MF-TLMLP: A multi-channel fusion-based multi-fidelity transfer learning method for predicting the cutting performance of conical picks
- 1Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.
- 2Sany Heavy Equipment International Holdings Co., Ltd., Shenyang, China.
Abstract
Conical picks are critical components in key excavation equipment (including shearers, continuous miners, and roadheaders) employed for rock fragmentation in mining and civil engineering. This study proposes the multi-fidelity transfer learning multi-layer perceptron (MF-TLMLP) method to predict cutting performance. Addressing the scarcity of high-fidelity (HF) data, MF-TLMLP integrates low-fidelity (LF) and HF data through a multi-channel fusion and transfer learning approach. Simulations using Latin hypercube sampling (LHS) generated 2500 LF samples and 500 HF samples. Compared to traditional Multi-Layer Perceptron (MLP) and transfer learning models, MF-TLMLP demonstrated superior performance, achieving a higher coefficient of determination (R2) and a lower mean squared error (MSE). Morris screening based on mean absolute elementary effects revealed output-dependent sensitivities, with cutting thickness dominating the force responses and cutting speed dominating the pulse-number responses within the sampled domain. These results demonstrate the potential of MF-TLMLP for predicting HF numerical cutting responses for the investigated rock properties, parameter ranges, and adjacent double-pick configuration.
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BibTeX
@article{zhou2026mftlmlp,
title = {{MF-TLMLP: A multi-channel fusion-based multi-fidelity transfer learning method for predicting the cutting performance of conical picks}},
author = {Meng Zhou and Xiaofei Song and Lingtao Weng and Weiguo Gao and Ketong Dong and Guandong Lu and Ruilin Ni and Dawei Zhang},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359537},
url = {https://doi.org/10.1371/journal.pone.0359537},
note = {CC BY},
}
RIS
TY - JOUR
TI - MF-TLMLP: A multi-channel fusion-based multi-fidelity transfer learning method for predicting the cutting performance of conical picks
AU - Meng Zhou
AU - Xiaofei Song
AU - Lingtao Weng
AU - Weiguo Gao
AU - Ketong Dong
AU - Guandong Lu
AU - Ruilin Ni
AU - Dawei Zhang
PY - 2026
DA - 2026/10/23
JO - PLOS One
DO - 10.1371/journal.pone.0359537
UR - https://doi.org/10.1371/journal.pone.0359537
AB - Conical picks are critical components in key excavation equipment (including shearers, continuous miners, and roadheaders) employed for rock fragmentation in mining and civil engineering. This study proposes the multi-fidelity transfer learning multi-layer perceptron (MF-TLMLP) method to predict cutting performance. Addressing the scarcity of high-fidelity (HF) data, MF-TLMLP integrates low-fidelity (LF) and HF data through a multi-channel fusion and transfer learning approach. Simulations using Latin hypercube sampling (LHS) generated 2500 LF samples and 500 HF samples. Compared to traditional Multi-Layer Perceptron (MLP) and transfer learning models, MF-TLMLP demonstrated superior performance, achieving a higher coefficient of determination (R2) and a lower mean squared error (MSE). Morris screening based on mean absolute elementary effects revealed output-dependent sensitivities, with cutting thickness dominating the force responses and cutting speed dominating the pulse-number responses within the sampled domain. These results demonstrate the potential of MF-TLMLP for predicting HF numerical cutting responses for the investigated rock properties, parameter ranges, and adjacent double-pick configuration.
SN - 1932-6203
ER -