基于工艺参数的随机森林机器学习TIG焊接焊缝轮廓预测模型

IF 1.9 3区工程技术 Q3 ENGINEERING, MANUFACTURING

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture Pub Date : 2023-11-13 DOI:10.1177/09544054231210018

Abhinav Arun Munghate, Shivraman Thapliyal

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引用次数: 0

摘要

活性钨惰性气体焊接工艺的焊头轮廓取决于工艺参数和焊剂成分。使用传统的基于统计的模型，这些输入参数与球头形状几何的相关性是复杂的。因此，采用基于机器学习的技术来预测奥氏体不锈钢A-TIG焊接过程中的焊头形状几何形状，即熔深(D)、宽度(w)和D/w比。采用随机森林回归和分类模型对A-TIG焊接过程中焊头几何形状进行预测。结果表明，基于分类的模型可以较好地预测矿珠剖面。此外，还建立了工艺参数和助焊剂组成与焊头轮廓的相关性。

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Process parameters based machine learning model for bead profile prediction in activated TIG Welding using random forest machine learning

The bead profile in the activated tungsten inert gas welding process depends on process parameters and flux composition. Using a conventional statistical-based model, the correlation of these input parameters with the bead shape geometry is complex. Therefore, machine learning-based techniques were implemented to predict the bead shape geometry, that is, penetration (D), width (w), and D/w ratio in the A-TIG welding process of austenitic stainless steel. Random forest regression and classification models were implemented to predict bead shape geometry in the A-TIG welding process. Based on the results, classification-based modeling was appropriate for predicting the bead profile. In addition, the correlation of the process parameters and flux composition with the bead profile was established.

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来源期刊

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 工程技术-工程：机械

CiteScore

5.10

自引率

30.80%

发文量

167

审稿时长

5.1 months

期刊介绍： Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed. Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing. Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.