In modern manufacturing, optimization algorithms have become a key tool for improving the efficiency and quality of machining technology. As computing technology advances and artificial intelligence evolves, these algorithms are assuming an increasingly vital role in the parameter optimization of machining processes. Currently, the development of the response surface method, genetic algorithm, Taguchi method, and particle swarm optimization algorithm is relatively mature, and their applications in process parameter optimization are quite extensive. They are increasingly used as optimization objectives for surface roughness, subsurface damage, cutting forces, and mechanical properties, both for machining and special machining. This article provides a systematic review of the application and developmental trends of optimization algorithms within the realm of practical engineering production. It delves into the classification, definition, and current state of research concerning process parameter optimization algorithms in engineering manufacturing processes, both domestically and internationally. Furthermore, it offers a detailed exploration of the specific applications of these optimization algorithms in real-world scenarios. The evolution of optimization algorithms is geared towards bolstering the competitiveness of the future manufacturing industry and fostering the advancement of manufacturing technology towards greater efficiency, sustainability, and customization.

Carbon fiber resin matrix composites (CFRP) are widely recognized for their exceptional properties such as high temperature resistance and high strength, making them indispensable in aerospace, automotive, and medical applications. Despite their growing use, precision machining of CFRP remains challenging. Traditional mechanical machining methods often lead to severe tool wear, matrix damage, fiber pullout, delamination, and chipping. In contrast, nanosecond pulsed laser machining has garnered significant attention due to its high precision, minimal heat-affected zone (HAZ), and versatility in processing various materials. In this study, a finite element model was developed to account for the anisotropic heat transfer and non-homogeneous properties of CFRP, enabling accurate simulation of laser machining processes. The study analyzed the influence of laser parameters on machining quality and revealed the ablation mechanism and HAZ evolution under varying laser conditions. Notably, it was observed that the thermal conductivity along the carbon fiber’s axial direction is higher than in the radial direction, resulting in an elliptical ablation pattern after laser irradiation. Additionally, the effects of the laser power, pulse frequency, and scanning speed on the depth and width of grooves were investigated through finite element simulations and validation experiments. A heat accumulation effect between laser pulses was observed, where resin matrix material around the grooves was removed once the accumulated heat exceeded the resin’s pyrolysis temperature. In addition, if there is too much laser power or too small a laser scanning speed, the fiber will undergo severe ablation removal, which will form serious thermal damage and a heat-affected zone. Gradually increasing the laser power or decreasing the scanning speed led to deeper and wider grooves, with an inverted triangular morphology. Moreover, the selection of different parameters had a significant effect on the ablation morphology, heat-affected zone, and the contour parameters of the grooves. This research contributes to understanding the laser–CFRP interaction mechanism and offers insights for optimizing laser processing parameters to improve material processing accuracy and efficiency, further expanding the potential applications of laser technology in composite material machining.

Fractal geometry theory has been widely used in engineering image processing. In this work, the basic principles and features of fractal geometry are first introduced and its importance in image processing is explained. The features of the symmetry and asymmetry of images are represented in fractal geometry and symmetry scaling is utilized to deal with image processing problems in engineering applications. Subsequently, specific applications of fractal geometry in engineering image processing are discussed in detail in terms of image compression, edge detection, texture analysis, and image reconstruction and restoration. The exploration of these applications reveals the advantages and usefulness of fractal geometry theory in image processing, and it is found that the image has certain symmetry and self-similarity, which is conducive to the establishment of mathematical models for the statistics of graphic contours and shapes. Finally, the unique value of fractal geometry in engineering image processing is further emphasized by comparing the innovations of fractal geometry with traditional image processing methods, which prompts the in-depth consideration of its potential value in this field. This paper provides new insights and directions for the research of engineering image processing, which is of positive significance for future research.

Achieving precise prediction and intelligent control remains a pivotal challenge in cutting processes. This need is addressed through a comprehensive survey of three critical enabling technologies: cutting force/temperature modeling, tool path planning, and vibration suppression. First, the evolution of cutting force and temperature modeling is analyzed, tracing its progression from traditional analytical methods and finite-element numerical simulations to data-driven models such as machine learning (ML) and physics-informed neural networks. This analysis highlights multiphysics coupling and model–data fusion as key to enhancing prediction accuracy. Subsequently, the evolution of tool path planning is examined, showing its development from a geometric interpolation problem into a multi-objective optimization challenge incorporating dynamic constraints, involving computational geometry, graph theory, and meta-heuristic algorithms. Finally, stability analysis based on time-delay differential equations, state identification via signal processing and ML, and active control strategies for vibration suppression are discussed. In conclusion, mathematical methods are shown to be fundamentally integrated throughout the ‘perception–prediction–decision–control’ closed-loop of the cutting process. This integration provides a solid theoretical foundation and technical support for building high-performance manufacturing systems dedicated to complex curved critical components.

In this paper, a femtosecond laser removal model of PMMA was built, and the surface primary and secondary microstructures of PMMA were fabricated. The surface morphology and wettability of the microstructures were measured and analyzed by an ultra-depth three-dimensional microscope and a contact angle measuring instrument. The results showed that femtosecond laser direct writing processing of PMMA surface microstructure changed the PMMA wettability from a hydrophilic state to a hydrophobic state. Meanwhile, the size parameters of multistage microstructures on the PMMA surface were optimized, according to numerical simulation. The contact angle of the optimized microstructures could exceed 150°, achieving superhydrophobicity of the PMMA surface.