Project Case Study

Topology Optimization & AM for
Suspension Components

Redesigned a double-wishbone suspension A-arm and MacPherson strut steering knuckle for metal additive manufacturing using density-based topology optimization in ANSYS Mechanical. The A-arm achieved 20% mass reduction while improving the static safety factor from 1.25 (steel) to 2.1 (Ti-6Al-4V), with material selection driven by a weighted performance index across specific stiffness, strength, and AM suitability.

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Tools CATIA / SolidWorks / ANSYS Mechanical / SpaceClaim / OrcaSlicer
Result 20-30% mass reduction with validated static structural performance and AM-ready redesigned geometry.P–30% mass reduction with validated static structural performance and AM-ready redesigned geometry.
Topology optimization suspension component render
01

Objective / Problem

What needed to be solved

Objective / Problem

The objective was to reduce the mass of suspension components without compromising structural integrity or preserving critical mounting interfaces. This project focused on a double-wishbone suspension A-arm and a MacPherson strut steering knuckle, where excess mass directly affects unsprung weight, material usage, and manufacturability.

The engineering challenge was to remove non-load-bearing material while keeping the core load paths, stiffness, and interface geometry required for assembly and functional performance. Using topology optimization and static structural validation, the components were redesigned for additive manufacturing with lightweight, manufacturable geometries that remained mechanically credible under the applied loading conditions.

Project snapshot

Quick details

Role CAD modeling, FEA, topology optimization, geometry cleanup, AM-oriented redesign
P25
Industry Automotive / Additive Manufacturing / Structural Design Research
Deliverable Optimized CAD geometry, structural FEA results, material selection matrix, AM-ready redesigned parts, and technical report/presentation
02

Approach and Tools Used

Method

Break the work into clear steps

01

Problem definition

Defined the engineering target as reducing suspension component mass while preserving structural load paths, stiffness, and all critical mounting interfaces for assembly and functional performance. The work focused on a double-wishbone A-arm and a MacPherson strut steering knuckle, where lower mass improves manufacturability and reduces unsprung weight.

02

Engineering work

Imported the baseline geometries, assigned materials, and ran static structural FEA in ANSYS Mechanical to identify deformation, strain, and stress distributions under representative loading. Applied density-based topology optimization to remove non-load-bearing material, then cleaned and reconstructed the optimized geometries for manufacturable additive-ready designs. For the A-arm, the workflow also included material screening using a weighted performance index, leading to Ti-6Al-4V as the preferred AM material. The steering knuckle was additionally prepared for printing in OrcaSlicer.

03

Validation

Validated the redesigned parts by comparing the optimized geometries against the baseline response under static loading. Checked deformation patterns, hotspot locations, stress levels, and overall mechanical consistency to confirm that the optimized components remained structurally credible after mass reduction. The final outcome was a 20-30% mass reduction with mechanically reasonable, AM-ready geometries.

Tool stack

Tool stack

CATIA, SolidWorks, ANSYS Mechanical, SpaceClaim, OrcaSlicer, topology optimization, static structural FEA, material selection, and additive manufacturing design.

CATIA SolidWorks ANSYS Mechanical SpaceClaim OrcaSlicer Topology Optimization Static Structural FEA Material Selection Additive Manufacturing Design
ANSYS Mechanical equivalent stress result for the suspension A-arm
ANSYS Discovery mesh view of the suspension A-arm
04

Results / Outcome

Outcome 01

XX%

Add the strongest metric

Replace this with the best measurable result, such as reduction, improvement, validation accuracy, or throughput gain.

Outcome 02

YY

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Takeaway

01

What this project proves

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