Simultaneous dimension and tolerance design for robot manipulator considering cost and positioning accuracy...

Area of Science:

• Robotics Engineering
• Manufacturing Process Optimization
• Tolerance Analysis

Background:

• Traditional tolerance allocation methods neglect manufacturability constraints, leading to design-manufacturing iterations and increased costs.
• The dependency between part size and achievable tolerance grades is often overlooked in conventional approaches.
• Costly iterations and increased manufacturing expenses arise from the disconnect between design and manufacturing stages.

Purpose of the Study:

• To propose an integrated method for tolerance allocation and dimensional synthesis in robot design.
• To optimize both robot positioning accuracy reliability and manufacturing cost simultaneously.
• To address the limitations of existing methods by integrating design and manufacturing considerations.

Main Methods:

• Simultaneous optimization of joint dimensions and tolerances using a cost function.
• Formulation of a cost function incorporating dimensional parameters, accuracy reliability, tolerance grades, and manufacturing cost.
• Application of a matrix-based Monte Carlo simulation (MCS) for accelerated reliability evaluation.
• Utilization of the NSGA-II multi-objective optimization algorithm to identify Pareto optimal solutions.

Main Results:

• The proposed integrated method reduced manufacturing costs by 22% compared to traditional methods.
• Improved positioning accuracy reliability was achieved with the new approach.
• The matrix-based MCS method demonstrated a 400-fold speed improvement over point-based MCS.

Conclusions:

• The integrated tolerance allocation and dimensional synthesis method effectively optimizes robot design for both accuracy and cost.
• The developed approach mitigates costly iterations between design and manufacturing.
• The matrix-based MCS significantly enhances computational efficiency for reliability analysis in robotic systems.