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Elastic deformation in thin-walled parts during machining is affected by the coupling of force and flexibility. Obtaining flexibility information along machining tool paths is crucial for online monitoring of this deformation. However, the current finite element method (FEM) is limited by mesh nodes, hampering its ability to accurately determine flexibility along tool paths. To overcome this limitation, this paper proposes a method that combines FEM with the surrogate model to predict flexibility accurately at any position on thin-walled parts' surfaces. The surrogate model is the hybrid model K-K-CNN based on two K-nearest neighbor (KNN-KNN) algorithms and a convolutional neural network (CNN) model. Initially, an initial dataset containing positions and flexibility of mesh nodes is generated automatically through secondary development of ABAQUS. Then, the K-K-CNN hybrid model is introduced and trained on this dataset to calculate flexibility accurately at any position on the surface of thin-walled parts. The hybrid model employs a CNN to address the nonlinear spatial correlation issue in flexibility prediction. Moreover, the hybrid model incorporates two KNN algorithms to alleviate the overfitting challenge stemming from the straightforward input features and extensive dataset size. In comparison to traditional deep learning models, the K-K-CNN hybrid model presents notable benefits in predicting flexibility for complex thin-walled parts at any given position, which affirms its robustness and accuracy. The proposed prediction method for flexibility can provide high-quality data-driven information for monitoring the elastic deformation of thin-walled parts. [ABSTRACT FROM AUTHOR]

The integral blisk has been widely used in aerospace, and its structural performance is inextricably linked to the blade surface quality. To improve the surface integrity of polished surface, ultrasonic vibration assisted belt flapwheel flexible polishing (UBFP) is proposed. In this study, the polishing principle of UBFP and the effects of vibration on surface generation are investigated, and kinematic analyses and trajectory simulations are performed. Furthermore, the influences of the main processing parameters on the polishing force and surface roughness in UBFP are explored experimentally, and the sensitivity of the main parameters is distinguished by multi-parameter relative sensitivity analysis based on Monte Carlo simulation. The results show that the ultrasonic vibration contributes to the polishing process primarily through kinematic state changing and trajectories interlacing of abrasives. Compared with conventional belt flapwheel flexible polishing, the polishing force decreases by 15.72% and the surface roughness decreases by 17.39%. The compression depth is the most sensitive parameter in the process of UBFP. This study demonstrates the feasibility of UBFP and provides a theoretical and experimental reference for improving the polishing surface quality of the blisk blade. [ABSTRACT FROM AUTHOR]

Tool wear is inevitable in actual manufacturing, especially in extreme processing conditions for machining difficult-to-cut materials. The monitoring of the tool state has an important influence on the surface quality and dimensional accuracy of the precision parts. In the previous studies, the original total power consumption is usually used to predict tool wear while ignoring the cutting power consumption accounts for a small proportion of the total power consumption of machine tools. Therefore, the accuracy is difficult to achieve the expected target. For better prediction results, a novel prediction method based on net cutting power consumption by Gaussian process regression (GPR) with ARD Matern 5/2 kernel is proposed in this study. Firstly, the physical model of net cutting power consumption is established. Then, tool wear under fixed working conditions is predicted by using the net cutting power consumption and GPR, and the advantage of the proposed method in this study is verified by comparing it with the existing methods. Finally, the proposed method is verified to obtain better prediction performance with variable cutting parameters than using total power consumption with the neural network. This study reveals that low-cost sensors like power meter can be used as an important supplement to monitoring tool conditions in the industry and also provides a research basis for predicting tool wear under different cutting conditions. [ABSTRACT FROM AUTHOR]

GH738 superalloy offers exceptional material properties at elevated temperature and is widely applied to the critical components of aero-engines and gas turbine. However, high strength and poor thermal conductivity bring significant challenges for the machining and surface qualities of GH738 superalloy. In this work, the creep feed grinding performance of GH738 was investigated. The variation laws and formation mechanism of surface integrity were explored with three common types of alumina grinding wheels under various grinding parameters. The experimental results showed that there is considerable impact of grinding parameters on the grinding force and grinding temperature. The stronger coupling of thermal and mechanical aggravates worse surface integrity. The propensity of grinding burn could be more sensitive when the maximum undeformed chip thickness is greater than 0.45–0.5 μm. With the material removal rate in the range of 0.83–1.67 mm3/mm·s, surface defects are visible, and the thickness of plastic deformation layer is about 4–6 μm. In addition, the surface quality ground by the white and pink fused alumina mixed abrasive grinding wheel (WA-PA) is slightly superior to that of the WA and BA grinding wheels. Finally, the grinding parameters for creep feed grinding GH738 are recommended as vw ∈ [50, 150] mm/min, vs ∈ [25, 35] m/s, and ap ∈ [0.2, 0.6] mm. [ABSTRACT FROM AUTHOR]

