Intelligent Grinding Path Planning for Surface Crack Removal in Special Steel Bars

An intelligent pavement crack recognition and automatic repair algorithm based on improved DeepLabv3+ and greedy algorithm

To address the growing need for pavement maintenance, this paper presents a two-stage algorithm that integrates intelligent crack identification with automated repair path planning, aiming to boost the intelligent repair efficiency of pavement crack diseases. In the intelligent crack identification phase, a lightweight modified DeepLabv3 + model is used for pixel – level pavement crack segmentation, and operations such as thinning, spur removal and crack segmentation are applied to extract crack skeleton information. In the crack repair path planning phase, the absolute greediness of traditional greedy algorithms is overcome by adding a probability – based crack endpoint selection mechanism. Meanwhile, to boost the greedy algorithm's in – depth optimization ability, three local optimization operators, namely 2 – opt, 3 – opt, and 2 – wopt, are designed to further optimize automated repair paths. To verify the improved greedy algorithm's efficiency, the paper designed experimental scenarios with 10, 20 and 30 cracks each in the repair area. In these scenarios, it compared three algorithms: ant colony optimization (ACO), traditional greedy algorithm (GA) and the proposed improved greedy algorithm (IGA). The comparison focused on optimization ability, efficiency and solution stability. Results showed that the IGA outperformed the other two in optimization and efficiency. This paper integrates deep learning with an improved optimization algorithm to present an integrated solution for one - step road crack repair.

Improved SA algorithm for masonry layout optimization of building infill walls

This paper proposes a hybrid optimization algorithm that fuses multiple methods to address the weak global exploration ability, frequent local optima, and poor engineering adaptability in masonry layout optimization of building infill walls. The method builds a multilayer cooperative framework. It first uses the Genetic Algorithm to create a diverse population. It then applies Simulated Annealing to perform probabilistic jumping optimization. After that, it introduces sparse A search to verify topological feasibility. It finally relies on a cooperative mechanism of Adaptive Whale Optimization and iterative local search to explore the solution space in depth. Experiments on the simultaneous localization and mapping–building information modeling coupled dataset and the building information modeling component multimodal dataset show that the algorithm reaches a standard block utilization rate of 98.76 percent. It also keeps the cutting loss rate as low as 2.79%and achieves a peak stagger-joint compliance rate of 97.11%. In irregular wall scenarios, it reduces cost by up to 33.87%. The results show that this algorithm improves the optimization quality and engineering applicability of masonry layout and provides reliable technical support for precise construction and efficient material use of building infill walls.

Automated Drive Curve Offset Strategy for Corner Cleaning Based on Enhanced Principal Component Analysis

Corner cleaning is a critical sub-stage of finishing and is used to remove residual material left by previous machining operations, thereby ensuring the designed dimensional precision and surface quality. Although essential in CNC machining, many current industrial CAM software like UG NX, PowerMill systems were largely developed based on early-stage theoretical frameworks. Meanwhile, the geometric shapes of machined workpieces are becoming increasingly complex, making such software gradually unable to meet the growing requirements for tool path quality. This paper establishes a rigorous mathematical framework to bridge the gap between industrial practice and theoretical modeling. Based on this framework, an enhanced Principal Component Analysis (PCA) method is proposed to generate an optimal drive curve by integrating geometric variance maximization with vector field guided directional optimization. Furthermore, a robust offset strategy is presented, incorporating self-intersection detection, critical-point trimming, and segment connection mechanisms to ensure path continuity and smoothness. Experimental results and real machining cases demonstrate that the proposed method outperforms the widely used commercial software in terms of robustness and path smoothness, validating its effectiveness and practical applicability.

Design and Path Planning Research of Heating Pipeline Defect Inspection Robot

To address safety hazards caused by aging urban heating pipelines, this paper develops a heating pipeline inspection robot system. The design employs a purely mechanical cam-rocker mechanism, overcoming the temperature limitations of conventional actuators and enabling adaptive coverage for pipe diameters ranging from DN800 to DN1200. In terms of path planning, an improved A*-ant colony hybrid algorithm incorporating variable-diameter constraints (VD-A*-ACO) is proposed. By integrating multidimensional constraints such as turning cost, obstacle risk, and inspection efficiency, the algorithm enhances navigation performance in complex environments. MATLAB simulation results demonstrate that the robot's mechanical structure exhibits strong adaptability, while the improved algorithm reduces path length to 13.8995 in complex scenarios, lowers total cost by approximately 29.6%, and decreases diameter adjustment requirements by 50.0%, effectively validating the engineering value of synergistic optimization between structural adaptability and path planning.

Intelligent Pavement Recognition and Automated Repair Algorithm Based on LU-Net and DP

Crack repair has long been a critical task in highway maintenance. With the continuous expansion of the scale of roads under maintenance, traditional manual patching can no longer meet the growing demand. Current mainstream pavement crack repair technologies generally have the shortcoming that real-time performance and accuracy are difficult to balance. To address the dual problems of insufficient crack detection accuracy and low efficiency in automated repair path planning, this study is committed to developing an efficient intelligent repair method. It proposes a two-stage algorithm integrating intelligent crack detection and automated repair. In the intelligent detection stage, a lightweight U-shaped hybrid network (LU-Net), which combines U-Net and MobileNetV2, is designed to facilitate intelligent crack identification and trajectory extraction. In the path planning stage, an exact algorithm based on dynamic programming (DP) is introduced to determine the optimal repair sequence automatically. Comparative experiments verified the effectiveness and accuracy of the method: the detection accuracy of LU-Net reached 88.35% with a mean intersection over union (MIoU) of 80.39%; for scenarios with 5–14 crack paths, the proposed dynamic programming algorithm not only found the global optimal solution but also reduced the solution time by 90% compared with existing exact algorithms. The results show that the LU-Net segmentation model and DP algorithm can significantly save operation time while ensuring accuracy.

Research on the optimisation method of cutting process of 9310 steel aerospace thin web gears