过程(计算)
相似性(几何)
序列(生物学)
雅卡索引
变形(气象学)
基本事实
集合(抽象数据类型)
有限元法
计算机科学
章节(排版)
横截面(物理)
算法
结构工程
机械工程
工程类
人工智能
材料科学
模式识别(心理学)
物理
操作系统
图像(数学)
生物
复合材料
程序设计语言
量子力学
遗传学
作者
R. Omar Chavéz-García,Emian Furger,Samuele Kronauer,Christian Brianza,Marco Scarfò,Luca Diviani,Alessandro Giusti
出处
期刊:IEEE robotics and automation letters
日期:2020-08-04
卷期号:5 (4): 6270-6277
标识
DOI:10.1109/lra.2020.3013833
摘要
Hot-rolling is a metal forming process that produces a workpiece with a desired target cross-section from an input workpiece through a sequence of plastic deformations; each deformation is generated by a stand composed of opposing rolls with a specific geometry. In current practice, the rolling sequence (i.e., the sequence of stands and the geometry of their rolls) needed to achieve a given final cross-section is designed by experts based on previous experience, and iteratively refined in a costly trial-and-error process. Finite Element Method simulations are increasingly adopted to make this process more efficient and to test potential rolling sequences, achieving good accuracy at the cost of long simulation times, limiting the practical use of the approach. We propose a supervised learning approach to predict the deformation of a given workpiece by a set of rolls with a given geometry; the model is trained on a large dataset of procedurally-generated FEM simulations, which we publish as supplementary material. The resulting predictor is four orders of magnitude faster than simulations, and yields an average Jaccard Similarity Index of 0.972 (against ground truth from simulations) and 0.925 (against real-world measured deformations); we additionally report preliminary results on using the predictor for automatic planning of rolling sequences.
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