PSO-BP-Based Morphology Prediction Method for DED Remanufactured Deposited Layers

Abstract

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.

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

“…Compared to finite element methods, machine learning can predict the dynamics of manufacturing processes more quickly and accurately without the need for complex physical knowledge, and it has gained widespread attention and application in the field of additive manufacturing in recent years [10]. Wang Z et al [11] used a Particle Swarm Optimisation(PSO) algorithm to optimise the weights and thresholds of the Backpropagation neural network (BPNN) to build a PSO-BP prediction model, and the results showed that the model has high prediction accuracy. Hao J et al [12] used SVR, PSO-BPNN, and XGBoost models to predict the morphology of the laser cladding layer in the tilted state, and XGBoost showed better prediction performance.…”