The fillet is a typical transitional form for a region with a common edge between combinatorial surfaces of blisk parts, and the processing quality of that has a strong influence on the aerodynamic performances of the parts. As a special type, the incomplete fillet is generated due to missing data of the combinatorial surfaces, which is not tangent to the combinatorial surfaces. For machining the clean-up region with incomplete fillet, the planned tool path using the conventional method may result in excess residual material, which will affect the part performance. To address this issue, a novel clean-up tool path planning method based on the cutter center point of the fillet boundary is proposed. In this paper, the method constructs a mapping model from a point on the fillet boundary to the cutter center point. The model is solved with a point-searching algorithm so that the tool path of the fillet boundary can be calculated. Then, the region between the adjacent tool path of the fillet boundary is equivalent to circular curve for planning the clean-up tool path. Finally, this method is applied to the clean-up machining between the blade and hub of an open blisk using a computational simulation and experiment to verify its effectiveness. The results show that the maximum allowance is 0.311 mm with the conventional method, whereas it is approximately 0 mm with the proposed method, which satisfies the design requirement. Thus, the correctness and effectiveness of the proposed method are validated. [ABSTRACT FROM AUTHOR]

Tool path generation is an important task in end milling. In the existing literature, the tool path pattern/style is determined based on geometric analysis. However, there is little analysis from the viewpoint of machining dynamics/cutting force, which is more related to the final manufacturing performance. Although there are many types of tool paths (e.g., zigzag, spiral, and ISO-scallop tool path), they can be decomposed into a number of circular tool paths with various path radii and cutting angles. Firstly, by investigating chip formation mechanism, the effect of tool path radius on uncut chip thickness is obtained. Analysis shows that tool path radius has a significant influence on uncut chip thickness, thus affecting the cutting force, especially when the tool path radius is small. Then, the effect of cutting angle on cutting force distribution is analyzed. It is found that the feed direction could vary the distribution of cutting force in X and Y direction in 3-axis end milling. Based on such analysis, the cutting force prediction model considering tool path effect is established. To validate the proposed model, various experiments have been carried out based on well-designed tool paths. The comparative results show that (1) tool path has great effect on cutting force through the tool path radius, rotation direction, and start/end point, and (2) the proposed model is capable of providing accurate cutting force prediction considering tool path effect. From a physical viewpoint, the proposed method provides huge potential in automated/intelligent tool path optimization towards better surface quality via minimizing cutting force, which significantly contributes to the state of the art in tool path generation. [ABSTRACT FROM AUTHOR]

The effect of material removal on workpiece dynamics has a great influence on suppressing resonance and regenerative vibrations in milling of thin-walled structures. Although some works have been done to obtain the varying dynamics of the workpiece, many researchers take plate structures as research objects. In this paper, a method based on FEM and Taylor series is proposed to predict the time-varying dynamics of thin-walled blade structures in milling. The input data in the method is the mass and stiffness matrices of the initial workpiece and the change of the matrices caused by material removal. The initial matrices can be obtained through the FEM model established by the mechanical analysis of the eight-node shell element, and the change of the matrices can be obtained by a third-order Taylor series. In comparison with three-dimensional cube elements, the shell elements can reduce the number of degrees of freedom of the FEM model by 74%, which leads to about 9 times faster computation of the characteristic equation. Meanwhile, the proposed method eliminates the necessities of re-building and re-meshing the FEM model at each cutting position, then the computational efficiency of workpiece dynamics is improved. Finally, the result of experiments shows that the maximum prediction error of the proposed method is about 4%. [ABSTRACT FROM AUTHOR]

