Materials processing is a critical subset of manufacturing which is benefitting by implementing machine learning to create knowledge from the data mined/collected and gain a deeper understanding of manufacturing processes. In this study, we focus on aluminum high-pressure die-casting (HPDC) process, which constitutes over 60% of all cast Al components. Routinely collected process data over a year's time of serial production are used to make predictions on mechanical properties of castings, specifically, the ultimate tensile strength (UTS). Random Forest, Support Vector Machine (SVM), and XGBoost regression algorithms were selected from the machine learning spectrum along with a Neural Network, a deep learning method. These methods were evaluated and assessed and were compared to predictions based on historical data. Machine learning, including Neural Network, regression models do improve the predictability of UTS above that of predicting the mean from prior tests. Choosing the correct models to use for the data requires an understanding of the bias-variance trade-off such that a balance is struck between the complexity of the algorithms chosen and the size of the dataset in question. These concepts are reviewed and discussed in context of HPDC.
The lateral deformation properties of oriented polymer fibres were examined by transverse compressive and torsional experiments. A modified interfacial test system machine was used to study the transverse compressive deformation behaviour of thermally cross-linkable poly(p-1,2-dihydrocyclobutaphenylene terephthalamide) (PPXTA) fibres and of a number of commercially available polymers (Nomex, nylon, Kevlar, Dacron) and ceramic (Nicalon and FP) fibres. The torsional (shear) modulus G of PPXTA and Kevlar poly(p-phenylene terephthalamide) (PPTA) fibres was measured by pendulum experiments. During both fibre torsion and transverse compression, the deformation involves materials slip on (h k 0) planes, in the [0 0 1] direction for the torsion and the [h k 0] directions for transverse compression. The intermolecular crosslinks in PPXTA did not significantly modify the elastic transverse modulus E t and caused only slight (13%) increase in shear modulus G. However, the plastic transverse properties of cross-linked PPXTA were significantly different than those of uncross-linked PPXTA. The stress at the proportional limit p , determined from the transverse load-displacement curves, was substantially higher for the cross-linked fibres than for the uncross-linked fibres. In addition, the cross-linked PPXTA fibres exhibited a large strain recoverable response reminiscent of elastomers, whereas the PPTA and uncrosslinked PPXTA fibres exhibited a large strain irreversible response.
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