Multi-Objective Machining Parameter Optimization Based on Strain Signal in Turning Process.

Area of Science:

• Manufacturing Engineering
• Mechanical Engineering
• Materials Science

Background:

• Machining parameter optimization is crucial for enhancing manufacturing quality and efficiency.
• Traditional methods often rely on direct cutting force measurements, which can be complex to implement.
• Tool holder strain signals offer a potential alternative for monitoring and optimizing the machining process.

Purpose of the Study:

• To investigate the feasibility of using tool holder strain signals to characterize the cutting process.
• To optimize machining parameters by correlating strain signals with cutting forces.
• To achieve multi-objective optimization considering strain signal, surface roughness, and material removal rate.

Main Methods:

• Tool holder strain signals were analyzed and compared with cutting force signals.
• Orthogonal cutting experiments were performed to gather data.
• The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for multi-objective optimization.
• Regression models were developed based on experimental results to serve as objective functions.

Main Results:

• Tool holder strain signals demonstrated a strong correlation with cutting force signals.
• The NSGA-II algorithm successfully optimized multiple objectives.
• Optimal machining parameters were identified: cutting speed 140.27 m/min, feed per rotation 0.19 mm, and depth of cut 1.47 mm.

Conclusions:

• Tool holder strain signals can effectively characterize the cutting process and be used for optimization.
• The proposed multi-objective optimization approach yields practical machining parameters.
• The findings provide valuable insights for improving machining efficiency and quality in production environments.