Modular Mechanism Design Optimization in Large-Scale Systems with Manufacturing Cost Considerations

2025-03-17

AI Summary

Large-scale mechanical systems face significant challenges in parametric design—including geometric constraint handling, variable loading conditions, performance deviations, over-specification, and cost-performance trade-offs. To address these, this paper proposes a modular mechanism design optimization framework based on Kriging surrogate modeling. Departing from conventional predefined design schemes, the method uniquely integrates geometry-constrained kinematic parameter optimization with manufacturability-aware cost modeling. It employs NSGA-II for multi-objective optimization to dynamically cluster components, enable customized grouping, and embed cost sensitivity analysis with decision support. The framework significantly improves motion performance consistency and component interchangeability while reducing over-specification rates. In validation on representative industrial systems, it achieves an average 12.7% reduction in manufacturing cost and a 9.4% decrease in carbon footprint.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Mechanism Design
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

Modular design maximizes utility by using standardized components in large-scale systems. From a manufacturing perspective, it supports green technology by reducing material waste and improving reusability. Industrially, it offers economic benefits through economies of scale, making it a practical design strategy. Typically, modularization selects a representative design from predefined candidates to meet all performance requirements. However, achieving effective modularization in mechanical mechanisms presents challenges. First, mechanisms depend on geometric relationships for functional motion, and varying loads lead to different optimal parameters, complicating representative design selection. Second, the chosen design often exceeds optimal parameters, causing over-specification and performance deviations, which worsen as scale increases. To address this, we propose a modular mechanism design framework using surrogate-based optimization. This approach finds optimal designs for large-scale systems and partitions them into groups, each assigned an optimized design. This multi-objective optimization (MOO) problem balances economies of scale and performance consistency. Unlike conventional methods based on predefined candidates and simple grouping, our framework optimizes design variables flexibly for modularization. Additionally, we analyze manufacturing cost parameters to develop a decision support system for selecting optimal strategies in diverse design scenarios. This enhances maintainability, improves interchangeability, and fosters environmentally sustainable manufacturing.

Problem

• Optimizes modular mechanism design for large-scale systems.
• Addresses challenges in geometric relationships and varying loads.
• Balances economies of scale with performance consistency.

Innovation

• Surrogate-based optimization for modular design
• Multi-objective optimization balancing scale and performance
• Decision support system for manufacturing cost analysis