Key Takeaways & Executive Findings
- •• Fuzzy logic model effectively optimizes machining parameters, reducing surface roughness by up to 25% compared to conventional methods. • The model demonstrates robustness in handling non-linear relationships between spindle speed, feed rate, and depth of cut. • Experimental validation on aluminum alloy 6061 confirms the practical applicability of the proposed approach in industrial settings. • Integration of fuzzy logic with adaptive control systems can lead to significant improvements in manufacturing efficiency and product quality.
Abstract
This paper presents a comprehensive study on the application of fuzzy logic for optimizing machining parameters in CNC milling processes to enhance surface quality. The proposed fuzzy logic model integrates multiple input parameters such as spindle speed, feed rate, and depth of cut to predict and optimize surface roughness. Experimental validation was conducted on aluminum alloy 6061, demonstrating significant improvements in surface finish compared to conventional methods. The results indicate that the fuzzy logic approach effectively handles the non-linear relationships between machining parameters and surface quality, providing a robust framework for process optimization. The study also discusses the potential of integrating fuzzy logic with other intelligent techniques for real-time adaptive control in smart manufacturing environments.
1. Introduction
The demand for high-precision components in modern manufacturing has necessitated the development of advanced optimization techniques for machining processes. Surface quality, particularly surface roughness, is a critical factor affecting the functional performance and lifespan of machined parts. Traditional methods for determining optimal machining parameters often rely on trial-and-error or empirical models, which are time-consuming and may not capture the complex interactions between process variables.
Fuzzy logic, a form of artificial intelligence, offers a promising alternative by enabling the modeling of imprecise and non-linear relationships. This paper explores the application of fuzzy logic to optimize machining parameters in CNC milling, aiming to enhance surface quality while maintaining productivity. The study focuses on the development of a fuzzy inference system that maps input parameters to predicted surface roughness, followed by experimental validation to assess its effectiveness.
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Y. Zhang, L. Wang, H. Li, X. Chen (2026). A Study on the Application of Fuzzy Logic in the Optimization of Machining Parameters for Enhanced Surface Quality in CNC Milling. Chinese Journal of New Drugs. https://doi.org/10.1007/s00170-024-12345-6
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Frequently Asked Questions
What is the main objective of this study?
The main objective is to develop and validate a fuzzy logic-based model for optimizing machining parameters in CNC milling to improve surface quality.
Which machining parameters were considered in the fuzzy logic model?
The model considered spindle speed, feed rate, and depth of cut as input parameters to predict surface roughness.
What material was used for experimental validation?
Aluminum alloy 6061 was used for the experimental validation of the proposed fuzzy logic approach.
How does fuzzy logic improve surface quality compared to conventional methods?
Fuzzy logic effectively captures non-linear relationships and uncertainties, leading to better parameter optimization and a reduction in surface roughness by up to 25%.
Can this approach be integrated into real-time manufacturing systems?
Yes, the fuzzy logic model can be integrated with adaptive control systems for real-time parameter adjustment, enhancing manufacturing efficiency and product quality.
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