Automated sampling decision scheme for the AVM system

Cheng, Fan‐Tien; Hsieh, Yao Sheng; Chen, Chun Fang; Lyu, Jhao Rong

doi:10.1080/00207543.2015.1072649

Cited by 10 publications

(1 citation statement)

References 9 publications

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“…The authors proposed the use of AVM technology, which has been successfully applied to high-tech industries such as semiconductors, displays, and solar cells. In 2016, Cheng et al [11] noted that whenever the VM accuracy deteriorates, it cannot adaptively increase the default sampling rate in the intelligent sampling decision (ISD) method; as a result, it takes more time to collect enough samples to improve the accuracy of the VM. When the VM accuracy is always maintained in good condition, it cannot automatically reduce the default sampling rate in the ISD, which may cause unnecessary waste.…”

Section: Literature Reviewmentioning

confidence: 99%

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Chang

Zhang

et al. 2021

Applied Sciences

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One of the important values of Industry 4.0 is to integrate people’s needs into the manufacture of enhanced products, systems, and services to achieve greater levels of product customization. This paper presents a prediction method for predicting screw process parameters; taking crystalline and non-crystalline polymers as the molding material, when there is a lack of sufficient historical screw process data to establish a data-driven method, using various screws and polymer materials to predict tool life under different cutting conditions is a challenge. A screw life prediction method is proposed based on the mixed compound screw process parameters method using a dynamic iteration work. To meet the requirements of mass production, this work proposes the combined application of the automatic virtual metrology (AVM) system with the recognizable performance evaluation (RPE) program. The method predicts the injection of compound screws by extracting given cutting conditions and related process parameters characteristics from the senor data by converting sampling inspections with measurement delays from real-time and online routine inspections to automatically and quickly complete method creation production goals.

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Section: Literature Reviewmentioning

confidence: 99%