MouldIt: Data Efficiency of Multi-Task Temporal Representation Learning in Injection Moulding
Overview
This research paper introduces MouldIt, a model using temporal convolution and attention mechanisms for injection moulding process analysis. The authors synthesize a dataset based on an Erlanger Tr"ager part cycle and evaluate the model's ability to reconstruct signals and classify parts against classical methods like k-nearest neighbors and random forests. Results show MouldIt outperforms baselines in both single-modal and multi-modal settings, achieving specific sorting losses of 6.0% +/- 0.7% and 1.0% +/- 0.3% respectively.
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Reported quantity · cycles: 4000 other · Basis not reported · Differing source assertions
“odal and multi-modal settings. We compare it with an accept-all baseline and the classical methods k-nearest neighbors, logistic regression, random forests, and principal component analysis, which we adapt to support both tasks. MouldIt outperforms classical methods across both tasks from 4000 cycles in the single-modal setting and from 2000 cycles in the multi-modal setting. It achieves part sorting losses of 6.0 $\”
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Reported quantity · cycles: 2000 other · Basis not reported · Differing source assertions
“odal and multi-modal settings. We compare it with an accept-all baseline and the classical methods k-nearest neighbors, logistic regression, random forests, and principal component analysis, which we adapt to support both tasks. MouldIt outperforms classical methods across both tasks from 4000 cycles in the single-modal setting and from 2000 cycles in the multi-modal setting. It achieves part sorting losses of 6.0 $\”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMouldIt: Data Efficiency of Multi-Task Temporal Representation Learning in Injection Moulding
This research paper introduces MouldIt, a model using temporal convolution and attention mechanisms for injection moulding process analysis. The authors synthesize a dataset based on an Erlanger Tr"ager part cycle and evaluate the model's ability to reconstruct signals and classify parts against classical methods like k-nearest neighbors and random forests. Results show MouldIt outperforms baselines in both single-modal and multi-modal settings, achieving specific sorting losses of 6.0% +/- 0.7% and 1.0% +/- 0.3% respectively.
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