Study on Optimization Method for CNC Machining Plastic-Shaped Appliances Based on ICOA Algorithm

The issue of deformation during the secondary processing of plastic plate is becoming increasingly concerning with the expanding use of plastic-shaped appliances. This is especially evident due to work-hardening and thermal softening, leading to problems such as hot-melt adhesion and distortion. To...

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Bibliographic Details
Published in:International journal of precision engineering and manufacturing Vol. 26; no. 3; pp. 613 - 634
Main Authors: Chen, Guo-hua, Zhou, Bo, Zhao, Xiao, Zhang, Zhi-yang, Yan, Qing, Mao, Jie
Format: Journal Article
Language:English
Published: Seoul Korean Society for Precision Engineering 01.03.2025
Springer Nature B.V
Subjects:
Algorithms
Compressed air
Cooling
Cutting parameters
Cutting tool materials
Cutting tools
Energy consumption
Engineering
Experiments
Feed rate
Genetic algorithms
Industrial and Production Engineering
Machine tools
Machining
Materials Science
Moisture content
Neural networks
Numerical controls
Optimization
Optimization algorithms
Plastic plates
Plate material
Prediction models
Problem solving
Process parameters
Regular Paper
Grey relational analysis
TRIZ (Theory of Inventive Problem Solving)
CNC-GRU
Thermo-fusion adhesion and distortion
ICOA
Innovative optimization strategy
ISSN:2234-7593, 2005-4602
Online Access:Get full text
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Summary:The issue of deformation during the secondary processing of plastic plate is becoming increasingly concerning with the expanding use of plastic-shaped appliances. This is especially evident due to work-hardening and thermal softening, leading to problems such as hot-melt adhesion and distortion. To tackle this challenge, a study employed the Theory of Inventive Problem Solving (TRIZ) to conduct a systematic analysis of the problem. It identified contradictions and conflicts therein and proposed an innovative optimization strategy based on the Improved Coati Optimization Algorithm (ICOA). Initially, five crucial processing parameters—spindle speed (A S ), cutting tool material (A CM ), feed rate (A vf ), plastic plate material (A PM ), and cutting depth (A ap )—are determined through grey relational analysis and utilized as input for the prediction model. Subsequently, a processing state prediction model is constructed on the MATLAB software platform, and the ICOA algorithm is used to optimize the model parameters of the convolutional gated recurrent unit network (CNN-GRU), thereby enhancing its robustness, generalization ability, and prediction accuracy. Experimental validation on the PT-1325 CNC machine tool demonstrated that the CNN-GRU model optimized by ICOA attained a prediction accuracy of 96.67% and effectively enhanced machining efficiency through optimizing the machining parameters.
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ISSN:2234-7593
2005-4602
DOI:10.1007/s12541-024-01139-9