Boosting 3D printing design with simulation to identify and propose best process parameters for avoiding future failures

About the customer

Ford Motor Company (Ford), a Fortune 50 automotive company, operates plants worldwide and produces millions of vehicles every year. Committed to being one of the world’s most reliable mobility and intelligent vehicle design companies, Ford maintains engineering centers in locations like the United States (U.S.), India, Mexico, Germany and Brazil. More than 30 years ago, Ford began working with Siemens Digital Industries Software to support the company’s product development activities. Today, the company employs Siemens software globally to support the development of Ford cars, trucks and heavy equipment.

Their challenge

Additive manufacturing (AM) is the process of adding material layer by layer to manufacture components, which provides benefits in the automotive industry, including component cost reduction, systems and tooling improvements and the freedom to create more complex geometries and designs. As this technology is relatively new, however, AM expertise in the industry is not on par with that of traditional manufacturing.

It is easy to overlook critical considerations in pursuit of mechanical design efficiency and difficult to set printer parameters that, if chosen incorrectly, could produce structural failures, performance deficiencies and aesthetic issues in the printed components.

Plastic strain (CAE model vs physical testing).

Our solution

Implementing AM along with the Simcenter™ Inspire™ software 3D printing simulation within the product development process has helped Ford identify possible error states throughout the manufacturing lifecycle, providing relevant information to optimize and ensure first-time quality via design.

Specifically, bracket design is meant to provide better heat management via internal cooling channel design. Simulation allows the creation of complex, optimal geometries for brackets, which promotes a more effective heat dissipation and ultimately produces better mechanical behavior and dimension control for these components.

Using the Simcenter Inspire 3D printing simulation, together with a design of experiment process created with Simcenter Hyperstudy™ software, has helped solve issues that typically happen at the printing stage such as detachment of the supports and aesthetic finish problems, and enables printer parameter optimization for peak performance. The Renishaw AM500Q printer datasheet was used to determine the operational range of the parameters that define the process, such as laser power and powder layer thickness, whereas maximum
displacement and maximum temperature were selected as output variables. This process provides designers and engineers with a better understanding of process parameters and their impact on results.

Utilizing Simcenter Inspire and Simcenter Hyperstudy has helped Ford develop new tools and methods for implementing beneficial AM technologies. Simcenter is part of the Siemens Xcelerator business platform of software, hardware and services.

DoE runs and results.

Results

With software solutions from Siemens, Ford better understands the 3D printing manufacturing processes that were covered. The simulation showed good A to B comparison results and correlations between the finite element analysis (FEA) model and physical testing, since the simulation method was able to provide qualitative correlations against the result on the physical manufacturing method. As a result, Ford can identify and propose the best process parameters to avoid future failures before going out to production.

“With Siemens’ additive manufacturing tools, we are able to successfully predict failures on physical models reducing production costs,” says José Cazares, PT cooling computer-aided engineering (CAE) engineer at Ford.

Using Simcenter Hyperstudy technology for design of experiments also provides value on the analysis’ decision-making, such that Ford is able to identify the impact of the process parameters on the output variables.

Baseline and run 14 CAE results.