Product quality is a crucial issue for manufacturing companies, so it is essential to take note of any emerging product defects. In contrast to the use of traditional methods, the "modern" constantly evolving data mining methods are now being more frequently used. The main objective of this paper is to detect the potential cause or the area of the production process where the majority of product defects arise. The dataset from the semiconductor manufacturing process has been used for this purpose. First, it was necessary to address dataset quality. Significant multicollinearity was found in the data and to detect and delete the collinear variables, correlations and variance inflation factors have been used. The MICE-CART method has been used for the imputation because the original dataset contained more than 5% of random missing values. In further analysis, the K-means clustering method has been used to separate the failed products from the flawless ones. Following this, the hierarchical clustering method has been used for the failed product to create groups of product defects with similar properties. For the optimal number of clusters, the determination of the BIC method has been used. Five clusters of products have been made although only three can be classed as important for further analysis. These groups of products should be directly subjected to the analysis in the production process, which can assist in identifying the source of scarcity.

Paper aims: This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations.Originality: Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing.Research method: In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings:The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice:This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose.

No abstract

This research aims to propose an effective model for the detection of defective Printed Circuit Boards (PCBs) in the output stage of the Surface-Mount Technology (SMT) line. The emphasis is placed on increasing the classification accuracy, reducing the algorithm training time, and a further improvement of the final product quality. This approach combines a feature extraction technique, the Principal Component Analysis (PCA), and a classification algorithm, the Support Vector Machine (SVM), with previously applied Automated Optical Inspection (AOI). Different types of SVM algorithms (linear, kernels and weighted) were tuned to get the best accuracy of the resulting algorithm for separating good-quality and defective products. A novel automated defect detection approach for the PCB manufacturing process is proposed. The data from the real PCB manufacturing process were used for this experimental study. The resulting PCALWSVM model achieved 100 % accuracy in the PCB defect detection task. This article proposes a potentially unique model for accurate defect detection in the PCB industry. A combination of PCA and LWSVM methods with AOI technology is an original and effective solution. The proposed model can be used in various manufacturing companies as a postprocessing step for an SMT line with AOI, either for accurate defect detection or for preventing false calls.

No abstract