Optimizing the Design Process Saves Precious Time in Mass Production


Today’s challenges of the automotive industry are short product development cycles along with resource efficiency and profitability. At the same time, desired or necessary design changes to products and components should still be feasible as late as possible in the development process to keep the required flexibility.

Especially for the mass production process of aluminum diecasting, used at Toyota Motor Corporation for various highly complex components, these changes lead to time-consuming and cost-intensive modifications of the valuable production tools and process layout.

To overcome this conflict, the engineers of Toyota Motor Corporation strive to move from a sequential to a concurrent development cycle of product and process design as well as production planning. The objective is to meet the partly contrary requirements at the earliest stage possible to minimize costly changes and prototyping efforts.

The application of virtual methods, such as casting process simulation, is key to assessing feasibility and potential manufacturing restrictions in the very beginning of and along the whole development and design process.

The aim is to realize a robust and cost-efficient casting process that incorporates the different demands of Design, Process Engineering, and Production at the same time. Therefore, the Toyota engineers successfully integrated the methodology of autonomous engineering with MAGMASOFT into their design process. The efficient identification of significant influencing variables on casting quality and profitability is only possible through methodical process analysis and systematic test planning.

To evaluate an optimal work-flow and assess the potential benefits, the methodology of MAGMASOFT autonomous engineering was applied to a representative housing geometry.
The objective was to find the best com- promise between the requirements of the design department regarding a minimum wall thickness of the component and the process engineering aiming for a robustness and early stage economic efficiency of the casting process. The engineers of Toyota Motor Corporation wanted to guarantee that both objectives were met, resulting in a thin walled housing that can be produced robustly from the first sampling on.

As design variables of the virtual DoE in MAGMASOFT®, two cast part geometries of different wall thicknesses were combined with parametric square sections and positions of the ingates.

In a second step, the objectives and critical quality criteria for the optimization process were defined in MAGMASOFT. In this case, avoiding misruns for a robust castability and reducing air entrapment for quality reasons were the most important objectives. In order to reduce the time for preparation and calculation, a simplified simulation model was used according to the MAGMA APPROACH.

A systematic assessment and comparison of the virtual trials using MAGMASOFT scatter charts clearly revealed the best solutions for the de- fined wall thicknesses and ingate designs. The quantitative comparison of all variants using statistical methods provides reliable results without subjective influences.

In this way misruns could be completely avoided upfront, as the optimized results could easily be found in the areas of a better range for filling and a better range for quality.

The application of MAGMASOFT autonomous engineering saved precious time in comparison to the conventional simulation process, as many manual tasks like 3-D geometry model- ling and meshing could be avoided completely.

With the reference of this example, Toyota engineers proved that in addition to identifying a robust design and manufacturing solution, the required investment in working hours during design could be reduced by 50%. This helped to significantly reduce lead time in the development process as a whole.

For Toyota, the methodology of MAGMASOFT autonomous engineering today supports a more comprehensive understanding of processes, providing quantitative information at early stages of product and process development securing reliable decision making.