Application of Pattern Search and Genetic Algorithms to Optimize HDPE Pipe Joint Profiles and Strength in the Butt Fusion Welding Process

Author:

Mathkoor Mahdi Saleh1,Jassim Raad Jamal1,Al-Sabur Raheem1ORCID

Affiliation:

1. Mechanical Department, Engineering College, University of Basrah, Basrah 61004, Iraq

Abstract

The rapid spread of the use of high-density polyethylene (HDPE) pipes is due to the wide variety of methods for connecting them. This study keeps pace with the developments of butt fusion welding of HDPE pipes by exploring the relationship between the performance of the weld joints by studying ultimate tensile strength and exploring the joint welding profiles by studying the shape of the joint at the outer surface of the pipe (height and width of the joint cap) and the shape of the joint at the internal surface (height and width of the joint root). Welding pressure, heater temperature, stocking time, and cooling time were the parameters for the welding process. Regression was analyzed using ANOVA, and an ANN was used to analyze the experimental results and predict the outputs. Two optimization techniques (pattern search and genetic algorithm) were applied to obtain the ideal operating conditions and compare their performance. The results showed that pattern search and genetic algorithms can determine the optimal output results and corresponding welding parameters. In comparison between the two methods, pattern search has a limited relative advantage. The optimal values for the obtained outputs revolved around a tensile strength of 35 MPa (3.45 and 4.5 mm for the cap and root heights, and 8 and 6.98 mm for the cap and root widths, respectively). When comparing the effects of welding parameters on the results, welding pressure had the best effect on tensile strength, and plate surface temperature had the most significant effect on the welding profile geometries.

Publisher

MDPI AG

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3