Today’s automotive manufacturers face increasing pressure to innovate rapidly while optimizing their development processes and material usage, making efficient production essential for maintaining an edge in a highly competitive market. In this context, Ford Mexico, headquartered in Mexico City, saw the need to streamline its stamping process, and – being a long-time customer – turned to the Altair team to solve this challenge.
Sheet metal forming in the automotive industry
Sheet metal stamping is fundamental to the automotive manufacturing industry. A wide range of different tool, die, and process combinations are employed to create an equally diverse array of components. Traditionally, determining the optimal stamping process for a specific part design has been a time-consuming and labour-intensive task, heavily dependent on the stamping engineer’s expertise and experience. To address this issue, Ford Mexico began documenting successful metal stamping production runs over a 5-year period. Management’s goal was to capture in-house domain knowledge and best-practices to explore ways to speed the selection of the best stamping process for future production runs to enable business benefits including increased plant efficiency and part quality, reduction of scrap material, and the ability to rapidly train new personnel.
Finding the right process
In many production facilities, there are multiple sheet metal stamping processes available to form nested and individual parts, including progressive, transfer, and tandem press lines. For a specific part design, numerous factors come into play to identify the optimal or most efficient stamping process, such as the material type, thickness, part width, and desired surface finish.
The success or failure in selecting the right process depends significantly on the experience and expertise of the manufacturing process engineer. However, increasing design complexity, non-conventional material types, and a multitude of process combinations can pose challenges even for the most seasoned process engineers, necessitating a labour and material-intensive trial-and-error prove-out process.
Cutting material waste
Material utilization is a particularly critical benchmark. Most automotive plants expect around 60% material utilization in their stamping mills while the remaining 40% is wasted. Ford aimed to improve these metrics while also improving the accuracy of selecting the optimal stamping process on the first attempt and increasing First Time Through (FTT) rates.
To achieve these goals, Ford Mexico started documenting and accumulating a valuable asset: large amounts of clean data associated with their successful production runs.
Spanning a 5-year period, process engineers recorded successful stamping processes for thousands of parts. Captured in this historical data were valuable insights but the question now was how they could use this information to help automate and guide the selection of best stamping process for a given part design.
Handling the data with Altair
First learning of Altair® Knowledge Studio® through an Altair technology briefing, Ford Mexico approached Altair to explore the possibility of applying Altair’s machine learning and predictive analytics solution to support their business objectives.
Leveraging the data Ford collected for over 3,000 stamping processes identified as being representative of future requirements, Ford’s stamping domain experts and Altair’s solution architects collaborated to develop an accurate, reliable machine learning model with Knowledge Studio.
Knowledge Studio offers 15 different machine learning models allowing users to explore, select and train the model that best fits their data. Using subsets of the data, the team ran a series of tests to determine which was most effective. With an accuracy rate of over 90%, the decision tree model produced the most consistent results. In the process, a surprising – and valuable – discovery was made. In terms of selecting the optimal stamping process, the most important factors are the overall dimensions and thickness of the finished part.
Individually, these factors are insufficient for making a final decision. However, when combined with all other data points, Knowledge Studio’s machine learning algorithm provided Ford with results that are nearly 100% accurate.
Improved processes and training
The machine learning predictive capabilities of Knowledge Stu dio demonstrated high accuracy and effectively automated much of the stamping process selection. By minimizing manual trial-and error process validations and rework, more time was available for stamping process engineers to address the most difficult and complex part designs further enhancing production efficiency and business value.
Overall, projected throughput increased threefold, while improvements in FTT rates led to reduced rework time – all accomplished without the need for additional resources.
Additionally, the Knowledge Studio machine learning model successfully captured the company's in-house domain knowledge. This supported a faster learning curve for training new personnel, ensuring that valuable expertise was efficiently passed on to new employees. By using this technology, the company has enhanced its training processes and maintained a knowledgeable workforce.
