An Improved Iterative Predictive Model for Grinding Residual Stress Considering Material Microstructure Evolution
Journal of Manufacturing Science and Engineering(2024)
No 333 LONGTENG ROAD
Abstract
Abstract During micro-grinding, multiple abrasive grains on grinding wheel circulate on the workpiece causing alternating mechanical and thermal loads which result in microstructure evolution. The microstructure evolution affects the flow stress of the material, which in turn affects force and temperature. This paper thoroughly investigates the cyclic iterative mechanism and proposes an analytical model to predict micro-grinding induced residual stress. In this investigation, the flow stress model is developed considering temperature, strain, strain rate, yield stress, and material microstructure evolution, based on which, the micro-grinding force and temperature are calculated. On the basis, the evolution of grain size and phases transformation induced by force and temperature are calculated, in turn affected grinding force by flow stress. Then, the analytical model of residual stress is proposed incorporating the stresses induced by mechanical and thermal loadings as well as microstructure evolution. Moreover, the elastic or plastic deformation is determined according to Von Mises criterion with the developed plastic modulus model in stress relaxation process. Finally, the residual stress is measured to validate the improved iterative model. By comparing the traditional models, the results indicated that the developed cyclic iterative model obtain a higher accurate prediction of residua stress.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined