A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Abstract

Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

“…The sixth cluster deals with improvements of operational processes in logistics that can often be deduced from nature, as the examples in the framework description have shown (see also [50][51][52][53]). The topic of biological transformation is an already emerging theme promoted, e.g., by the Fraunhofer research institutes [69] and other researchers [70], and describes the change from solely bioinspired manufacturing or logistics systems towards those that are bio-integrated and bio-intelligent by combining what we can deduce and learn from the observation of processes in nature and leaping computational power as well as progress in AI, ML, and DL.…”

“…Ademujimi et al review the current literature on ML techniques in manufacturing by focusing on techniques, e.g., Bayesian networks, the artificial neural network, the support vector machine, and the hidden Markov model for the optimization of manufacturing fault diagnosis [52]. Teschemacher and Reinhart develop an ant colony optimization algorithm to enable dynamic milk runs in logistics to reduce the number of necessary vehicles in long-term optimization approaches [53].…”

“…Cluster 6, "improvement of operational processes in logistics", outlines various possibilities for the enhancement of operational processes in logistics by applying AI, ML, and DL technologies. Thereby, swarm robotics can be used to optimize smart warehouses, sound location systems can increase the efficiency of identification and tracking approaches, AI-based algorithms ensure holistic manufacturing fault diagnosis, and ant colony optimization approaches enable the optimization of milk runs in logistics problems [50][51][52][53].…”

Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes

“…Basically, the input dataset is mapped into a high dimension feature space and a linear model (hyperplane) is constructed in that space. SVM uses kernel functions, such as radial basis function (RBF), sigmoid, polynomial and linear kernel functions, to determine a hyperplane/line that best separates the dataset into classes [36]. The optimal hyperplane is that which maximizes the margin, i.e., the distance between the hyperplane and the closest data points (support vectors).…”

Evaluating Impact of AI on Cognitive Load of Technicians During Diagnosis Tasks in Maintenance

“…In the field of the manufacturing numerous AI related papers can be found. In the manufacturing, AI is often used to detect product quality problems [8]. For example, Nguyen et al and Yang et al used an AI to detect defective wafers in the semiconductor industry [9,10].…”