Press Casting Quality Detection and Analysis Based on Machine Learning

Lin, Chih‐Hsueh; Hu, Guo-Hsin; Ho, Chia-Wei; Hu, Chia-Yen; Kuo, Po-Chun

doi:10.1109/ispacs51563.2021.9651064

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Ref. [ 108 ] provides a solution based on supervised learning to inspect press-casting products using CNN, Random Forest, PCA, and XGBoost. Similarly, ref.…”

Section: Quality Controlmentioning

confidence: 99%

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Frankó

Hollósi

Ficzere

et al. 2022

Sensors

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Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders at many levels much faster, with much greater granularity than ever before. When it comes to smart production, the aim of analyzing the collected data is usually to achieve greater efficiency in general, which includes increasing production but decreasing waste and using less energy. Furthermore, the boost in communication provided by IIoT requires special attention to increased levels of safety and security. The growth in machine learning (ML) capabilities in the last few years has affected smart production in many ways. The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality assurance and sustainability aspects. The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain—namely security and safety, asset localization, quality control, maintenance—has a dedicated chapter, with a concluding table on the typical ML techniques and the related references. The paper summarizes lessons learned, and identifies research gaps and directions for future work.

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“…Ref. [ 108 ] provides a solution based on supervised learning to inspect press-casting products using CNN, Random Forest, PCA, and XGBoost. Similarly, ref.…”

Section: Quality Controlmentioning

confidence: 99%

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Frankó

Hollósi

Ficzere

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…At the same time, the visual inspection system based on machine vision technology does not need to directly contact the sample, avoiding damage to the sample during the detection process, and solving the many drawbacks of the traditional manual visual inspection. Therefore, the new energy battery process manufacturing intelligent visual inspection technology has a broad application prospect and great market value [3] .…”

Section: Introductionmentioning

confidence: 99%

Research on precision visual inspection technology based on new energy battery manufacturing

Zhou,

Huang,

2023

First Advanced Imaging and Information Processing Conference (AIIP 2023)

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Press Casting Quality Detection and Analysis Based on Machine Learning

Cited by 2 publications

References 9 publications

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Research on precision visual inspection technology based on new energy battery manufacturing

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