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The module configuration optimization of vertical computer numerical control honing machine based on ideal outranking
Chuang Wang1, Guanghui Zhou2,3, Fengtian Chang2,3
1Institute of IoT and IT Based Industrialization, Xi'an University of Posts & Telecommunications, Xi'an, China.
Science Progress
|July 4, 2020
Summary
This study presents an optimization method for configuring modules in high-end computer numerical control (CNC) machine tools. The approach uses an ideal outranking cardinal point to select the best configuration based on performance, cost, and delivery.
Area of Science:
- Manufacturing Engineering
- Operations Research
- Industrial Engineering
Background:
- Selecting optimal module configurations for high-end computer numerical control (CNC) machine tools is complex.
- Balancing performance, cost, and delivery is crucial for effective CNC machine tool configuration.
Purpose of the Study:
- To develop and verify an optimization method for module configuration schemes in CNC machine tools.
- To propose an ideal outranking cardinal point approach for multi-objective decision-making in module configuration.
Main Methods:
- Establishing a multi-objective decision model incorporating performance, cost, and delivery indicators.
- Transforming feasible configuration schemes into a decision attribute data matrix.
- Utilizing the ideal outranking cardinal point and inverse ideal outranking cardinal point for scheme selection.
Main Results:
- The proposed method effectively selects optimal module configuration schemes for CNC machine tools.
- The approach was validated through a case study on a vertical CNC honing machine.
- Demonstrated feasibility and universality of the module configuration optimization method.
Conclusions:
- The ideal outranking cardinal point method provides an effective solution for CNC machine tool module configuration optimization.
- This approach aids in selecting schemes that best meet performance, cost, and delivery requirements.
- The validated method offers a valuable tool for enhancing decision-making in manufacturing.
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