Simulation-Driven Support Optimization for Additive Manufacturing of a Tibial Baseplate

A Collaboration Between Armadillo Additive and AdditiveLab

White Paper

Introduction

The tibial baseplate, a key component in total knee arthroplasty, serves as the interface between the patient’s bone and the articulating polyethylene insert. It must combine high mechanical stability with precise geometry and biocompatibility. To achieve this, manufacturers often rely on titanium alloys (typically Ti6Al4V) with porous surfaces that promote bone ingrowth, combined with complex internal features for fixation and stress distribution.

Additive manufacturing (AM) has become an increasingly preferred production method for such implants. It enables unparalleled design freedom, allowing engineers to integrate porous regions, organic contours, and optimized internal structures directly into the implant geometry. For medical device companies, this means more consistent part quality, patient-specific customization, and reduced tooling costs compared to traditional machining or casting.

Armadillo Additive’s Expertise

Armadillo Additive, a leader in the design and production of high-end medical implants, has years of experience developing and manufacturing orthopedic components using metal AM technologies. Through rigorous process control, advanced powder-bed fusion systems, and refined post-processing techniques, Armadillo has established itself as a trusted partner for high-quality, regulatory-compliant implant production.

A tibial baseplate designed by Armadillo Additive.

Armadillo Additive combines years of expertise in medical implant manufacturing with advanced design capabilities in nTop, enabling complex, organic geometries optimized for performance and manufacturability. Their innovation extends beyond geometry, targeting process efficiency, cost reduction, and sustainability. Aware that support structures critically influence build success and cost, Armadillo continuously explores smarter, more efficient support strategies to enhance overall production outcomes.

The Role of Supports in Metal Additive Manufacturing

In metal additive manufacturing, supports are temporary structures printed alongside the part to anchor it to the build plate, dissipate heat, and mitigate distortion during the melting and cooling cycles. Supports also prevent warping, delamination, or collapse of overhanging surfaces.

However, these structures are non-functional and must be removed after printing, often manually or through machining, adding time, material waste, and cost. Traditionally, supports are designed with uniform parameters (density, thickness, spacing) throughout the part, without considering the local mechanical or thermal loads that develop during the AM process

The Collaboration: Armadillo Additive and AdditiveLab

In a joint effort to push the boundaries of support design, Armadillo Additive partnered with AdditiveLab, a leading provider of simulation solutions for additive manufacturing. The goal: to explore how AM process simulation can drive localized optimization of support structures, making them stronger where needed and lighter where possible. The demonstration case focused on the tibial baseplate, leveraging both Armadillo’s manufacturing expertise and AdditiveLab’s simulation-driven workflow to optimize the part’s support configuration.

Tibial baseplate design oriented for production with its supports, by Armadillo Additive.

Workflow Overview

1. Build Preparation

The tibial baseplate geometry was positioned and oriented on the build platform to ensure optimal surface quality and heat dissipation. Conventional solid tree-supports were generated following standard build-preparation practices.

2. AM Process Simulation

Using AdditiveLab’s physics-based process simulation, the manufacturing process was virtually replicated. The analysis revealed localized stress patterns within the part and its supports resulting from cyclic heating and cooling, as well as forces introduced by global part distortions.

Mechanical analysis indicating high stress zones highlighted in red by AdditiveLab simulation.

3. Identification of Critical Regions

From the simulation results, regions of the supports experiencing elevated stresses or risk of failure were identified. These high-load areas indicated where additional stiffness was necessary to prevent detachment or local deformation, while zones subjected to lower stresses revealed potential for material reduction and more efficient support design.

Stress zones exported as point cloud data by AdditiveLab simulation.

4. Data-Driven Support Redesign

The simulation results were exported and used within Armadillo’s design environment to generate Voronoi-based lattice supports. Support density, and therefore stiffness, was locally adapted according to the simulation data: denser in high-stress areas, sparser in low-stress zones.

Support conversion to a lightweight lattice design without consideration of stresses, by Armadillo Additive.

5. Optimized Manufacturing

The resulting support structures effectively sustained process-induced loads while requiring less material and reduced laser exposure time, leading to faster builds and easier post-processing.

Optimized supports with a lightweight lattice design on lower stress zones, and solid material on higher stress concentration zones, by Armadillo Additive.

Results and Benefits

The simulation-driven workflow demonstrated several key advantages:

• Reduced Support Volume: Optimized structures used significantly less material compared to uniform supports.

• Shorter Build Time: Lower support mass translates to shorter laser exposure times and reduced overall build duration.

• Improved Post-Processing: Simplified support removal, reduced labor, and minimized surface damage.

• Maintained Part Integrity: Despite the reduced support material, distortion and defect levels remained within the same tight tolerance range required for medical implants.

This approach proves that simulation-guided support optimization can effectively balance manufacturability, cost, and quality, without compromising on the stringent performance standards expected in orthopedic implant production.

Conclusion

The collaboration between Armadillo Additive and AdditiveLab showcases how advanced simulation tools can transform additive manufacturing from an experience-based process to a data-driven, optimized workflow. By tailoring support stiffness according to real thermo-mechanical conditions during printing, manufacturers can achieve substantial material and time savings while maintaining uncompromised quality.

For high-value applications such as medical implants, this represents a decisive competitive edge, enabling faster production, reduced costs, and more sustainable operations, all while ensuring the precision and reliability patients depend on.

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