manufacturing process groups. While cutting and joining processes account for half of the publications, there are process groups which are hardly dealt with (e.g. coating) or not at all (i.e. changing of material properties). Furthermore, there are also large differences within the process groups. While, for example, many different domains are covered in cutting (e.g. turning, drilling, milling, laser cutting), joining processes are largely covered only by welding processes. Other important branches such as riveting, gluing or solderingare hardly to befound. One reason for this imbalance may be the different degrees of digitization in the domains. The availability of process and quality data in the manufacturing process is an essential requirement for predictive quality. It is noticeable that many of the reviewed publications lie in manufacturing domains in which solutions for process and tool condition monitoring already exist, such as machining processes (Mohanraj et al., 2020; Serin et al., 2020b) or additive manufacturing processes (Lin et al., 2022; Montazeri & Rao, 2018). Accordingly, in these domains it is easier to collect data from the process. Process integration As mentioned before, the learning models used in the publications show very promising results for their use in real manufacturing scenarios. In most cases, however, the approaches are not integrated into the manufacturingprocess.Thoughsomepublicationsuserealproduction data for quality inspection (Li et al., 2018; Oh et al., 2019; Ståhl et al., 2019; Wagner et al., 2020; Schmitt et al., 2020a; Yun et al., 2020) or quality prediction (Goldman et al., 2021; Essien & Giannetti, 2020; Lee et al., 2018; Kebisek et al., 2020; Meyes et al., 2019), the training and evaluation as well as the use of the models mostly happen offline or away from the actual process. There are a few works that implement and deploy predictive quality approaches as part of a larger framework (Kebisek et al., 2020; Lee et al., 2018;Lietal., 2020a; Martinez et al., 2020; Oh et al., 2019; Schmitt et al., 2020a). In addition, some publications discuss aspects such as inline capability and real-time capability of the model estimations (Doltsinis et al., 2020;Lietal.,2018; Martinez et al., 2020; Moreira et al., 2019; Sarivan et al., 2020; Schmitt et al., 2020a; Zhang et al., 2019a). However, no discussions are given on the implementation of predictive quality in real quality assurance processes. Furthermore, there is a lack of evaluation of the approaches in terms of their impact on processquality by using quality-oriented metrics(e.g., reduction of rejects, yield rate). 123 1896 Journal of Intelligent Manufacturing (2022) 33:1879–1905 Data bases and characteristics Input variables The publications show that predictive quality models can be trained on very different data sources and types. The majority of publications use data taken from the physical manufacturing process. This can be process parameters, which are set for the manufacturing of the product, as well as measurements and sensor data, which are gatheredduring themanufacturingprocess.Manyoftheproposed approachesarebased on the useofafew input variablesofthe same type. However, using parameters in combination with sensor data can significantly improve performance, which wasshownbyElangovanetal.(2015).Inaddition,wenoticed that other important variables that influence product quality in manufacturing, such as product design or material properties, are not considered in the publications at all. In most cases, ML models are studied for only one product type. The common approach is to train a model on data (parameters or sensors) for a single product with specific characteristics and material compositions. The question of how a model trained for a single product can be used for other products remains open. Common data processing steps The pre-processing of raw data is an important step before training machine learning models. In particular when using measurement data and sensor data, many publications perform data cleansing (e.g., filling missing data, removing noise and outliers) and data scaling (e.g. normalization or standardization) methods prior to model training. In addition, we noticed two other processing steps that occur frequently. One is the transformation of temporal sensor data into scalar features using feature extraction methods (Doltsinis et al., 2020; Du et al., 2021; Gaikwad et al., 2020; Garcia et al., 2019;Lietal.,2019,2020b; Lieber et al., 2013; Neto et al., 2013). This involves extracting statistical features from the data (e.g., minimum, maximum, mean values) or transforming the data using signal processing methods. On the other hand, when using image data, data augmentation methods are used to significantly enrich the data set (Dimitriou et al., 2020; Ferguson et al., 2018;Hsu &Liu,2021;Lietal.,2018; Jun et al., 2021; Liu et al., 2021; Martinez et al., 2020; Yun et al., 2020; Zhu et al., 2020). These methods generate additional image variants by adding noise, rotating the images or randomly cropping them. Data amount The availability of representative data in a sufficiently large quantity is a fundamental requirement for ML and DL and consequently also for predictive quality. This is a major challenge in a domain like manufacturing where generating data can be cumbersome and costly. The study results show that many approaches are developed and evaluated on a small amount of experimentally generated data, where the experiments often involve the variation of a few process parameters. While experiments offer the advantage over running productions that they can include boundary conditions and edge cases, they provide in general a less representative data basis. Since many publications also use data sets that contain fewer than 100 data points, their results purelyservetodemonstrate thepotentialandthefeasibilityof predictive quality. Therefore, solutions have to be researched and developed to improve the data representation and to increase the data quantity. Data augmentation is a promising approach for this. Though it used in some of the reviewed publications (as mentioned above), the focus here lies only on image data. Benchmark data As mentioned, the generation and use of manufacturing process data is usually expensive and requires time and effort. The use of this data to investigate predictive quality is therefore an investment that cannot always be made. It is therefore all the more important to have freely available benchmark data sets that can be used for investigations. The literature review shows that the vast majorityofthepublicationsdonotusefreelyavailablebenchmark datasets, except from (Ferguson et al., 2018; Jun et al., 2021; Liu et al., 2021,2020b; Yu et al., 2019), nor do they provide their own data base or source code. Thus, on the one hand, there is a lack of comparability between different approaches for similar predictive quality tasks. On the other hand, reproducibility and further development of existing research results is hardly possible. Machine learning methods Prime models 68% of publications from 2020 and 2021 use an MLP or CNN as their prime model. This clearly shows the popularity and potential of these models for predictive quality. They are well suited to identify complex patterns and relationships in process data. MLPs in particular are used in a wide variety of use cases and data sets. Many publications show that MLPs are superior to other machine learning models such as SVMs or random forests in terms of their prediction performance. CNNs are by far the most widely used deep learning models for predictive quality. As they are very well suited for pattern recognition in image data, they are often used in visual quality inspection. Regarding other deep learning models, only LSTMs are currently studied in a few publications. It is noteworthy that other popular models such as transformer networks are not found in the reviewed publications. Baseline models Conducting experimental comparisons of different models or model variants is an important part in machine learning. About 50% of publications compare their prime model with other baseline models. These are mainly other machine learning models that are established for nonlinear learning problems, such as SVM, random forest, decision tree, and k-nearest neighbor. Although these models do not achieve the same performances as MLPs in 123 Journal of Intelligent Manufacturing (2022) 33:1879–1905 1897 most experiments, they also show strong versatility for different predictive quality use cases. Models and data modalities The selection of an appropriate ML or DL model for a particular predictive quality use case depends on many factors. One of them is the data modality. Looking at the publications with respect to which model is used for which data modality, some common approaches become apparent. If the data basis consists of images, most publications (9 out of 11) use a CNN-based approach (Dimitriou et al., 2020; Ferguson et al., 2018; Jun et al., 2021; Li et al., 2018; Liu et al., 2021; Martinez et al., 2020;Yun et al., 2020; Wang et al., 2021; Zhang et al., 2019a). For numerical/continuous data, an MLP is used in about half of the corresponding publications, followed by random forest, SVM, and other models. For time series data, no common model is established yet. As mentioned above, the common approach for raw time series data coming from sensors is to transform them first into numerical/continuous variables using feature extraction and training then a machine learning model on it. However, of the few publications that train models on the time series directly, five publications use LSTMs (Essien & Giannetti, 2020; Meyes et al., 2019; Ståhl et al., 2019; Zhang et al., 2018,2019b) and three publications use CNNs (Goldman et al., 2021;Hsu&Liu,2021; Sarivan et al., 2020). Future research directions Based on the obtained results and conclusions, we provide an outlook on future directions of predictive quality research. We believe that these research directions can address the identified gaps as well as boost the prevalence of predictive quality in research and industry. Synthetic data generation Machine learning and especially deep learning models typically require large amounts of training data. Therefore, solutions have to be researched and developed to increase the data quantity and to overcome the identified data sparsity in predictive quality scenarios. Onepromisingapproachistogenerate synthetic training data usinggenerative deeplearningmodels. Ithasbeen shownthat they are suitable for generating realistic data in large quantitiesatlowcost(Maoetal.,2019;Nikolenko,2021; Pashevich et al., 2019). In the predictive quality context, they could be used with manufacturing simulations to synthetically replicate data for rare process variations and product defects. In addition, we propose the establishment and further development of data augmentation methods for manufacturing process data, in particular sensor and time series data. We thus refer to research on data augmentation for time series problems (Iwana & Uchida, 2021; Wen et al., 2021). Benchmark data sets We see great potential in research to make results and data sets more accessible to other scientists. Therefore, we recommend establishing benchmark data for predictive quality to be used for the evaluation of new approaches. This could be data for the classification of product defects or for the numerical prediction of quality values based on sensor data. In addition, we recommend scientists to use already existing data sets for their own research work. At this point, we refer to publications that provide an overview of datasets and repositories, such as for surface defect detection (Chen et al., 2021) or for machine learning in production (Kraußet al., 2019). Novel deep learning methods The review results show thatamongtheexistingdeeplearningmodels,onlyCNNsand LSTM-based models are investigated for predictive quality. However, in the state of the art of deep learning, new types of methods have already been established. Among them is the Transformer network, an attention-based method that performs very well on sequential data (Vaswani et al., 2017) and image data (Khan et al., 2021). Though applications of Transformer networks for predictive quality are not yet known to us, there are already applications in the predictive maintenance context (Liu et al., 2020a; Mo et al., 2021). Also strongly researched in the deep learning field are graph neural networks (Zhou et al., 2020), which are suitable for graph data and can therefore be useful for pattern recognition in CAD or simulation data. We therefore see the potential for these methods to be equally well suited for predictive quality scenarios. Time series classification and forecasting While deep learning on image data has gained acceptance in predictive quality (via CNNs), there is still a large research potential for training models on raw sensor data or time series data. In current deep learning research, there are already a number of different model approaches for performing time series classification (Ismail Fawaz et al., 2019) or forecasting (Lim & Zohren, 2021). Considering predictive quality scenarios, these approaches could be suitable to perform quality prediction based on temporal sensor data in the manufacturing process. Transfer learning and continual learning The current works in predictive quality are mainly based on the assumption that the training data basis is representative for the given problem. However, this assumption is often not valid in industrial production, since manufacturing processes are subject to continuous changes (e.g. the production of new products). Process changes mean that previously trained learning models no longer work sufficiently well, which is whyalotofnewtrainingdatahastobegeneratedatgreatcost. The emerging fields of transfer learning and continual learningcan overcome thischallengebytrainingdata-efficientand cost-effective models over process variants. In the current state of research, some efforts for the use of transfer learning (Maschler & Weyrich, 2021b; Maschler et al., 2021a; Tercan et al., 2018,2019) and continual learning (Tercan et 123 1898 Journal of Intelligent Manufacturing (2022) 33:1879–1905 al., 2021) in manufacturing exist. We see great potential in further research of these fields for predictive quality. Integration and deployment With regard to an establishment of predictive quality in industrial manufacturing