Scholarlink

<p>Glass Fibre Reinforced Polymer (GFRP) composites are commonly used in many applications, such as in the aerospace, construction, maritime and automotive sectors, owing to their lightweight, corrosion resistant, strong rigidity and lightweight to strength ratios. This form of material normally struggles on machining with surface delamination, fibre peeling up, fibre pulling away, fibre cracking, fibre de-bonding and fibre breaking. There is a need to optimize the drilling parameters specifically in reducing the type of damage known as delamination which is prevalent during drilling activity on Chopped Strand Mat GFRP material. Regression equations for the experimental results collected from drilling Chopped Strand Mat GFRP material are established for this purpose. Considering the established regression equations as objective functions, under parallel search methodology, Simulated Annealing Algorithm employs innovatively to refine the drilling parameters for reduced delamination. Simulated Annealing Algorithm (SAA) is programmed using C++ and the decrement factor, known as the cooling factor applied during the search process, is varied for the parallel search of optimization. Through this optimization strategy, the tailored drilling parameters for drilling Chopped Strand Mat GFRP material with minimal delamination are substantially enhanced, and the performance of SAA are verified by the Adaptive Neuro Fuzzy Inference System (ANFIS).</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

Advancement in optimization using Simulated Annealing Algorithm and ANFIS to minimize delamination during drilling Chopped Strand Mat GFRP

  • 期刊论文

  • 发表日期：

    2026年3月17日

查看全文
  • Raghuraman Srinivasan,
  • Panneerselvam Thangavel,
  • Manoj Vasu,
  • Monica Panneerselvam

摘要

Glass Fibre Reinforced Polymer (GFRP) composites are commonly used in many applications, such as in the aerospace, construction, maritime and automotive sectors, owing to their lightweight, corrosion resistant, strong rigidity and lightweight to strength ratios. This form of material normally struggles on machining with surface delamination, fibre peeling up, fibre pulling away, fibre cracking, fibre de-bonding and fibre breaking. There is a need to optimize the drilling parameters specifically in reducing the type of damage known as delamination which is prevalent during drilling activity on Chopped Strand Mat GFRP material. Regression equations for the experimental results collected from drilling Chopped Strand Mat GFRP material are established for this purpose. Considering the established regression equations as objective functions, under parallel search methodology, Simulated Annealing Algorithm employs innovatively to refine the drilling parameters for reduced delamination. Simulated Annealing Algorithm (SAA) is programmed using C++ and the decrement factor, known as the cooling factor applied during the search process, is varied for the parallel search of optimization. Through this optimization strategy, the tailored drilling parameters for drilling Chopped Strand Mat GFRP material with minimal delamination are substantially enhanced, and the performance of SAA are verified by the Adaptive Neuro Fuzzy Inference System (ANFIS).