Work

MF-TLMLP: A multi-channel fusion-based multi-fidelity transfer learning method for predicting the cutting performance of conical picks

Year: 2026

Type: article

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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0359537