A Dual-Mean Statistical and Multivariate Framework for Machinability Evaluation in CNC Turning: Gradient and...

Area of Science:

• Materials Science
• Manufacturing Engineering
• Mechanical Engineering

Background:

• Machinability assessment is crucial for optimizing CNC turning processes.
• Understanding material response to cutting forces and deformation is key for selecting appropriate engineering alloys.
• Existing methods may not fully capture the complex interplay of factors influencing machinability.

Purpose of the Study:

• To develop and validate a dual-statistical and gradient-based framework for evaluating the machinability of five engineering alloys.
• To quantify and compare the cutting forces, surface deformation, and efficiency of different alloys under CNC turning conditions.
• To establish a composite machinability score for informed material selection in precision manufacturing.

Main Methods:

• Utilized a dual-statistical and gradient-based framework for machinability evaluation.
• Measured cutting forces and surface deformation across five machining zones for Stainless Steel 304, Aluminum 6061, Carbon Steel 1020, Brass C26000, and Bronze C51000.
• Employed finite difference gradients, arithmetic/harmonic means, and principal component analysis (PCA) for data analysis.

Main Results:

• Stainless Steel 304 exhibited the highest cutting force (328 N); Aluminum 6061 showed the highest deformation (0.0164 mm).
• Carbon Steel 1020 demonstrated the highest force-to-deformation efficiency (>97,000 N/mm).
• A composite machinability score ranked Aluminum 6061 highest, followed by Brass C26000 and Bronze C51000.

Conclusions:

• The proposed framework provides interpretable benchmarking for engineering alloy machinability.
• The methodology effectively integrates stiffness variation, efficiency gradients, and multivariate features for material assessment.
• This approach facilitates informed material selection in precision manufacturing environments.