Jin Qi

Boom‐type roadheaders face a trade‐off between cutting efficiency and component degradation, requiring precise performance evaluation of picks. To address high tunneling experiment costs and limitations of conventional simulations in rock modeling and motion reproduction, a performance analysis framework is developed via co‐simulation based on Multibody Dynamics and the Discrete Element Method. This framework integrates three methodological contributions: (1) a reusable parametric simulation platform is constructed to resolve the complex “environment‐motion” coupling, realizing a parameter mapping method that accurately associates macroscopic rock properties and actual motion trajectories with simulation boundary conditions; (2) oriented toward the cost‐effective cutting mode, this study evaluates the performance of picks under the typical “S‐shaped” path, specifically assessing the rock‐breaking rate, load strength, and wear rate; (3) a component decoupling strategy and motion path segmentation are implemented to systematically analyze 25 distinct performance indicators of picks. Consequently, utilizing the enhanced Latin Hypercube Sampling (LHS) and Partial Dependence Plots (PDP) to conduct batch simulations, this study rigorously investigates the performance evolution laws, degradation mechanisms, and key influencing factors across different cutting phases. This work provides a rigorous workflow for tunneling equipment optimization, especially for the development of high‐fidelity digital twins.

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection.