No abstract

In abrasive belt grinding process for surface of blade, elasticity, deformation and abrasive belt wear are major factors affecting the machining stability, efficiency and quality. In order to improve the grinding process and realize optimal grinding, a new controllable and flexible belt grinding mechanism is designed and assembled in a special computer numerical control grinding machine, accompanied with a constant grinding force control system. Based on the analysis of proportional valve and cylinder system in this grinding mechanism, a mathematic model of normal grinding force is constructed. Afterward, a fuzzy proportional–integral–derivative control strategy is proposed to deal with the uncertainty and nonlinearity of this system. A Simulink model of force control process is developed, and the good performance is achieved according to the simulation result. Finally, several grinding experiments for an aero-engine fan blade are carried out. The measurements show that the proposed grinding process with fuzzy proportional–integral–derivative force controller enhances the machining stability and efficiency considerably. What is more, the machining qualities, such as surface roughness, form accuracy and consistency, are improved significantly. And all the grinding results satisfy the machining requirements in the manufacturing process of blade.

Abstract3D printing, also known as additive manufacturing (AM), is evolving from a rapid prototyping tool to a pillar of the Industry 4.0 revolution and widely used in various industries, since it can quickly and efficiently create workpieces with complex structures and integrated functions. After analyzing the 3D printing principle of fused deposition modeling (FDM), this paper proposes a deformation control‐oriented optimization method for process parameters of FDM of the polylactic acid (PLA) materials, based on support vector regression (SVR) and cuckoo search (CS). First, FDM printing principle and its main process parameters were analyzed. Two key parameters, printing temperature and printing speed, were selected for research, with the maximum shrinkage deformation as the workpiece contour accuracy index. Combining Latin Hypercube Sampling (LHS) with Finite Element Analysis (FEA), finite sample sets were generated. Second, learning from the sample data, the SVR surrogate model was built to predict the workpiece contour accuracy, and the nonlinear relationship between FDM process parameters and PLA shrinkage deformation was obtained. Finally, taking the combination of printing temperature and speed as the design variable and minimizing the maximum shrinkage deformation as the optimization objective, the FDM process parameters optimization model was established, and CS was used to search for the optimal parameters combination. Experimental comparison showed that the FEA results and SVR model in this method are correct and effective, and the PLA workpiece shrinkage deformation is smaller under the optimal parameters combination, which effectively improves the shape accuracy of FDM workpieces.Highlights Combining support vector regression (SVR) and cuckoo search (CS) can optimize polymer 3D printing process parameters. Effective finite element model was built for polymer 3D printing. SVR model established by simulation data can predict workpiece deformation. The optimal process parameters were found using CS algorithm.

Due to the characteristics of thin-walled curved surface, wall thickness variations and processing cantilever fixtures, the mechanical state of the different contact positions of aircraft engine blades varies significantly during the grinding process. The different contact interactions between contact wheel and blade result in changes of material removal efficiency and surface quality. To achieve contact state control during blade grinding process, a novel flexible abrasive belt grinding device was designed and developed considering the compliance of rubber contact wheel. The significant effect of compliance parameters on grinding contact state was verified through simulation. The grinding contact pressure distribution and normal contact force at different positions in the blade width and length directions were studied, and a prediction model for the maximum contact pressure and normal contact force was established based on BP neural networks. The results showed that with the increase in contact wheel compliance, the effective contact range increased, the pressure distribution gradually became uniform, and showed a double-elliptical distribution. The maximum contact pressure was significantly reduced, with a reduction of up to 46.00%. As the grinding contact position moved towards the weak rigidity area of the blade, the contact pressure distribution became more uniform. And the normal contact force was significantly reduced, with a maximum reduction of 68.49%. The mean average percentage error (MAPE) of the prediction model was small, verifying the effectiveness of the model. The research results of this manuscript laid a foundation for achieving consistent control of blade grinding material removal rate through contact wheel compliance adjustment.