Contribution Ratio Assessment of Process Parameters on Robotic Milling Performance

Area of Science:

• Manufacturing Engineering
• Robotics
• Materials Science

Background:

• Robotic milling offers significant potential but faces challenges due to parameter sensitivity and resulting machining defects.
• The serial structure of robotic systems amplifies the impact of process parameters on milling performance, affecting surface quality.

Purpose of the Study:

• To quantitatively assess the influence of key process parameters on robotic milling performance.
• To identify critical parameters affecting milling load, surface quality, and vibration in a specific robotic milling posture.
• To provide insights for optimizing robotic milling parameters and enhancing application development.

Main Methods:

• Conducted robotic flat-end milling experiments on 7075-T651 aluminum alloy under dry conditions.
• Investigated the effects of milling depth, spindle speed, and feed rate on milling performance metrics.
• Employed Analysis of Variance (ANOVA) to determine the contribution ratio of each parameter to milling outcomes.

Main Results:

• Milling depth was the most significant factor for milling load (69.25% contribution) and vibration (up to 75.97% in the Z-direction).
• Spindle speed significantly influenced surface roughness, accounting for 48.02% of the variation.
• Feed rate's impact, while not detailed with percentages, was also assessed in relation to milling performance.

Conclusions:

• Milling depth and spindle speed are critical parameters that must be carefully controlled for effective robotic milling.
• Statistical analysis, particularly ANOVA, provides a robust method for understanding parameter influence in robotic machining.
• Optimizing these parameters can significantly improve robotic milling efficiency, surface quality, and expand its manufacturing applications.