Machine Learning-Based Quality Improvement for Thermal Energy Cutting Processes

Maintaining quality control in manufacturing processes is a challenge. Historically, quality control has required time-consuming manual or semi-manual approaches that lead to large amounts of valuable data being lost. Thermal energy cutting processes outperform mechanical cutting processes; however, there are defects that require post-finishing. These secondary operations can add significant amounts of time to the overall process and impact the overall production throughput.

The team will develop and validate a digital tool that collects data on processes and quality. That data will then provide direct feedback to the operator and process planner, assisting them as they make decisions about quality control. The team plans to use sensor technology with machine learning algorithms that will incrementally train a predictive model. That model can then be consistently updated to predict quality measures based on process input parameters.

This low-cost digital solution is one way to close the quality-control loop for manufacturing processes. Leveraging the advancements in sensor technologies and data analytics can lead to significant cost savings as rework and defects are reduced. Consequently, production throughput increases and product quality is better managed. This can contribute to MxD’s core mission of enhancing U.S. manufacturing’s competitiveness through digital transformation.