Citation

Ts, An Da and Ng, Kok Why and Chua, Fang Fang (2022) Intelligent Inspection of Manufacturing Workpiece Based on Parallel Processing of Machine Vision. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

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Abstract

With the rise of artificial intelligence, the integration of artificial intelligence and manufacturing has become closer and closer, which has promoted the transition from traditional manufacturing to intelligent manufacturing. It speeds up the in-depth integration of a new generation of information inspection technology and manufacturing, which is conducive to solving the problem of flexibility in intelligent manufacturing of workpiece inspection. Traditional artificial inspection is highly adaptable and can detect features of various types of workpieces. However, its detection speed is slow, relying on artificial experience, and it is difficult to fit the requirements of online fast detection. The speed of machine vision detection is fast, but it is difficult to adapt to the detection of various types of workpieces and different features. If the strong adaptability of artificial inspection is combined with the rapid inspection of machine vision, it is possible to realize the parallel and collaborative processing of classification, positioning, feature extraction and surface defect detection of multiple types of workpieces, thereby realizing intelligent inspection of workpieces. By constructing a deep learning model, designing a new algorithm of detection, and realizing the classification, positioning, feature extraction and surface defect detection of the workpiece. This research will provide technical support for intelligent inspection of workpieces.

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	Artificial intelligence, Machine Learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	19 Dec 2022 04:12
Last Modified:	19 Dec 2022 04:12
URII:	http://shdl.mmu.edu.my/id/eprint/10916

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