systems, further research work has to be done. On the one hand, we see the need to evaluate predictive quality solutions in real quality assurance processes. This includes the development of strategies for automated feedback of model decisions to humans and systems as well as the evaluation of approaches in terms of their impact on process quality by using quality-oriented metrics (e.g. yield rate). Another important aspect is the operationalization and automation of modeltrainingand model use. In the current stateofresearch, firstapproachesofMLOPs(machinelearningoperationalization) exist to continuously monitor, integrate and deliver ML models in business environments (Cardoso Silva et al., 2020; Garg et al., 2021). We see great potential of MLOPs strategies for continuous integration of predictive quality models. Furthermore, there is a need for certification of predictive quality processes that guarantees the reliability of the ML models and thus enables their use in industrial manufacturing processes. To the best of our knowledge, there are currently no established approaches for certifying machine learning methods in industries. Summary This review paper provided a comprehensive overview of 81 scientific publications between 2012 and 2021 that address predictive quality in manufacturing processes. The publications were categorized and evaluated based on three guiding questions. The first question was to discover which manufacturing processes and quality criteria are addressed in the publications. The categorization was done according to the DIN 8580. On the one hand, the results show that predictive quality is used in a wide variety of manufacturing processes, estimating various quality metrics or defect types. On the other hand, an imbalance in the process groups is evident. While a lot of research is done in process groups such as cutting and joining, hardly any publications lie in the fields of coating and changing material properties. The second question dealt with the data used in the publications for training and evaluating the learning models. Here it was shown that for a large part of the publications, process parameters or sensor data were gathered from a real manufacturing process and merged with the quality values. The generation of the data was often carried out experimentally by varying a few parameters or boundary conditions. With regard to data modality, it appeared that numerical quantities as well as image data (e.g. from product measurements) are playing an increasingly important role. The third question addressed the machine learning and deep learning models used for predictive quality. The results of the review showed that especially models based on artificial neural networks (MLPs) and deep learning (mainly CNNs) were very much in focus. While the MLP was used in versatileways,CNNs were commonly usedon image data. In abouthalfofthepublications,themodelswerealsocompared experimentally with other models. Here, it was observed that popular machine learning methods such as SVMs and random forests were frequently used for comparison. Based on the obtained results, central challenges for predictive quality research were derived, which need to be addressed in future work. On the one hand, these include the tackling of sparse data sets in the manufacturing context and the generation of benchmark data for more comparability in research. On the other hand, there is a lack of approaches to integrateand deploy sustainable and robustpredictivequality solutions in real production processes. In conclusion, predictive quality is a very heterogeneous and highly researched field in the manufacturing world. The relevance and popularity of the field will likely continue to increase in the coming years. The current state of research highlights the great potential that data-driven methods of machine learning and deep learning bring to quality assurance and inspection. Yet the use cases, approaches, and results are still viewed in a very isolated way. As such, there is still much to be done in research to overcome this isolated view and enable greater prevalence of predictive quality in the future. Funding Open Access funding enabled and organized by Projekt DEAL. