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

Zisheng Wang, Xingyu Jiang (), Boxue Song, Guozhe Yang, Weijun Liu, Tongming Liu, Zhijia Ni and Ren Zhang
Additional contact information
Zisheng Wang: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China
Xingyu Jiang: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China
Boxue Song: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China
Guozhe Yang: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China
Weijun Liu: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China
Tongming Liu: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China
Zhijia Ni: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China
Ren Zhang: School of Mechanical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Tiexi District, Shenyang 110870, China

Sustainability, 2023, vol. 15, issue 8, 1-15

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.

Keywords: deposited quality; technological parameters; particle swarm optimization; BP neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/8/6437/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/8/6437/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:8:p:6437-:d:1120106

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().