Mingjun Ren
21 Papers
1 Citations
Mingjun Ren is an academic researcher. The author has contributed to research in topics: Computer science & Chemistry. The author has co-authored 1 publications.
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Papers
Development of an on-machine measurement system for ultra-precision machine tools using a chromatic confocal sensor
TL;DR: In this paper , an on-machine measurement device based on a chromatic confocal sensor is designed, which can inspect workpiece surfaces with larger slopes and depths, and a nonlinear least squares method is then used to further reduce the adverse influence of the alignment error between the axes of the sensor and the spindle of the machine tool.
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Overlapping Bubble Detection and Tracking Method based on Convolutional Neural Network and Kalman Filter
TL;DR: In this article , a new detection and tracking technique for overlapping bubbles was proposed to identify the overlapped bubbles, which achieved 85 % accuracy under high overlap rate conditions in a 10 mm narrow rectangular channel with around 0.1 s for an image.
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Adaptive detection of tool-workpiece contact for nanoscale tool setting based on multi-scale decomposition of force signal
Zhichao You,Yixuan Meng,Duo Li,Zhe Zhang,Mingjun Ren +4 more
TL;DR: A novel method is proposed for adaptive tool setting in nanoscale machining, utilizing multi-scale decomposition of force signals with discrete wavelet transform and density-based clustering to detect tool-workpiece contact with high accuracy and precision.
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Neural Process Enhanced Machining Error Evaluation for Coordinate Measuring Machines
TL;DR: Wang et al. as discussed by the authors proposed an efficient machining error evaluation approach for complex surfaces based on the neural process, which improves the efficiency of measurement by sparse sampling and reconstruction, and further improves the sampling efficiency by introducing adaptive sampling.
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Normalized Variational Auto-Encoder With the Adaptive Activation Function for Tool Setting in Ultraprecision Turning
Zhichao You,Yixuan Meng,Zhe Zhang,Mingjun Ren +3 more
TL;DR: A novel normalized variational auto-encoder model with an adaptive activation function (NVAE-AAF) is proposed in this article, which significantly improves tool setting accuracy by 75%–85%, reaching a level of 75 nm.
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