Sheet metal stamping is fundamental to the automotive manufacturing industry. A vast array of different tool, die, and process combinations are employed to create an equally diverse array of components. Traditionally, identifying the optimum approach for each part has been a labor intensive and time-consuming task that requires engineering teams with high levels of skill and experience. This case study demonstrates how Altair Knowledge Studio, a general-purpose data analytics tool, can enable engineering managers and data analysts to deliver clear and quantifiable benefits in the manufacturing domain. For Ford, this is reflected in dramatic improvements in the speed and efficiency with which the best possible sheet metal stamping processes were selected.

Serba Dinamik is an engineering company specializing in operations and maintenance (O&M), engineering, procurement, construction and commissioning (EPCC), and IT solutions for energy exploration and production firms. Their team worked with Altair to develop a Smart Predictive Maintenance Data System (SPMDS) utilizing Knowledge Studio and Panopticon. Maintenance crews use Panopticon-powered dashboards built into SPMDS to monitor every sensor mounted on operating turbines in real time. AI models built with Knowledge Studio identify potential failures or issues that require engineering attention, and, based on that understanding, take turbines offline only when necessary.

By extracting real value from their data, manufacturers can make accurate predictions about component life, replacement requirements, energy efficiency, utilization, and other factors that have direct impacts on production capacity, throughput, quality, sales, customer acceptance, and overall efficiency.

The presentation will go through a scenario of a smart factory (along the lines of the Industry 4.0 initiative) where a chemical process within a pharmaceutical company produces a substance critical to the production of medications.