Monitoring Shearing-Plowing Transitions in Micro-Milling Using Fluctuations in Cutting Forces
Journal of Micro and Nano-Manufacturing(2021)
Indian Inst Technol
Abstract
In micromilling, understanding transitions between the desired shearing-dominant to the undesired plowing-dominant cutting mechanism could help obtain high quality microfeatures. This work investigates the transitions in cutting mechanisms in micromilling using fluctuations in cutting force signals, characterized by using a fluctuation parameter. A new analytical model correlating fluctuation in force signals to the transitions in cutting mechanism has been developed. Two types of slot milling experiments were performed to understand the transitions in cutting mechanisms, as a function of processing parameters, and over the entire life of micro-endmills. The proposed model was found to agree with experimental values of forces within 15% error. The limiting value of the fluctuation parameter has been estimated as 0.01, which corresponds to a limiting feed of 1 mu m/tooth. Feed per tooth and cutting edge radius are the important parameters that affect transitions in cutting mechanisms. The cutting mechanism changes from shearing to plowing and vice-versa over the entire life of the tool. Shearing-dominant mechanism prevailed in the first region due to the sharper cutting edges with radius less than 9 mu m. Though plowing-dominant cutting mechanism prevails in the remaining two regions, the mechanism comes closer to shearing-dominant near the end of tool life. This is primarily because of the generation of localized sharpness on tool cutting edges due to chipping. Furthermore, it was evident that cutting mechanism changes from shearing to plowing due to wear, when surface roughness increases above 400 nm Ra.
MoreTranslated text
Key words
micro-milling,cutting mechanism,shearing,plowing,force fluctuation,chipping
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