Intelligent sampling decision scheme based on the AVM system

Cheng, Fan‐Tien; Chen, Chun Fang; Hsieh, Yao Sheng; Huang, Hsuan Heng; Wu, Chu Chieh

doi:10.1080/00207543.2014.955924

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Hybrid SDS strategies perform the best because they have the advantages of both time-and event-based strategies. A fixed-time-based frequency accounts for hidden drifts, while an event-based frequency takes care of the other types of drift (Cheng et al 2015). Some approaches propose adapting the time-based frequency in function of the event occurrence (Cheng et al 2016).…”

Section: Sampling Decision Systemmentioning

confidence: 99%

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Dreyfus

Psarommatis

May

et al. 2021

International Journal of Production Research

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Virtual metrology (VM) involves estimating a product's quality directly from production process data without physically measuring it. This enables the product quality of each unit of production to be monitored in real time, while preserving the process efficiency. Initially developed for the semiconductor industry, VM has recently been examined for use in other industrial fields. VM is enabled by components such as quality estimators and drift detectors. It enhances various industrial applications such as machine control and sampling decision systems. The literature lacks a comprehensive summary and a systematic review of the state of the art in VM. To fill this gap, this study followed a systematic methodology to conduct a complete and structured literature review of VM. This paper presents a detailed analysis of the 199 papers that we identified based on our search and selection criteria. Following our methodological framework, we assigned these papers to categories and highlighted shortcomings. Finally, we discussed VM's use in various industrial fields, underlining its potential for every manufacturing industry. This study and the proposed framework are useful for both practitioners and academicians since they raise crucial theoretical and managerial questions as well as provide suggestions for further research on this critical topic.

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Section: Sampling Decision Systemmentioning

confidence: 99%

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Dreyfus

Psarommatis

May

et al. 2021

International Journal of Production Research

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“…Studies show that this method can effectively reduce the detection time and achieve the total inspection of the product quality. e virtual metrology has been widely applied in diverse high-tech industries, including the semiconductor industry [1], TFT-LCD industry [2], and the solar-cell industry [3]. Moreover, the virtual metrology technology has been applied for processing the CNC machine tool and reasonable results have been achieved accordingly [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Performance Prediction Model for the Machining Process Based on the Interval Type-2 Fuzzy Neural Network

Tian

Zhao

Sun

et al. 2020

Mathematical Problems in Engineering

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The prediction model is the most important part of the virtual metrology system. Predicting the performance of the machining process has been widely applied in manufacturing, which can reduce costs and improve efficiency compared with the manual operation. In this paper, a novel performance prediction model for the machining process is proposed based on the interval type-2 fuzzy neural network. The interval type-2 fuzzy logic system with a complete rule base, type-reduction, and defuzzified output is simplified by the BMM method to meet the requirements of the prediction. The proposed prediction model is trained using a gradient-based optimization algorithm. To evaluate the performance of the proposed approach, it is applied to wire electrical discharge turning process for predicting material removal rate and surface roughness with a published dataset. The results show that the proposed method is an effective scheme in the studied cases.

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