Automated Process Planning for Hybrid Manufacturing

TL;DR

This paper introduces an automated, systematic approach for hybrid manufacturing process planning that optimally combines additive and subtractive methods using symbolic reasoning and Boolean algebra, improving efficiency and versatility.

Contribution

It presents a novel Boolean algebra-based framework for automating hybrid manufacturing process planning, capable of identifying cost-effective, distinct AM/SM combinations and subsuming unimodal planning.

Findings

Framework efficiently plans complex 3D parts with diverse AM/SM tools.

Symbolic reasoning simplifies manufacturability analysis.

Method outperforms traditional approaches in computational efficiency.

Abstract

Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities, leveraging AM's strengths in fabricating complex geometries and SM's precision and quality to produce finished parts. We present a systematic approach to automated computer-aided process planning (CAPP) for HM that can identify non-trivial, qualitatively distinct, and cost-optimal combinations of AM/SM modalities. A multimodal HM process plan is represented by a finite Boolean expression of AM and SM manufacturing primitives, such that the expression evaluates to an 'as-manufactured' artifact. We show that primitives that respect spatial constraints such as accessibility and collision avoidance may be constructed by solving inverse configuration space problems on the 'as-designed' artifact and manufacturing instruments. The primitives generate a finite Boolean algebra (FBA) that enumerates the entire search space for planning. The FBA's canonical intersection terms (i.e., 'atoms') provide the complete domain decomposition to reframe manufacturability analysis and process planning into purely symbolic reasoning, once a subcollection of atoms is found to be interchangeable with the design target. The approach subsumes unimodal (all-AM or all-SM) process planning as special cases. We demonstrate the practical potency of our framework and its computational efficiency when applied to process planning of complex 3D parts with dramatically different AM and SM instruments.