Zero Defect Manufacturing by Through-Process Modelling and Control
Dr. Geir Ringen, Professor, Norwegian University of Science and Technology, Trondheim, NO-16, Norway
Co-Speaker: Torgeir Welo
Keywords: Zero-Defect Manufacturing, Through-Process Control, Adaptivity
Growing demand for personalized, sustainable, and multi-functional products is hypothesized to increase complexity in manufacturing systems. At the same time, these systems are expected to produce low-cost zero-defect products throughout a dispersed value chain. This case study explores approaches of through-process modelling and control of aluminum automotive parts, complying to stringent quality measures in terms of dimensional accuracy, mechanical properties and use-phase behavior. The particular case involves process steps from casting, heat treatment, extrusion and forming, where the latter naturally causes a set of potential failure modes, where spring-back control is one of the most critical countermeasures. The following three approaches and levels of adaptive control loops are outlined:
1. In-process feedback control based on sensing in the forming process itself
2. Feedforward control from the upstream processes
3. Integrated adaptive control, combining the feedforward and feedback functionality
The first one set forward that dimensional accuracy in advanced forming could be enhanced by adopting a superior in-line correction algorithm, beyond the hardware system or machine accuracy, that in real-time compensates for any geometrical deviation. The second approach considers that aluminum remembers its thermomechanical history, thus, by measuring critical upstream process and product parameters the forming process can compensate based on this information. Third, a combination of feed-forward and feedback systems may give the optimal solution due to quality performance and information processing time for short cycle times.
We have conducted experiments, both virtual and physical, for through-process modelling and control of an industrial aluminum value chain. Results are promising for a given range of part complexity, but data integrity, harmonization, and reliability are critical factors when tracking and analyzing information from dispersed value chains.