Scholarlink

<p>In recent times, the quest for an advanced composite material (polymer reinforcement in metal matrix) has become a challenge for promising industrial and household applications. Therefore, the present study focuses on the indigenous production of a novel microparticle-based Aegle marmelos natural polymer reinforced (AMNPR) aluminum composites using the powder metallurgy (P/M) technique. The results revealed that the reinforcement (AMNP) concentration had a considerable effect on the physico-mechanical, thermal, and chemical characteristics of composites. Further, in this study, TOPSIS coupled with the CRITIC method (CRITIC-TOPSIS) is adopted to convert the multiple characteristics into a closeness coefficient (Ci) response. The optimal parameters are found to be reinforcement - 20 wt. %, ball milling duration - 180 min, and speed - 300 rpm. Moreover, the Ci values predicted by the artificial neural network (ANN) model are in good agreement with the experimental values having a mean absolute error of 4.116 %.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Experimental and computational studies on characteristics of indigenously produced novel Aegle marmelos micro polymer reinforced aluminum composites using powder metallurgy

  • 期刊论文

  • 发表日期：

    2025年1月31日

查看全文
  • V. Veeranaath,
  • Ranjeet Kumar Sahu,
  • Infanta Mary Priya

摘要

In recent times, the quest for an advanced composite material (polymer reinforcement in metal matrix) has become a challenge for promising industrial and household applications. Therefore, the present study focuses on the indigenous production of a novel microparticle-based Aegle marmelos natural polymer reinforced (AMNPR) aluminum composites using the powder metallurgy (P/M) technique. The results revealed that the reinforcement (AMNP) concentration had a considerable effect on the physico-mechanical, thermal, and chemical characteristics of composites. Further, in this study, TOPSIS coupled with the CRITIC method (CRITIC-TOPSIS) is adopted to convert the multiple characteristics into a closeness coefficient (Ci) response. The optimal parameters are found to be reinforcement - 20 wt. %, ball milling duration - 180 min, and speed - 300 rpm. Moreover, the Ci values predicted by the artificial neural network (ANN) model are in good agreement with the experimental values having a mean absolute error of 4.116 %.