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Today's metal matrix composites are widely used due to their excellent properties, which are useful for high-performance applications in the automotive and aerospace industries. Furthermore, improving the machinability of these composites is vital for improving product quality in the manufacturing process. In this research, various adaptive network-based fuzzy inference systems (ANFISs) were introduced to evaluate the effect of the feed rate, the cutting speed and the particle size on the machinability of Al–20 Mg2Si metal matrix composite. Two ANFIS models were selected as the most precise models for predicting surface roughness and cutting force. Results show that the proposed ANFIS models have an adequate accuracy in predicting the machinability of metal matrix composites. [ABSTRACT FROM AUTHOR]

Clean machining is gaining ground as one of the crucial issues in future manufacturing. Many machining industries are looking for alternative solutions to wet machining because of the costs of the latter, including environmental costs, as well as its impact on occupational health and safety. Dry machining is being proposed, but in most conditions, the cutting tool wears out quicker, leading to part quality deterioration and increased machining costs. Minimum quantity lubrication (MQL) machining is also proposed to maintain a reasonable tool life and limited cost, as compared to the situation with flood or wet machining. Both the MQL and dry machining methods, however, are susceptible to the generation of aerosols containing metallic particles that can be detrimental to occupational health and safety. Minimizing particle emissions is of prime importance since these can have serious consequences on the operator's health. They have been seen to be at a minimum at two specific cutting ranges. In this study, investigations are done to examine the effect of lubrication (dry and MQL) during the turning of the 7075-T6 aluminum alloy. The performance indexes studied are the surface roughness, the chip thickness, and aerosol generation. [ABSTRACT FROM AUTHOR]

The machinability of composite materials depends on reinforcements, matrix properties, cutting parameters, and on the cutting tool used (material, coating, and geometry). For new composites, experimental studies must be performed in order to understand their machinability, and thereby help manufacturers establishing appropriate cutting data. In this study, investigations are conducted to analyze the effects of cutting parameters and drill bit diameter on the thrust force, surface roughness, specific cutting energy, and dust emission during dry drilling of a new hybrid biocomposite consisting of polypropylene reinforced with miscanthus fibers and biochar. A full factorial design was used for the experimental design. It was found that the feed rate, the spindle speed, and the drill bit diameter have significant effects on the thrust force, the surface roughness, and the specific cutting energy. The effects of the machining parameters and the drill bit diameter on ultrafine particles emitted were not statistically significant, while the feed rate and drill bit diameter had significant effects on fine particle emission. [ABSTRACT FROM AUTHOR]