Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric

Alexander Gerling (), Holger Ziekow (), Andreas Hess (), Ulf Schreier (), Christian Seiffer () and Djaffar Ould Abdeslam ()
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Alexander Gerling: Furtwangen University of Applied Science
Holger Ziekow: Furtwangen University of Applied Science
Andreas Hess: Furtwangen University of Applied Science
Ulf Schreier: Furtwangen University of Applied Science
Christian Seiffer: Furtwangen University of Applied Science
Djaffar Ould Abdeslam: Université de Haute-Alsace

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 2, No 10, 555-573

Abstract: Abstract In order to manufacture products at low cost, machine learning (ML) is increasingly used in production, especially in high wage countries. Therefore, we introduce our PREFERML AutoML system, which is adapted to the production environment. The system is designed to predict production errors and to help identifying the root cause. It is particularly important to produce results for further investigations that can also be used by quality engineers. Quality engineers are not data science experts and are usually overwhelmed with the settings of an algorithm. Because of this, our system takes over this task and delivers a fully optimized ML model as a result. In this paper, we give a brief overview of what results can be achieved with a state-of-the-art classifier. Moreover, we present the results with optimized tree-based algorithms based on RandomSearchCV and HyperOpt hyperparameter tuning. The algorithms are optimized based on multiple metrics, which we will introduce in the following sections. Based on a cost-oriented metric we can show an improvement for companies to predict the outcome of later product tests. Further, we compare the results from the mentioned optimization approaches and evaluate the needed time for them.

Keywords: Hyperparameter optimization; Manufacturing; Metrics; Machine Learning; Production Line (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-021-01890-0

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