Defect Detection in Manufacturing via Machine Learning Algorithms

Abstract

In today's highly competitive global market, defects in manufacturing cause additional costs and these costs decrease the profit margin. So, defect-free products are crucial for firms. There are various studies conducted with different methodologies in different fields related to defect detection in the literature. Although one of these methodologies is machine learning (ML) algorithms, the literature review shows very few studies using these algorithms to detect physical product defects. This study aims to utilize machine learning algorithms for predicting defects in the manufacturing. Different ML algorithms are used to predict defected products. Then the performances of the algorithms are compared to find the most appropriate algorithm. The study is conducted on one of the leading manufacturing companies in Turkey, and the data is provided from the real-life problem of the company. With the proposed algorithm, it is desirable to predict defected products that may occur during the powder coating phase.

Key Terms in this Chapter

GBM: Gradient boosting machine.

ML: Machine learning.

SVM: Support vector machines.

RF: Random forest.

K-NN: K-nearest neighbour.