ROM-based stochastic optimization for a continuous manufacturing process

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

Abstract

This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variability and from the raw material itself. The proposed method, which is based on Bayesian optimization, takes these factors into account and obtains a robust set of process parameters. Due to the high computational cost and complexity of performing detailed simulations, a reduced order model is used to address the optimization. The proposal has been evaluated in a virtual environment, where it has been verified that it is able to minimize the impact of process uncertainties. In particular, it would significantly improve the quality of the product without incurring additional costs, achieving a 50% tighter dimensional tolerance compared to a solution obtained by a deterministic optimization algorithm.
基于 ROM 的连续生产流程随机优化。
本文提出了一种基于模型的优化方法,用于在挤压工艺中生产汽车密封件。生产量大,加上质量限制和工艺固有的不确定性,促使人们寻求将不合格情况降至最低的操作条件。主要的不确定性来自于工艺的可变性和原材料本身。所提出的方法以贝叶斯优化法为基础,将这些因素考虑在内,并获得一套稳健的工艺参数。由于进行详细模拟的计算成本高、复杂性大,因此采用了一个简化模型来进行优化。在虚拟环境中对该建议进行了评估,证实它能够最大限度地减少工艺不确定性的影响。特别是,与确定性优化算法相比,它能实现 50%的尺寸公差,从而在不增加额外成本的情况下显著提高产品质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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