Assembly Sequences Based on Multiple Criteria Against Products with Deformable Parts

TL;DR

This paper presents a multiobjective genetic algorithm for generating assembly sequences considering insertion conditions and constraints, extended to handle deformable parts in 3D CAD models, demonstrated through simulation results.

Contribution

It introduces an extended method for extracting part relations in 3D CAD models with deformable parts, enabling more realistic assembly sequence planning.

Findings

Successfully generated Pareto-optimal assembly sequences for models with deformable parts.

Extended relation matrices facilitate analysis of interference and constraints in deformable components.

Simulation results demonstrate the method's potential for complex assembly planning.

Abstract

Aiming to generate easy-to-handle assembly sequences for robotic assembly, this study tackles assembly sequence generation by considering two tradeoff objectives: (1) insertion conditions and (2) degrees of constraints among assembled parts. We propose a multiobjective genetic algorithm to balance these two objectives for generating assembly sequences. Furthermore, the method of extracting part relation matrices including interference-free, insertion, and degree of constraint matrices is extended for application to 3D computer-aided design (CAD) models, including deformable parts. The interference of deformable parts with other parts can be easily investigated by scaling parts. A simulation experiment was conducted using the proposed method, and the results show the possibility of obtaining Pareto-optimal solutions of assembly sequences for a 3D CAD model with 33 parts including a deformable part. This approach can potentially be extended to handle various types of deformable parts and to explore graspable sequences during assembly operations.