Work

Cobalt-Based Ceramic Wear-Resistant Cutting Pick Laser Cladding Process and Its Law Analysis

Year: 2025
Type: article
Abstract: As a core wear-prone component of coal mining equipment, the wear resistance of cutting picks directly affects mining efficiency and operating costs. This study addresses the premature failure of traditional hard alloy cutting picks caused by impact fatigue and abrasive wear under complex geological conditions. By introducing WC powder, the research aims to enhance the quality of the laser cladding coating on cobalt-based reinforced cutting picks and to investigate the variation in optimal process parameters with an increasing WC ratio. Five sets of L9 orthogonal experiments were conducted using the Taguchi method. Combined with the analysis of the signal-to-noise ratio (SNR), the optimal parameters under each material ratio were obtained and experimentally verified. The errors were all within 10%, which proves the reliability and repeatability of the optimization results. Subsequently, the effects of laser power, powder feeding rate and scanning speed on coating quality were systematically evaluated. Scanning speed had the most significant effect on microhardness, while laser power predominantly influenced dilution rate. For low WC content, powder feeding rate had a greater impact on porosity; as WC content increased, laser power became the main factor affecting porosity. Grey Relational Analysis (GRA) was subsequently applied to integrate the three response targets into a single grey relational grade (GRG), optimizing the parameters for each WC ratio. And the law of mutual influence between different material ratios and their process parameters was found. Wear tests on the optimized cladding layer showed that, compared with the original and pure cobalt-based picks, wear resistance increased by 45% and 80%, respectively. These results indicate a clear correlation between WC content, process parameter optimization, and improved coating performance.
Source: Coatings
Authors: Yiming Zhu, Chenguang Guo, Shengli Xue, Haitao Yue, Junlin Dai
Language: English
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415858968
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Cites: 27
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