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Tungsten heavy alloys (WHAs) are an extremely hard-to-machine material extensively used in demanding applications such as missile liners, aerospace, and optical molds. However, the machining of WHAs remains a challenging task as a result of their high density and elastic stiffness which lead to the deterioration of the machined surface roughness. This paper proposes a brand-new multi-objective dung beetle algorithm. It does not take the cutting parameters (i.e., cutting speed, feed rate, and depth of cut) as the optimization objects but directly optimizes cutting forces and vibration signals monitored using a multi-sensor (i.e., dynamometer and accelerometer). The cutting parameters in the WHA turning process are analyzed through the use of the response surface method (RSM) and the improved dung beetle optimization algorithm. Experimental verification shows that the algorithm has better convergence speed and optimization ability compared with similar algorithms. The optimized forces and vibration are reduced by 9.7% and 46.47%, respectively, and the surface roughness Ra of the machined surface is reduced by 18.2%. The proposed modeling and optimization algorithms are anticipated to be powerful to provide the basis for the parameter optimization in the cutting of WHAs.

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Double-sided lapping (DSL) is a precision process widely used for machining flat workpieces, such as optical windows, wafers, and brake pads owing to its high efficiency and parallelism. However, the mechanism of parallelism error reduced by the DSL process was rarely investigated. Furthermore, the relationship between parallelism and the flatness was not clearly illustrated. To explain why the parallelism of workpieces becomes convergent by the DSL, a theoretical model has been developed in this paper by calculating the parallelism evolution with the consideration of variation contact situations between workpieces and lapping plates for the first time. Moreover, several workpieces, including a slanted one rendering the model close to the actual process, are taken to calculate the parallelism evolution, and the mechanism of the parallelism error reduced by the DSL process is clarified. The calculation result has indicated that the parallelism error was reduced from 100.0 μm to 25.6 μm based on the parallelism evolution model. The experimental results showed that the parallelism improved from 108.6 μm to 28.2 μm, which agreed with the theoretical results well.