WeChat Mini Program
Old Version Features

Algorithm for Detecting Load-Carrying Regions Within the Tip Seat of an Indexable Cutting Tool

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING(2024)

AB Sandvik Coromant

Cited 0|Views3
Abstract
Maintaining an even pressure distribution in an indexable cutting tool interface is crucial to the life expectancy of a carbide insert. Avoiding uneven pressure distribution is highly relevant for intermittent cutting operations because two load cases arise for full immersion, inside and outside the cutting zone, which can cause alternating contact positioning. Current positioning methodologies, such as 3-2-1 principles, do not consider external mechanical forces, which must be considered for insert-tool body positioning designs. Therefore, this paper proposes an algorithm to calculate a contact index to aid in the design of locating schemes for the early design phases of insert-tool body interface design. The results indicate that it is possible to visualize where a contact condition needs to exist to give support based on the mechanical loads acting on the insert.
More
Translated text
Key words
computer-aided design,computer-aided engineering,data-driven engineering,model-based systems engineering
求助PDF
上传PDF
Bibtex
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