Directed energy deposition is a typical laser remanufacturing technology, which can effectively repair failed parts and extend their service life, and has been widely used in aerospace, metallurgy, energy and other high-end equipment key parts remanufacturing. However, the repair quality and performance of the repaired parts have been limited by the morphological and quality control problems of the process because of the formation mechanism and process of the deposition. The main reason is that the coupling of multiple process parameters makes the deposited layer morphology and surface properties difficult to be accurately predicted, which makes it difficult to regulate the process. Thus, the deposited layer forming mechanism and morphological properties of directed energy deposition were systematically analyzed, the height and width of multilayer deposition layers were taken as prediction targets, and a PSO-BP-based model for predicting the morphology of directed energy deposited layers was settled. The weights and thresholds of Back Propagation (BP) neural networks were optimized using a Particle Swarm Optimization (PSO) algorithm, the predicted values of deposited layer morphology for different process parameters were obtained, and the problem of low accuracy of deposited layer morphology prediction due to slow convergence and poor uniformity of the solution set of traditional optimization models was addressed. Remanufacturing experiments were conducted, and the experimental results showed that the deposited layer morphology prediction model proposed in this paper has a high prediction accuracy, with an average prediction error of 1.329% for the layer height and 0.442% for the layer width. The research of the paper provided an effective way to control the morphology and properties of the directed energy deposition process. A valuable contribution is made to the field of laser remanufacturing technology, and significant implications are held for various industries such as aerospace, metallurgy, and energy.
To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop scheduling for aerospace manufacturing enterprises is proposed. This paper considers the process flexibility of aerospace complex components comprehensively, and an integrated optimization model for the process planning and production scheduling of aerospace complex components is established with the optimization objectives of achieving a minimum makespan, machining time and machining cost. A honey-bee mating optimization algorithm (HBMO) combined with the greedy algorithm was proposed to solve the model. Then, it formulated a four-layer encoding method based on a feature-processing sequence, processing method, and machine tool, a tool was designed, and five worker bee cultivation strategies were designed to effectively solve the problems of infeasible solutions and local optimization when a queen bee mated to a drone. Finally, taking the complex component parts of an aerospace enterprise as an example, the integrated optimization of process planning and workshop scheduling is carried out. The results demonstrate that the proposed model and algorithm can effectively shorten the makespan and machining time, and reduce the machining cost.
To cope with the problems of frequent mold changes, long production cycles and serious logistics crossings in workshop of aerospace enterprise. First, a manufacturing cell layout planning method based on the feature bit code domain method and K-Means++ is proposed to realize the accurate division of manufacturing cells. Then, a multiobjective optimization method of dynamic reconstruction layout based on improved fruit fly optimization algorithm (IFOA) is proposed to solve the reconstruction layout optimization of the production workshop problem with the optimization objectives of logistics cost, reconstruction cost, loss cost, and cell integrated area. Finally, plant simulation software is applied to simulate the workshop layout before and after optimization. The simulation results show that the logistics cost of the workshop cell layout after optimization is reduced by 8.7%, the utilization rate of the workshop area is improved by 5.2%, and the value-added rate of products is increased by 6.6%, which verifies the effectiveness and feasibility of the proposed model and method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.