Since shaft parts operate under harsh environments for a long time, many critical parts suffer from corrosion, wear and other problems, leading to part failure and inability to continue in service. It is imperative to repair failed parts and increase their service life. An orthogonal experimental scheme is designed to numerically simulate the process of laser cladding of Inconel 718 alloy powder on 4140 alloy structural steel based on the ANSYS simulation platform, derive the relationship equation of cladding layer thickness according to the heat balance principle, establish a finite element model, couple three modules of temperature field, stress field and fluid field, and analyze different modules to realize the monitoring of different processes of laser cladding. The optimal cladding parameters were laser power 1000 W, scanning speed 15 rad/s, spot radius 1.5 mm, thermal stress maximum value of 696 Mpa, residual stress minimum value of 281 Mpa, and the degree of influence of three factors on thermal stress maximum value: laser power > spot radius > scanning speed. The pool in the melting process appears to melt the “sharp corner” phenomenon, the internal shows a double vortex effect, with a maximum flow rate of 0.02 m/s. The solidification process shows a different shape at each stage due to the different driving forces. In this paper, multi-field-coupled numerical simulations of the laser cladding process were performed to obtain optimal cladding parameters with low residual stresses in the clad layer. The melt pool grows and expands gradually during melting, but the laser loading time is limited, and the size and shape of the melt pool are eventually fixed, and there is a vortex flowing from the center to both sides of the cross-section inside the melt pool, forming a double vortex effect. The solidification is divided into four stages to complete the transformation of the liquid phase of the melt pool to the solid phase, and the cladding layer is formed. The multi-field-coupled numerical simulation technique is used to analyze the temperature, stress and fluid fields to provide a theoretical basis for the residual stress and surface quality of the clad layer for subsequent laser cladding experiments.

Manufacturability evaluation is the effective way to shorten the period of development, optimize the manufacturing process and reduce the product costs. The manufacturability of a product depends on the processing ability of specific manufacturing resources. The building of the manufacturing resources model is the foundation for manufacturability evaluation. To better utilize the information of manufacturing resources, the hybrid algorithm of fuzzy c-means clustering algorithm (FCM) and genetic algorithm (GA) is implemented in this paper to group manufacturing resources based on manufacturing and geometric features. The information model of manufacturing resources is built by using the object-oriented method and the framework of manufacturability evaluation based on the manufacturing resources is defined. An application sample is exploited and its results are analyzed. The grouping result shows that the hybrid algorithm is reliable and effective.

Manufacturability evaluation is an effective way to shorten the development period, optimize manufacturing processes, and reduce product costs. The manufacturability of a product depends on the processing ability of specific manufacturing resources. The development of a manufacturing resources model serves as the foundation for manufacturability evaluation. To better utilize the information on manufacturing resources, this study adopted a hybrid approach by integrating the fuzzy c-means clustering algorithm and the genetic algorithm to group manufacturing resources based on manufacturing and geometric features. The information model of manufacturing resources was built by using the object-oriented method. Subsequently, the framework to evaluate manufacturing capability based on manufacturing resources was defined. Further, an application sample was exploited and its results were analyzed. The results of the subgroup showed that the hybrid algorithm was reliable and valid and helped improve the overall performance of the company chosen for this study. The proposed approach enhanced feasibility in decision-making and facilitated the management to make more informed decisions.