COCOON
COCOON
An exercise on traditional manufacturing processes
MAGNIGYING GLASS
An exercise on traditional manufacturing processes
MAGNIGYING GLASS
Tinkerer, Engineer, Designer
Steering Knuckle Optimization with Autodesk Generative Design
ME 329: Mechanical Analysis in Design
(Winter 2021)
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​Mentors: Professor Andrew Lew (Stanford University), Daniel Noviello (Autodesk)
​Student Team: Eleni Alexandraki, Bella Carrera, Ankur Das, Colin Zheng
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Renderings of the original knuckle manufactured in cast iron (left) and the final knuckle design (right), to be 3d printed in aluminum
Goal
Use Autodesk’s Generative Design software with new additive manufacturing constraints to optimize the weight of a steering knuckle, given a set of fixed mounting locations and vehicle loading conditions.
The Problem
The steering knuckle is the central element of a car’s suspension system, connecting the wheel to the rest of the chassis. Consequently, a steering knuckle needs to endure all ground forces and braking loads across a range of vehicle fatigue and abuse conditions. The placement of each attachment point to another component determines vehicle handling parameters, so the knuckle has many fixed geometric constraints as well. Finally, larger knuckle weights reduce the effectiveness of the suspension, reducing tire traction and harming performance.
Diagram of steering knuckle location (source)
CAD assembly provided by Autodesk
Original knuckle design provided (to be optimized)
Why Generative Design?
This design problem is a good case study for Autodesk Generative Design, a software that offers useful tools for optimization of complex design spaces. Traditional engineering design can be slow and inefficient for parts with a variety of multiaxial loads and geometric constraints, often relying on historical design precedents. Shape optimization software improves component performance, but it still relies on those initial design choices. Generative design can add or remove material as needed to link defined geometry given a range of loading conditions, as with the knuckle design challenge. The use of additive manufacturing can produce knuckle designs generated through the Generative Design workspace, with performance unachievable through conventional manufacturing.
What did the team do?
Given basic vehicle specifications, we first explored vehicle dynamics and NASTRAN simulations to identify useful load cases with which to optimize the knuckle. After the load cases were determined, we developed a workflow to adjust the load inputs in Generative Design as the software uses a Voxel based mesh that yields slightly lower stress results than the NASTRAN solver. We then analyzed the Generative Design outcomes, which were further edited in Fusion 360 and validated in NASTRAN, to produce manufacturable parts.
Load Cases
The magnitude of the abuse loads was determined with the local acceleration multiplied by the corner mass acting on the suspension knuckle.
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The fatigue loads were determined by the maximum global acceleration rate generated by the tires, and the loads acting on the knuckle in each loading case were calculated based on vehicle dynamics calculations.
The Proposed Workflow
We looked at literature reviews and studied examples of workflows of shape optimization applications for steering knuckles specifically. Our proposed workflow has adjustments made that are aimed towards mitigating Generative Design shortcomings when optimizing complex parts.
The proposed workflow is a modified version of the workflow explained in: Wan Mansor Wan Muhamad, Endra Sujatmika, Hisham Hamid, & Faris Tarlochan. Design Improvement of Steering Knuckle Component Using Shape Optimization. International Journal of Advanced Computer Science, Vol. 2, No. 2, pp. 65-69, Feb. 2012.
A Step by Step Overview of the Generative Design Workflow
Detailed workflow within the Generative Design workspace of Fusion 360.
Generative Design Outcomes
After running our generative design study, the software offered a variety of solutions, as shown below. It is critical at this stage to determine the most ideal of the suggested outcomes.
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Comparing results of different manufacturing methods
Before deciding which additive manufacturing result was best, we compared different manufacturing method results, and a number of notable differences between the 3-axis milling and AM solutions were apparent.
Down-selecting Outcomes
Both AlSi10Mg and Stainless Steel 17-4 PH were considered as the material for the additively manufactured knuckle. AlSi10Mg was the final material chosen for its lower weight and cost, as well as better manufacturability.
The outcomes were split amongst the team members for analysis and down-selection
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Post-processing Generative Design Outcomes
Due to the coarseness of the generative design solver, generated outcomes tend to have significant stress concentrations along the boundaries of predefined geometry. Using Fusion 360’s editing tools, we modified the T-splines that define any generated organic geometry, smoothing out transitions and avoiding problematic cuts.
Surface smoothening executed by Ankur Das
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obstacle geometry interference
poor transitions to preserved geometry
smoothened geometry after using T-spline tool
Final Outcome
The resultant knuckle reduced weight by over 70% while improving strength by 2 - 3 times for most load cases, except buckling.
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Preparing for Manufacturing
We used Netfabb to construct an additive manufacturing setup for the Aconity DMLS printer at Stanford, and produced a scaled model of the resultant knuckle using Stainless Steel 316L.
File preparation executed by Bella Carrera
