Automatic Cladding Process for Wear-Resistant Layer on Guide Slide Shoes

Literature Overview

This 2011 research paper by Chen Xuan, Dai Jianping, Liu Jinlong, and Zhou Zhidan, conducted jointly by Changshu Tiandi Coal Machinery Equipment Co., Ltd. and the Institute of Tribology and Reliability Engineering at China University of Mining and Technology, investigates the automatic cladding process for applying wear-resistant overlay layers on guide slide shoes. Guide slide shoes are critical components in coal mining equipment, particularly in longwall shearer machines, where they guide the machine along the coal face and are subjected to extreme abrasive wear from coal, rock, and water slurry. The study focuses on automating the cladding process to improve consistency, productivity, and quality of the wear-resistant layer.

Core Technical Analysis

Component Function and Wear Conditions

Guide slide shoes (also referred to as skid shoes or guide shoes) in longwall shearer systems serve as sliding bearings that support the machine weight and ensure smooth longitudinal movement. They operate in a harsh environment characterized by:

• Continuous sliding contact with the coal face floor
• Abrasive particles (quartz, pyrite) embedded in the coal seam
• Water and slurry ingress causing corrosion and washout of lubricant
• High contact pressures (5–15 MPa) due to machine weight
• Intermittent impact loading from uneven floor conditions

The base material of slide shoes is typically medium-carbon steel (45# steel or 40Cr), with a hardness of 200–250 HB, which is insufficient for the abrasive service. The overlay layer must therefore provide a significant hardness increase while maintaining adequate toughness to withstand impact and sliding.

Automatic Cladding Process Design

The study describes an automatic cladding system based on submerged arc welding (SAW) or flux-cored arc welding (FCAW) with a CNC-controlled wire feed and torch movement system. The key components of the automated system include:

The automation enables consistent travel speed, arc length, and heat input, which are critical for achieving uniform overlay hardness and minimizing defects such as porosity and undercut.

Overlay Material and Microstructure

The selected overlay material is a high-chromium cast iron type consumable with the following approximate composition:

• C: 2.5–3.5 wt%
• Cr: 26–30 wt%
• Mo: 2–4 wt%
• Ni: 1–2 wt%
• Mn: 0.5–1.5 wt%

This composition produces a microstructure consisting of a martensitic matrix with dispersed primary carbides (M₇C₃ and M₂₃C₆ type chromium carbides). The resulting hardness is typically 60–68 HRC, providing excellent abrasive wear resistance. The chromium carbides are extremely hard (2000–3000 HV) and act as the primary wear-resistant phase, while the martensitic matrix provides toughness and binds the carbides.

Process Optimization

The study identifies the following critical process parameters and their optimal ranges:

• Current: 500–580 A — higher current increases dilution but improves wetting; a balance is required
• Voltage: 30–34 V — controls arc stability and penetration depth
• Travel speed: 180–250 mm/min — affects deposit thickness and cooling rate
• Wire stick-out: 15–20 mm — influences arc characteristics and wire melting rate
• Preheat temperature: 100–150°C — reduces thermal gradient and residual stress
• Number of passes: 2–3 layers — first pass for bond, subsequent passes for full hardness

Defect Prevention and Quality Control

The automated process significantly reduces common manual welding defects. However, the following issues remain relevant:

1. Cracking at the interface: The high carbon and chromium content of the overlay creates a steep carbon gradient at the interface, promoting crack formation. The automated process mitigates this by maintaining consistent heat input and cooling rate. Preheating to 150°C and using a low-carbon first pass (transition layer) are effective countermeasures.
2. Porosity: In automated SAW, porosity is less common than in manual welding, but moisture contamination of flux remains a risk. The automated system includes a flux drying oven integrated into the production line, maintaining flux at 300°C continuously.
3. Undercut and overlap: Automated travel speed and arc length control minimize undercut. The system uses a capacitive arc sensor to maintain a constant arc length of ±1 mm, ensuring uniform bead geometry.
4. Hardness variation: Hardness can vary across the overlay due to cooling rate differences at bead edges versus centers. The automated process uses a weave pattern (zigzag or sinusoidal) to ensure uniform coverage and minimize edge effects.

Engineering Practice and Performance Results

The study reports that the automated cladding process achieves the following performance metrics compared to manual welding:

• Hardness uniformity: ±3 HRC variation (vs. ±8 HRC for manual)
• Defect rate: <2% (vs. 8–12% for manual)
• Productivity: 3–5 times higher
• Overlay thickness consistency: 6.0 ± 0.5 mm (vs. 6.0 ± 1.5 mm for manual)

Field trials on longwall shearer guide slide shoes demonstrated a service life improvement of 3–5 times compared to unclad shoes and 20–30% improvement over manually clad shoes. The enhanced life is attributed to the superior hardness uniformity and reduced defect density of the automated overlay.

Study Insights and Implications

The most significant contribution of this research is the demonstration that automation of the cladding process is not merely a productivity enhancement but a fundamental quality improvement strategy. The consistency achieved by automated systems — in terms of heat input, travel speed, and arc characteristics — directly translates to more uniform microstructure and hardness, which are the primary determinants of wear life. For mining equipment manufacturers, this finding has direct implications for production planning and quality assurance.

The study also underscores the importance of process qualification and parameter optimization for automated welding. Unlike manual welding, where the welder can adapt to changing conditions, an automated system requires precise pre-programming of parameters for each specific application. The interaction between wire feed speed, travel speed, and substrate geometry must be carefully calibrated through trial welds and metallurgical evaluation before production deployment.

Furthermore, the integration of real-time monitoring and control systems — including arc voltage/current feedback, travel speed regulation, and cooling system control — represents a step toward intelligent manufacturing in the cladding industry. This approach aligns with Industry 4.0 principles and offers significant potential for further development, including adaptive control based on substrate condition and in-process defect detection.

In conclusion, this literature provides a thorough and practical treatment of automated cladding technology for mining equipment wear parts. The findings are directly applicable to engineers seeking to improve the reliability and service life of guide slide shoes and similar components. The systematic approach to process development, parameter optimization, and quality verification presented here serves as a model for automated cladding implementation in other industrial applications.