In this paper, ultrahigh‐strength steel AF1410 was milled with the carbide tool, and a total of thirty experiments were performed based on central composite design (CCD) of response surface methodology. The prediction models of milling force and surface roughness are established, respectively. The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA‐II) with milling force, surface roughness, and material removal rate as optimization objectives. The surface roughness, cutting force, and material removal rate are important indexes to measure the energy consumed in the process of product, the surface machining quality, and machining efficiency of processing, respectively. In order to minimize milling force and surface roughness and maximize material removal rate, NSGA‐II was used for multiobjective optimization to obtain the optimal fitness value of the objective function. The NSGA‐II has been applied to obtain a set of optimal combination of parameters from the Pareto‐optimal solution set to enhance the machining conditions.

The cold roll-beating forming (CRBF) process is a particular cold plastic bulk forming technology for metals that is adequate for shaping the external teeth of important parts. The process parameters of the CRBF process were studied in this work to improve the process performance. Of the CRBF process characteristics, the forming forces, tooth profile angle, surface roughness, and forming efficiency were selected as the target indices to describe the process performance. Single tooth experimental tests of ASTM 1045 steel were conducted with different roll-beating modes, spindle rotation speeds, and feed speeds. Using analysis of variance (ANOVA) and regression analysis, the influence of the process parameters in each index was investigated, and regression models of each index were established. Then, the linear weighted sum method and compound entropy weight method were used to determine the process parameters for multi-objective optimization. The results show that the impact capacity and optimum value range of the process parameters vary in different indices, and that, to achieve the comprehensive optimum effect of a small forming force, high product quality, and high forming efficiency, the optimal process parameter combination is the up-beating mode, a spindle rotation speed of 801 r/min, and a feed speed of 960 mm/min.

This paper presents a study on the variation of compressive failure mechanisms in carbon fiber reinforced polymer (CFRP) as well as the effects of material properties and structure parameters on the compressive strength in different failure mechanisms. A two-dimensional microscale model is developed to simulate numerically the compressive process of CFRP with local structure imperfections. The fiber kinking can be induced by either the interfacial failure or the matrix yielding depending on the interfacial strength of carbon fibers. CFRP with lower interfacial strength can exhibit wider kink band. Higher Young’s modulus of matrix, yield strength of matrix or misalignment level causes the failure mechanisms variation to occur at higher interfacial strengths, while the fiber diameter affects the failure mechanism variation in the opposite way. Furthermore, the compressive strength is found to be sensitive to the variation of matrix properties in the matrix-driven failure and to the fiber diameter in the interface-driven failure. These findings indicate that the strategies for the compressive strength enhancement can be different according to the compressive failure mechanisms.