Adaptive Tempering in High-Pressure Die Casting through Prediction Functions

Digitisation and cross-linking in high-pressure die castingHigh-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, cycl...

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Vydané v:Light Metals 2022 s. 1
Hlavní autori: Disselhoff, Torben, Tewes, Sebastian, Biehl, Sebastian
Médium: Kapitola
Jazyk:English
Vydavateľské údaje: Switzerland Springer Nature 2022
Springer International Publishing AG
Springer International Publishing
Edícia:The Minerals, Metals & Materials Series
Predmet:
General References
High-pressure die casting
Machine learning
Metals & Metallurgy
Prediction function
Process data analysis
Process drift
Tempering units
ISBN:3030925285, 9783030925284
ISSN:2367-1181, 2367-1696
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Popis
Shrnutí:Digitisation and cross-linking in high-pressure die castingHigh-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 analysisProcess 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 learningMachine learning, and linked with quality-relevant machine key performance data of the HPDC machine. The resulting prediction functionsPrediction function generate process control options to holistically optimise casting production.
ISBN:3030925285
9783030925284
ISSN:2367-1181
2367-1696
DOI:10.1007/978-3-030-92529-1_100