Intelligent Prediction and Performance Optimization for Deep Underground Resource Excavation Process

Dear Colleagues,

As global energy demands continue to grow, the scope of deep underground resource extraction is expanding, making improvements in tunnel excavation efficiency a core concern in the fields of mining and civil engineering. The performance of heavy tunneling machines, which are critical in the mechanized excavation of coal and rock tunnels, directly affects the efficiency and stability of the entire tunneling process. During rock breaking, the cutting tools of the tunneling machine induce stress concentration around the tool tip, leading to crack propagation and ultimately resulting in rock fragmentation under combined shear and tensile forces. This complex rock-breaking process is influenced by numerous factors, including cutting parameters (e.g., cutting speed, oscillation frequency, drilling rate, cutting depth, cutting angle, and installation angle), as well as the dynamic physical and mechanical properties of the rock.

However, the interaction mechanisms between the cutting tools of heavy tunneling machines and deep rock formations are highly complex and difficult to precisely understand. This often leads to the rapid wear of machine components, reduced tool life, and decreased tunneling efficiency. Under varying cutting parameters and rock conditions, including different rock strengths and in situ stress states, significant changes occur in cutting resistance, cutting force, surface stress/strain distribution, thermal field, and wear characteristics of cutting tools. These changes not only affect tool performance, such as rock-breaking capacity, penetration depth, and rock fragment size distribution, but also have a direct impact on overall rock-breaking efficiency.

To address these critical challenges, this Special Issue aims to focus on exploring the intricate mapping relationships between the cutting parameters of heavy tunneling machines and the dynamic physical and mechanical properties of deep rock formations. It will also delve into the rock-breaking mechanisms of cutting tools under various operational conditions. Special attention will be paid to the application of artificial intelligence (AI) and machine learning (ML) techniques in developing precise mathematical models that describe the interaction between cutting tools and deep rock formations. By analyzing extensive experimental data, numerical simulation results, and field data, intelligent predictions of cutting tool performance under different conditions can be made, and operational parameters can be optimized to significantly enhance the efficiency, stability, and longevity of heavy tunneling machines.

Specific research areas include, but are not limited to, the following:

1. Study of macro- and micro-scale fracture and damage characteristics of rocks under cyclic dynamic loading conditions.
2. Investigation of macro- and micro-scale fracture and damage characteristics of cutting tools and metallic materials under cyclic dynamic loading conditions.
3. Analysis of cutting force, surface temperature, surface stress/strain distribution, and wear characteristics during the interaction between cutting tools and rocks.
4. Development of interaction mapping models between cutting parameters of heavy rock tunneling machine tools and the dynamic physical and mechanical properties of rocks.
5. Numerical simulation and optimization of cutting parameters during coal and rock tunneling processes.
6. Dynamics analysis and optimization techniques for tunneling machine cutting tools.
7. Modeling studies on the impact fracture and damage of rocks and metallic materials.
8. Intelligent monitoring and prediction of tool wear states based on deep learning.
9. Adaptive optimization systems for cutting parameters of tunneling machines based on reinforcement learning.

You may choose our Joint Special Issue in Processes.

Dr. Tao Meng
Dr. Gan Feng
Dr. Reza Taherdangkoo
Dr. Guanghui Zhao
Prof. Dr. Tingzhuang Han
Guest Editors

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• rocks
• materials
• mining