Application of machine learning and data mining in manufacturing industry

Abstract

With the rise of machine learning in various industries, the traditional manufacturing industry is facing a new disruption, which requires the use of different technologies and tools to achieve its production targets; In this regard, machine learning (ML) and data mining (DM) play a key role. This paper provides a statistical understanding of the main methods and algorithms used to improve manufacturing processes over the past 20 years by dividing them into four main themes: Scheduling, Monitoring, Quality and Failure, presents previous ML research and the latest advances in manufacturing, followed by a comprehensive discussion of existing problem solutions in manufacturing from multiple aspects, It includes tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machines, neural networks), learning types (i.e., ensemble learning, deep learning), and performance indicators (i.e., accuracy, mean absolute error). In addition, the main steps of database knowledge discovery (KDD) process that should be followed in manufacturing applications are described in detail, and the methods to overcome some problems and the advantages of machine learning applied to manufacturing industry are briefly described. Finally, the paper summarizes and further looks forward to the future development direction.