MULTI-OBJECTIVE OPTIMIZATION OF CUTTING PARAMETERS FOR REDUCED TOOL WEAR AND IMPROVED DIMENSIONAL AC

Abstract

The optimization of cutting parameters in CNC milling is critical for enhancing machining performance, particularly when working with materials like EN24 steel, known for its high strength and hardness. This study focuses on the multi-objective optimization of cutting parameters to achieve reduced tool wear and improved dimensional accuracy during the CNC milling of EN24 steel. Using a combination of Design of Experiments (DOE), Response Surface Methodology (RSM), and Taguchi’s method, the optimal cutting parameters, including spindle speed, feed rate, and depth of cut, are identified. The study incorporates both experimental trials and simulation-based analyses to evaluate the influence of these -parnmctcrs-on- -toolwear- and- dimensional-accuracy. .The -Pareto-optimal^solutions... determined to balance the conflicting objectives, providing a set of cutting conditions that minimize tool wear while maintaining stringent dimensional tolerances. The findings reveal that a higher spindle speed coupled with an optimized feed rate significantly reduces tool wear, while a moderate depth of cut enhances dimensional accuracy. The optimized parameters are validated through experimental tests, confirming the robustness of the proposed methodology. The study's results offer practical guidelines for CNC milling operations, aiming to extend tool life and improve product quality in the machining of high-strength materials like EN24 steel. This research contributes to the development of sustainable manufacturing practices by reducing tool replacement frequency and enhancing machining efficiency.

Specification

THE FIELD OF INVENTION
The field of invention relates to the optimization of cutting parameters in CNC milling, specifically for EN24 steel, aimed at minimizing tool wear and enhancing dimensional accuracy. The invention focuses on multi-objective optimization techniques to achieve improved machining efficiency and product quality in industrial applications.
BACKGROUND OF THE INVENTION
In CNC milling of EN24 steel, optimizing cutting parameters is crucial for enhancing tool performance and achieving high precision. EN24 steel, known for its high strength and toughness, poses significant challenges in machining due to its abrasive nature and thermal conductivity.
Traditional single-objective optimization methods often focus on either minimizing tool wear or improving dimensional accuracy, neglecting the trade-offs between these objectives. This invention addresses these limitations by integrating multi-objective optimization techniques to simultaneously reduce tool wear and enhance dimensional accuracy. By leveraging advanced algorithms and real ­time data analysis, this approach aims to identify the optimal cutting conditions that balance these competing factors, leading to improved tool longevity and precision. This innovative method offers a comprehensive solution to the complex problem of parameter optimization in the milling of high- performance materials like EN24 steel.
SUMMARY OF THE INVENTION
This invention optimizes CNC milling cutting parameters for EN24 steel to minimize tool wear and enhance dimensional accuracy. By integrating multi-objective optimization techniques, the process balances performance and precision, extending tool life while achieving superior surface finish and dimensional control in machining operations.



Clearly define the multi-objectives for optimization, focusing on minimizing tool wear and maximizing dimensional accuracy during CNC milling of
EN24 steel.
• Specify the cutting parameters to be optimized, such as cutting speed, feed rate, and depth of cut. Ensure that these parameters are within the operational range for EN24 steel.
• Identify and apply suitable multi-objective optimization techniques, such as ■ "gcnclic- algorithm particlc optimization,- or ..response.. surface methodology, to balance and optimize the defined objectives. • Define the metrics for evaluating tool wear and dimensional accuracy, such as tool life measurements, surface roughness, and dimensional deviations, to assess the effectiveness of the optimized parameters. • Outline the experimental setup for CNC milling of EN24 steel, including machine specifications, tool material and geometry, and measurement techniques, to ensure reproducibility and accuracy in results.


This study explores the multi-objective optimization of cutting parameters in CNC milling of EN24 steel to achieve a balance between reduced tool wear and enhanced dimensional accuracy. EN24 steel, known for its high strength and toughness, presents challenges in milling due to significant tool wear and dimensional deviations. The research employs advanced optimization techniques to simultaneously minimize tool wear and maximize precision in the machining process. By systematically varying cutting parameters such as speed, feed rate, and depth of cut, the study identifies optimal conditions that reduce tool degradation and improve the quality of the machined components. Utilizing multi-objective optimization algorithms, the research aims to provide practical guidelines for manufacturers to enhance productivity, extend tool life, and achieve superior dimensionaL Gor milling operations. The findings offer valuable insights for optimizing machining processes in industrial applications involving high-strength materials.

We Claim 1. Claim: Optimized cutting parameters significantly reduce tool wear in CNC milling of EN24 steel, leading to extended tool life and lower operational costs. 2. Claim: -objective optimization ensures better dimensional accuracy in machined components by minimizing deviations and maintaining tight tolerances. 3. Claim: Optimized parameters enhance the efficiency of material removal processes, improving . Claim: The study achieves lower surface roughness values, resulting in higher quality finished surfaces with fewer post-processing requirements. 5. Claim: By balancing tool wear and dimensional accuracy, the optimization strategy contributes to cost savings through reduced tool replacement frequency and improved machining efficiency.

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