Using fault detection and classification techniques for machine breakdown reduction of the HGA process caused by the slider loss defect

IF 1.9 Q3 ENGINEERING, MANUFACTURING

Manufacturing Review Pub Date : 2022-01-01 DOI:10.1051/mfreview/2022020

T. Wanglomklang, Phathan Chommaungpuck, K. Chamniprasart, J. Srisertpol

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引用次数: 1

Abstract

Fault Detection and Classification (FDC) based on Machine Learning (ML) approach was used to detect and classify mount head fault in the slider attachment process which causes the machine alarm 71 to occur which leads to 2% of machine downtime. This paper has focused on the use of classified pixel surface of mount head with fault difference conditions including Healthy, Fault I, Fault II, and Fault III to detect and diagnose mount head before a vacuum leak. The Artificial Neural Network (ANN) algorithm was a proposed classification model and has to be evaluated before using in the real processes. Three features of mount head surface pixel, i.e., inner, outer, and overall areas were investigated and used as model training data set. The experiment result indicates that the classification using the ANN model with three features performed with an accuracy of 94.3%. According to the result, it was found that the reliability of the production processes of FDC technique has increased as a result of the reduction of machine downtime by 1.886%.

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采用故障检测和分类技术减少了HGA工艺中滑块损耗缺陷引起的机器故障

基于机器学习(ML)方法的故障检测和分类(FDC)用于检测和分类滑块附着过程中的安装头故障，该故障导致机器报警71发生，导致机器停机时间为2%。本文主要研究了利用健康、故障一、故障二、故障三种故障差分条件下的挂载头分类像素面，在真空泄漏前对挂载头进行检测和诊断。人工神经网络(ANN)算法是一种被提出的分类模型，在应用于实际过程之前必须进行评估。研究了mount head表面像素的内、外、整体三个特征，并将其作为模型训练数据集。实验结果表明，采用具有三个特征的人工神经网络模型进行分类，准确率达到94.3%。根据结果，发现FDC技术的生产过程的可靠性提高了，因为机器停机时间减少了1.886%。

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来源期刊

Manufacturing Review ENGINEERING, MANUFACTURING-

CiteScore

5.40

自引率

12.00%

发文量

审稿时长

8 weeks

期刊介绍： The aim of the journal is to stimulate and record an international forum for disseminating knowledge on the advances, developments and applications of manufacturing engineering, technology and applied sciences with a focus on critical reviews of developments in manufacturing and emerging trends in this field. The journal intends to establish a specific focus on reviews of developments of key core topics and on the emerging technologies concerning manufacturing engineering, technology and applied sciences, the aim of which is to provide readers with rapid and easy access to definitive and authoritative knowledge and research-backed opinions on future developments. The scope includes, but is not limited to critical reviews and outstanding original research papers on the advances, developments and applications of: Materials for advanced manufacturing (Metals, Polymers, Glass, Ceramics, Composites, Nano-materials, etc.) and recycling, Material processing methods and technology (Machining, Forming/Shaping, Casting, Powder Metallurgy, Laser technology, Joining, etc.), Additive/rapid manufacturing methods and technology, Tooling and surface-engineering technology (fabrication, coating, heat treatment, etc.), Micro-manufacturing methods and technology, Nano-manufacturing methods and technology, Advanced metrology, instrumentation, quality assurance, testing and inspection, Mechatronics for manufacturing automation, Manufacturing machinery and manufacturing systems, Process chain integration and manufacturing platforms, Sustainable manufacturing and Life-cycle analysis, Industry case studies involving applications of the state-of-the-art manufacturing methods, technology and systems. Content will include invited reviews, original research articles, and invited special topic contributions.