Inspection and Compensation of Spindle Thermal Extension Based on Machine Vision

Zhang, Longiia; Zhang, Xi; Liu, Xiangwei; Guo, Zixie

doi:10.1109/icma49215.2020.9233704

Cited by 2 publications

(1 citation statement)

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“…The word H02 is refer to the tool number 2 in the machine tool magazine, at the sane time it is the active tool in the NC code of the active part program. More details about the tool length expansion was discussed at reference [29]. The authors used this vision system to detect and compensate the thermal expansion of main spindle with different rotational speeds.…”

Section: Principle Of Measuring Axial Tool Wear and Compensatingmentioning

confidence: 99%

Tool Wear Inspection and Classiﬁcation by Artiﬁcial Neural Network Based on Vision System and Cutting Force Signal.

Abdeltawab

Zhang

et al. 2021

Preprint

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The current work focuses on the cutting tool condition monitoring of end milling based on direct and indirect approach in machining AISI H13 alloy steel. Indirect process parameters such as cutting force signals are measured as responses using force sensor. In order to successfully inspect the milling tool life online for direct approaches, an automated machine vision system was used for tool condition monitoring. The image processing algorithms are developed to extract different features of rotating milling tool. A detection and compensation system for tool wear based on machine vision is designed. Feedforward Back-Propagation Neural Network applied for tool wear classification developed based on many force features. Ten time-domain features extracted and the sensitive features is determined based on Pearson’s correlation coefficient. I-kaz method which integrates between kurtosis and standard deviation is added as input feature with the ten time-domain features. A strong correlation is established between most of time-domain features and tool wear with high correlation coefficient. ANN model applied for classification tool states as normal and abnormal. Experiments with vision system have shown that area of wear at bottom and ﬂank is suitable to inspect in-process. Actual measurements of the tool wear stages are possible to identify the abnormality in cutting using vision system. ANN model showed superior results for tool states classification. The mean squared Error (MSE) for classification model was less than 6.7E-09 and R equal to 1. The model can be used to construct fault estimation mode for tool wear online classification and inspection.

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Section: Principle Of Measuring Axial Tool Wear and Compensatingmentioning

confidence: 99%