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Automated Process Optimization in Manufacturing Systems Based on Static and Dynamic Prediction Models

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Predictive Maintenance in Dynamic Systems

Abstract

A key aspect in predictive maintenance is the early recognition of product downtrends and a proper reaction on it, to reduce production waste and to avoid machine failures, components destruction, and risks for operators. We propose an approach for the automated optimization of process parameters in manufacturing systems in order to automatically compensate possible downtrends in product quality at an early stage. This should significantly reduce or even avoid manual (reaction) efforts for operators which are often time-intensive and laborious. Such downtrends are recognized by prediction models for product quality, which are extracted from process data and which come in two different variants: (1) static predictive mappings established based on process (machining) parameter settings through a combination of a new hybrid variant of design of experiment (DoE), cross-correlation analysis, and data-driven mapping construction; and (2) dynamic forecast models which respect the time-series trends of process values measured during on-line production, being able to properly recognize undesired changes and dynamics happening (unexpectedly) during the process. These models will have the property to be able to self-adapt and evolve over time based on new recordings; they employ generalized (flexible) evolving fuzzy systems (GEFS) combined with a new incremental update of the latent variable space obtained through partial least squares (PLS). Both types of prediction models can then be used as surrogate mappings within a multi-objective, evolutionary optimization process for important target quality criteria, which relies on a fast co-evolution strategy. Several results from a micro-fluidic chip production process will be included to demonstrate the applicability and performance of the proposed methods and to discuss open challenges.

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Acknowledgement

The authors acknowledge the Austrian research funding association (FFG) within the scope of the “IKT of the future” programme, project “Generating process feedback from heterogeneous data sources in quality control” (contract # 849962). The first author also acknowledges the support by the LCM–K2 Center within the framework of the Austrian COMET-K2 program.

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Authors and Affiliations

  1. Fuzzy Logic Laboratorium Linz-Hagenberg, Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer

  2. Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Alexandru-Ciprian Zavoianu

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Mahardhika Pratama

  4. Stratec Consumables, Anif, Austria

    Thomas Radauer

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  1. Edwin Lughofer
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  2. Alexandru-Ciprian Zavoianu
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  3. Mahardhika Pratama
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  4. Thomas Radauer
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Corresponding author

Correspondence to Edwin Lughofer .

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Editors and Affiliations

  1. Fuzzy Logic Laboratorium Linz-Hagenberg, Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer

  2. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Lughofer, E., Zavoianu, AC., Pratama, M., Radauer, T. (2019). Automated Process Optimization in Manufacturing Systems Based on Static and Dynamic Prediction Models. In: Lughofer, E., Sayed-Mouchaweh, M. (eds) Predictive Maintenance in Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-05645-2_17

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  • DOI: https://doi.org/10.1007/978-3-030-05645-2_17

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