| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Cast Shop Technology
|
| Presentation Title |
Adaptive Tempering in High Pressure Die Casting through Prediction Functions |
| Author(s) |
Torben Disselhoff, Sebastian Tewes, Sebastian Biehl |
| On-Site Speaker (Planned) |
Torben Disselhoff |
| Abstract Scope |
Digitisation and cross-linking in high pressure die casting technology (HPDC) have developed greatly over the past few years. In modern HPDC cells, almost all parameters are recorded and evaluated with the aim of achieving optimum casting production in terms of quality, cycle time and energy efficiency. However, the focus of this process data analysis and recording is particularly on the HPDC system itself and less on the periphery. This leads to possible interactions remaining undetected and avoidable casting defects continuing to occur. Therefore, the so-called tempering process, which is gaining more and more importance due to the shift towards minimum quantity spraying, is investigated in this research work. In particular, the process parameters of all tempering circuits, which change over time, are analysed with machine learning, and linked with quality-relevant machine key performance data of HPDC machine. The resulting prediction functions generate process control options to holistically optimise casting production. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Aluminum, Machine Learning, Process Technology |