.png){kind=icon}
返回
Classification-based process parameter recommendation in sheet metal forming
.png){kind=icon}
OA
AI
DOI：10.1016/j.jii.2023.100458.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For the production of sheet metal parts for car bodies, continuous adjustment of process parameters is required to maintain the desired part quality in the presence of scattering blank properties. The digital transformation enables data-driven methods for finding process parameters instead of a time-consuming experience-driven trial-and-error approach. However, it is still hard to measure quality for every part due to cost and technical limitations. Removing data points of low-quality parts helps in recommending proper process parameters. In this paper, we propose a classification-based solution enhanced by a particular data preprocessing for recommending cushion cylinder forces. The solution utilizes anomaly detection and knowledge-based methods to remove potential low-quality data points without quality measures in data prepossessing. On the processed data, a classification model is trained to predict process parameters according to blank properties. Our solution detects 32.46% low-quality parts and gives competitive performance (94.30% prediction accuracy) compared to a model trained on data comprising quality measures.
Keyword:
Classification
Anomaly detection
Process parameter recommendation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.6
论文数:
919
被引数:
4.4K
机构
查看更多机构
引用论文
Tumorigenic poxviruses: Characterization of the expression of an epidermal growth factor related gene in shope fibroma virus.png)
Virology
VirologyIF0
PREAI
分享收藏Tumorigenic poxviruses: Genomic organization and dna sequence of the telomeric region of the shope fibroma virus genomeVirology
VirologyIF0
PREAI
分享收藏分享收藏分享收藏 没有更多内容