Design and topology optimization of 3D-printed wax patterns for rapid investment casting

Jiayi Wang, Santosh Reddy Sama, Paul C. Lynch, Guha Manogharan

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

Traditional investment casting (IC), also known as lost-wax casting, suffers from high tooling cost and long lead-time during fabrication of wax patterns. In recent years, rapid investment casting (RIC) based on AM has become widely accepted in casting foundries due to rapid production of patterns without any tooling requirements. Direct AM production of wax patterns is not only cost-efficient for low volume production but also capable of creating freeform and highly complex geometries that are otherwise extremely difficult or too expensive to cast conventionally. Such advantages provide unlimited opportunities in the cast part design and enable the use of advanced design tools especially advanced optimization tools like topology optimization (TO). In this study, knowledge-based design guidelines for rapid investment casting are developed through streamlined integration of TO with design for investment casting and design for AM principles. Specifically, TO routine based on Solid Isotropic Material with Penalization (SIMP) method using Abaqus Topology Optimization Module (ATOM) has been developed for both material extrusion and investment casting constraints. Part design optimization is demonstrated for this new technique and is validated by a case study using heat-treated ASTM A216 WCB cast carbon steel parts. AM patterns for investment casting are produced using commercially available wax filaments. Wax material extrusion parameters are identified for optimal AM accuracy and surface finish.

Original languageEnglish (US)
Pages (from-to)683-694
Number of pages12
JournalProcedia Manufacturing
Volume34
DOIs
StatePublished - Jan 1 2019
Event47th SME North American Manufacturing Research Conference, NAMRC 2019 - Erie, United States
Duration: Jun 10 2019Jun 14 2019

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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