Mohammad Hossein Nikzad , Mohammad Heidari-Rarani , Reza Rasti
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引用次数: 0
Abstract
This study applied a computationally efficient Taguchi-based long-short-term memory (LSTM) algorithm to predict the elastic modulus of 3D-printed polylactic acid (PLA) specimens. 128 data points were collected from the literature, and 80% were allocated for training and the rest for the validation of the LSTM algorithm. The results suggested that the LSTM algorithm, configured with 25 units in the first memory cell, 100 units in the second memory cell, the “selu” activation function in the first memory cell, the “elu” activation function in the second memory cell, the RMSprop optimizer, and a learning rate of 0.01, was precisely able to predict the elastic modulus of 3D-printed PLA parts.
期刊介绍:
Materials Letters has an open access mirror journal Materials Letters: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
Materials Letters is dedicated to publishing novel, cutting edge reports of broad interest to the materials community. The journal provides a forum for materials scientists and engineers, physicists, and chemists to rapidly communicate on the most important topics in the field of materials.
Contributions include, but are not limited to, a variety of topics such as:
• Materials - Metals and alloys, amorphous solids, ceramics, composites, polymers, semiconductors
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• Properties - Mechanical, magnetic, optical, electrical, ferroelectric, thermal, interfacial, transport, thermodynamic
• Synthesis - Quenching, solid state, solidification, solution synthesis, vapor deposition, high pressure, explosive