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitteduse,youwillneedtoobtainpermissiondirectlyfromthecopyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/. Appendix A: Literature See Table 10 123 Journal of Intelligent Manufacturing (2022) 33:1879–1905 1899 Table 10 List of all selected publications for the review Publication Type Name Alvarado-Iniesta et al. (2012) Journal Journal of Applied Research and Technology Ge et al. (2012) Journal Industrial Engineering & Chemistry Research Nurwaha and Wang (2012) Journal Fibres & Textiles in Eastern Europe Hamidinejad et al. (2012) Journal Materials and Design Lieber et al. (2013) Conference CIRP Conference on Manufacturing Systems Neto et al. (2013) Conference CIRP Conference on Intelligent Computation in Manufacturing Engineering Abd-Ellatif (2013) Conference Alexandria Engineering Journal Dhas and Kumanan (2014) Journal Applied Soft Computing Hossain and Ahmad (2014) Journal International Conference on Mechanical Engineering Ciancio et al. (2015) Conference CIRP Conference on Intelligent Computation in Manufacturing Engineering Acayaba and de Escalona (2015) Journal CIRP Journal of Manufacturing Science and Technology Elangovan et al. (2015) Conference International Symposium on Big Data and Cloud Computing de Oliveira Leite et al. (2015) Journal Applied Soft Computing Vrabel et al. (2016) Conference CIRP Conference on Manufacturing Systems Martín et al. (2016) Journal Materials Science & Engineering: A Tercan et al. (2016) Conference Changeable, Agile, Reconfigurable & Virtual Production Conference Yu et al. (2016) Journal Journal of Mechanical Science and Technology Ai et al. (2016) Journal Optics and Lasers in Engineering Kayabasi et al. (2017) Journal Solar Energy Varma et al. (2017) Journal Material Today: Proceedings Tušar et al. (2017) Journal Applied Soft Computing Tercan et al. (2017) Journal Production Engineering Zhang and Lei (2017) Conference Global Congress on Manufacturing and Management Bustillo et al. (2018) Journal Journal of Manufacturing Systems Ferguson et al. (2018) Journal Smart and Sustainable Manufacturing Systems Li et al. (2018) Conference IFAC Workshop on Mining, Mineral and Metal Processing Zhang et al. (2018) Conference International Conference on Through-life Engineering Services Kim et al. (2018) Journal International Journal of Computer Integrated Manufacturing Lee et al. (2018) Journal Sensors Mulrennan et al. (2018) Journal Polymer Testing Thomas et al. (2018) Journal Computers in Industry Zhang et al. (2019a) Journal Additive Manufacturing Gyasi et al. (2019) Conference International Conference on Flexible Automation and Intelligent Manufacturing Garcia et al. (2019) Journal Journal of Intelligent Manufacturing Zhang et al. (2019b) Journal Computers in Industry Li et al. (2019) Journal Robotics and Computer-Integrated Manufacturing Natesh et al. (2019) Journal Measurement Moreira et al. (2019) Journal Computers & Industrial Engineering Papananias et al. (2019) Conference CIRP Conference on Modelling of Machining Operations 123 1900 Journal of Intelligent Manufacturing (2022) 33:1879–1905 Table 10 continued Publication Type Name Ståhletal.(2019) Journal Applied Mathematical Modelling Oh et al. (2019) Journal Reliability Engineering and System Safety Meyes et al. (2019) Conference North American Manufacturing Research Conference Yu et al. (2019) Journal Wear Lei et al. (2019) Journal Journal of Manufacturing Processes Gejji et al. (2020) Conference International Conference Interdisciplinarity in Engineering Gaikwad et al. (2020) Journal Additive Manufacturing Lutz et al. (2020) Conference CIRP Conference on Manufacturing Systems Zimmerling et al. (2020) Conference International Conference on Material Forming Dib et al. (2020) Journal Neural Computing and Applications Dimitriou et al. (2020) Journal IEEE Transactions on Industrial Electronics Doltsinis et al. (2020) Journal IEEE Transactions on Automation Science and Engineering Zahrani et al. (2020) Conference CIRP Conference on Photonic Technologies Essien and Giannetti (2020) Journal IEEE Transactions on Industrial Informatics Serin et al. (2020a) Conference International Conference on Flexible Automation and Intelligent Manufacturing Sarivan et al. (2020) Conference International Conference on Flexible Automation and Intelligent Manufacturing Bartlett et al. (2020) Journal Materials Science & Engineering: A Jiao et al. (2020) Journal Applied Sciences Yun et al. (2020) Journal Journal of Manufacturing Systems Ke and Huang (2020) Journal Polymers Li et al. (2020a) Journal Scanning Li et al. (2020b) Journal Materials and Design Liu et al. (2020b) Journal Journal of Intelligent Manufacturing Li and Anand (2020) Journal Journal of Manufacturing Processes Kebisek et al. (2020) Journal IFAC-PapersOnLine Martinez et al. (2020) Journal International Journal of Advanced Manufacturing Technology McDonnell et al. (2021) Journal Journal of Intelligent Manufacturing Wagner et al. (2020) Conference CIRP Conference on Computer Aided Tolerancing Schmitt et al. (2020a) Journal Advanced Engineering Informatics Schorr et al. (2020a) Conference CIRP Conference on Manufacturing Systems Schorr et al. (2020b) Conference North American Manufacturing Research Conference Klein et al. (2020) Conference CIRP Conference on Manufacturing Systems Nguyen et al. (2020) Journal Optics and Lasers in Engineering Yu et al. (2020) Journal Metals Zhu et al. 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