Machine learning tools in production engineering

Abstract

Machine learning methods have shown potential for the optimization of production processes. Due to the complex relationships often inherent in those processes, the success of such methods is often uncertain and unreliable. Therefore, understanding the (algorithmic) behavior and results of machine learning methods is crucial to improve the prediction of production processes. Here, mathematical tools may help. This paper shows how efficient algorithms for the training of neural networks and their retraining in the framework of transfer learning are expressed in a discrete as well as a time-continuous formulation. The latter can be analyzed and investigated using mathematical techniques from kinetic gas dynamics. The results obtained provide a first step towards explainable artificial intelligence. Based on the mathematical description, an adapted ensemble method for retraining of neural networks is proposed and compared with backpropagation algorithms. The process of training and retraining is a common task and therefore demonstrated for two very different production processes. The first one involves the prediction of specific cutting forces and the second one the prediction of particle properties in a plasma spraying coating process. For both use cases, the presented algorithms are applied and their performance is evaluated giving thereby an indication how mathematically inspired methods improve classical tasks in production processes.

A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

“…Figures (12)(13)(14)(15) show the onsite control performances (a) and saturation curves (b) for extreme cases of different target settings of peening intensity and peening coverage of 98%. In the control performance plot, the yellow line is the input voltage.…”

“…As a result, the determination of operational parameters can be time-consuming, cost expensive and material waste. From another perspective, machine learning (ML) has been widely applied to develop the control system design in advanced manufacturing processes [10][11][12][13]. Therefore, a deep and tight combination of the model predictive control (MPC) and ML can properly offer a promising solution for a smart and fully automated process in the advanced manufacturing operation.…”

Prediction of the Reaming Torque Using Artificial Neural Network and Random Forest Algorithm: Comparative Performance Analysis

“…2023, 59, 97 2 of 8 and insights once buried within the depths of complex manufacturing systems. These algorithms act as a virtual brain, processing and interpreting this data to drive intelligent decision-making, enabling manufacturers to optimize processes, streamline operations, and enhance overall productivity [1,2].…”