Constitutive model plays an important role in finite element (FE) simulation of machining, especially the Johnson-Cook (J-C) model. Although many researches have completed on modifying the J-C model to improve FE simulation of cutting, the systematic determination scheme of the added coefficients is heretofore not available. Inverse identification approach presents a promising advantage on determining constitutive coefficient compared with traditional methods, such as Split Hopkinson Pressure Bar (SHPB). In this work, a new inverse method based on finite element analysis (FEA) is developed for determining added J-C coefficients. A modified J-C constitutive model (MJC) including hyperbolic tangent failure function is introduced for machining titanium alloy Ti-6Al-4V. The determination process of the added coefficients is taken as an optimization problem, where experimental cutting forces and the added J-C coefficients are considered as the optimization objectives and design variables, respectively. An FE model of orthogonal cutting titanium alloy Ti-6Al-4V is established on the Deform-2D simulation platform. The response surface method (RSM) is subsequently used to build the mapping relation of the simulated cutting forces and the added coefficients. Then, orthogonal cutting experiments are conducted to obtain the objective cutting forces. The firefly algorithm (FA) is employed to solve the optimization model for the optimal added J-C coefficients. Verification results show that the identified constitutive model has higher accuracy on predicting cutting forces compared with other models including the origin J-C model and the Calamaz’s model. [ABSTRACT FROM AUTHOR]

The Johnson and Cook (J-C) constitutive model is widely employed in finite element analysis for machining. Currently, inverse approach shows greater superiority on identifying the J-C parameters than the traditional way, like the split Hopkinson pressure bar (SHPB) test. In this paper, a modified parallel-sided shear zone model at primary zone is presented for determining J-C parameters. Based on the assumption of nonequidistant primary zone, the distribution of velocity, strain rate, strain, and temperature is successfully predicted. Finite element simulation (FEM) is subsequently used to verify the theoretical prediction. Based on the proposed shear band model, an inverse approach is developed for determining the J-C parameters. In this method, the parameter determining process is taken as an optimization problem. The J-C parameters are considered as the design variable while the optimization objective is cutting force. In conjugation with the measured cutting forces and chip thickness, the particle swarm optimization (PSO) algorithm was introduced to solve the optimization model. The Johnson-Cook's model of nickel-base superalloy Inconel 718 is successfully identified from orthogonal cutting test. Finite element models with seven constitutive models from Deform, references and this work are established to predict cutting force. The results showed that the proposed J-C models have higher prediction precision compared with other constitutive models. With the proposed J-C models, the errors between the simulated and measured cutting forces are controlled within 7.3%. [ABSTRACT FROM AUTHOR]

This present study aimed at the wear behaviors of uncoated carbide tools and its effect on machinability in dry milling of magnesium alloy at high cutting speeds. Through the experimental observations, the dominant wear mechanisms for the carbide tool were determined, namely adhesion, abrasion, and diffusion wear. Compared to lower speed, the experimental results indicated that the higher speed could bring much better surface quality in the steady wear stage; however, the duration of this stage was shorter than the lower. Within each steady wear, surface roughness declined gradually with the increasing cutting edge radius. Meanwhile, a mass of workpiece material piled up the stagnant zone below the cutting edge on the flank face, causing the flank built-up edge formation. Besides that, the built-up edge accumulated to a certain size that is beneficial to improve the machined surface quality. In addition, the chip morphologies depended on the severity of the tool wear. The cutting tool during the early period of cutting operation predominantly produced inerratic flake chips. However, with the development of built-up edge, there was a great quantity of threadlike chips formed, as well as the plentiful needlelike chips. [ABSTRACT FROM AUTHOR]

In this study, Taguchi with grey relational analysis of experimental data was carried out to determine an optimum combination of process parameters for promoting the surface roughness and the microhardness of dry milling magnesium alloy. According to Taguchi's signal-to-noise ratio, the optimal process parameters were obtained for desired surface roughness and microhardness, respectively. Moreover, in order to achieve an improved surface quality, the optimum combination of process parameters was determined by grey relational grade. The analysis of variance for grey relational grade showed that feed rate was the most dominant factor influencing surface integrity in milling of magnesium alloy. Finally, the optimum combination of process parameters were validated by confirmation experiments that yield substantially improved multiple quality characteristics. The outcome of the confirmation experiment indicated that the Taguchi with grey relational analysis is an effective method to determine available cutting parameters for a desired surface quality for milling magnesium alloy under dry condition. [ABSTRACT FROM AUTHOR]