Review demonstrates trade-offs across assisted and non-contact machining methods for SiCp/Al composites, indicating no single manufacturing strategy provides universal superiority.
Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are increasingly used in high-precision components because of their high specific stiffness and dimensional stability. However, the pronounced mechanical mismatch between hard SiC particles and the ductile aluminum matrix causes unstable material removal, rapid tool wear and severe surface and subsurface damage. This review summarizes recent advances in the precision machining of SiCp/Al composites using a feature-oriented and mechanism-centered analytical framework. First, the functional requirements and dominant damage characteristics of threaded holes, internal holes, planar surfaces and cylindrical surfaces are identified. Second, particle-scale removal and damage mechanisms, including SiC particle fracture, interfacial debonding, particle pull-out and matrix plastic deformation, are linked to measurable machining responses such as cutting force, tool wear, dimensional accuracy and surface/subsurface integrity. Third, conventional, ultrasonic-assisted, laser-assisted, electrochemical/electrical-discharge and multi-energy-field machining processes are comparatively evaluated in terms of their dominant intervention mechanisms, reported performance, limitations, applicable conditions and industrial scalability. The comparison indicates that no machining strategy is universally superior: ultrasonic assistance reduces prolonged tool–workpiece contact with limited thermal effects; laser assistance reduces cutting resistance but requires careful control of thermal damage; non-contact processes reduce direct mechanical tool wear; and multi-energy-field processes offer broader multi-response control at the cost of greater equipment and parameter complexity. Future research should emphasize standardized machinability databases, multi-scale damage prediction, in-situ process monitoring, intelligent multi-objective optimization and industrial validation.
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Ding et al. (2026) studied this question.
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