journal article·2025-08-01

Remanufacturing oriented statistical optimization and performance analysis of laser cladding Ni60 coating on 316L piston rods

Lilan Liu, Fan Yang, Jiayi Wang, Shen Wang, Boyu Guo

Journal of Laser Applications· Volume 37· Issue 3
Publisher page

Abstract

In order to explore the remanufacturing of 316L piston rods, theoretical and experimental researches on laser cladding the Ni60 alloy on 316L stainless steel are conducted. A multiobjective optimization procedure is proposed to achieve appropriate process parameters based on the experimental results and the response surface method. The regression prediction models are built up to obtain the nonlinear relations between the main process parameters, as well as coating geometric dimensions and forming qualities. The laser power, scanning speed, powder feeding rate, and overlap rate are set as the input factors, and coating’s width, height, dilution rate, and hardness are set as the target responses. Through statistical analysis, the influences of the process parameters and their coupling effects on the geometric dimension and forming quality of Ni60 coatings are also studied. Then, process parameter optimization is conducted using the multiresponse desirability function method to laser cladding a given coating, and a verification experiment is carried out. Furthermore, the microstructure and mechanical performance of the Ni60 coating are investigated. The results show that there exists a dense metallurgical bonding between the substrate and the coating, and the microstructure of the coating is mainly composed of an austenite phase, a FeNi3 phase, and a (Cr, Fe)7C3 phase. The microhardness of the Ni60 coating is about three times that of 316L stainless steel, and its wear rate is significantly lower than that of 316L stainless steel. Finally, based on the optimized process parameters, repairing of the worn surface of a 316L piston rod is successfully realized.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · High Entropy Alloys Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology , Xi’an 710048,

Funding

National Natural Science Foundation of China

References (41)

Show source-provided references
1. Laser cladding: Repairing and manufacturing metal parts and tools10.1117/12.463704 ↗
2. Laser cladding and laser assisted direct manufacturing10.1016/j.surfcoat.2008.04.033 ↗
3. Advances in surface laser cladding remanufacturing of shaft parts10.21062/mft.2023.037 ↗
4. Research and development on application of laser cladding technology in ship building industry10.3969/j.issn.1674-165X.2023.12.002 ↗
5. Effect of SS 420 and Stellite 6 laser cladding powders on mechanical properties of 1080 Cr grade railway steel
6. Laser cladding remanufacturing of aircraft landing gear based on 30CrMnSiNi2A steel
7. Control design for a class of multivariable nonlinear system with uncertain control direction: A laser cladding case study10.1016/j.ejcon.2021.04.007 ↗
8. Experimental investigation and statistical optimization of laser surface cladding parameters10.1007/s00170-014-6338-x ↗
9. Statistical modeling in the laser cladding process of Inconel 625 via linear regression and response surface method10.2351/7.0000964 ↗
10. Optimization of laser-cladded SS316L/IN625 functionally graded material deposited on a copper substrate for boiler pipe heat exchanger applications10.1007/s00170-023-12764-5 ↗
11. Laser cladding of Inconel 713 LC with Stellite 31 powder: Statistical modeling and optimization10.1088/1555-6611/ac1920 ↗
12. Effect of process parameters on the geometry of single-track deposits of Inconel 718 onto AISI4140 using laser cladding10.1007/s00170-024-14276-2 ↗
13. Statistical modeling and optimization of clad geometry in laser cladding of Amdry 365 on Hastelloy X superalloy with response surface methodology
14. Bead morphology prediction of coaxial laser cladding on inclined substrate using machine learning10.1016/j.jmapro.2023.05.032 ↗
15. Prediction of geometric characteristics of laser cladding layer based on least squares support vector regression and crested porcupine optimization
16. Prediction of morphology size of laser-melted Ni-based coating10.13289/j.issn.1009-6264.2021-0208 ↗
17. Prediction of single track clad quality in laser metal deposition using dissimilar materials: Comparison of machine learning-based approaches10.2351/7.0001108 ↗
18. Process parameter optimization when preparing Ti(C, N) ceramic coatings using laser cladding based on a neural network and quantum-behaved particle swarm optimization algorithm10.3390/app10186331 ↗
19. Prediction method for multi-track layer cladding layer morphology based on GWO-RFR10.13289/j.issn.1009-6264.2023-0289 ↗
20. Process parameters optimization of laser cladding for HT200 with 316L coating based on response surface method10.1007/s11595-024-3027-y ↗
21. Process parameter optimization of Fe-based coating on ductile iron using laser cladding with grey related analysis10.1016/j.optlastec.2024.111906 ↗
22. Development of automated impact system for modal analysis of micro-end mill10.1016/j.mfglet.2023.08.118 ↗
23. Multi-objective optimization of key process parameters in laser cladding Stellite12 cobalt-based alloy powder
24. Prediction modeling, process optimization, and property analysis of laser cladding Ni60 alloy on 56NiCrMoV7 steel10.2351/7.0001663 ↗
25. Deep learning-driven precision control of dilution rate in multi-pass laser cladding: Experiment and simulation10.1007/s00170-023-11732-3 ↗
26. Impact of heat input on the cladding of super austenitic stainless steel through the gas tungsten arc welding process on ASTM A516 grade 70 steel10.3390/coatings14111356 ↗
27. Predictive modeling and optimization of layer-cladded Ti-Al-Nb-Zr high-entropy alloys using machine learning10.3390/coatings14101319 ↗
28. Optimization of laser cladding powder ratio and process parameters based on MOGWO algorithm10.1016/j.mtcomm.2024.110605 ↗
29. Influence of Ti element on the microstructure and crack of modulated WC-strengthened Ni-based alloy coatings by laser cladding10.1016/j.mtcomm.2024.110541 ↗
30. Ni-P SiC composite coatings on piston rings by plate and bumper technique and its tribological properties10.1177/02670844241293510 ↗
31. Study on electrodeposition of CoNiW coating on 1045 steel as piston ring materials and its wear resistance performance10.1016/j.ijoes.2023.01.012 ↗
32. Effect of SiC and TiC content on microstructure and wear behavior of Ni-based composite coating manufactured by laser cladding on Ti–6Al–4V10.1016/j.wear.2024.205431 ↗
33. Mesoscopic characteristics, microstructure evolution, friction mechanisms, and corrosion behavior of Ni60-SiCp coatings fabricated by laser-based directed energy deposition10.1016/j.triboint.2024.109877 ↗
34. Influence of laser cladding process parameters on crack and thickness of Ni6010.7510/jgjs.issn.1001-806.2021.06.019 ↗
35. Microstructure and properties of high-speed laser cladding Ni60 coating on surface of high manganese steel10.12073/j.hj.20230515001 ↗
36. Microstructure and properties of Ni60 alloy coating prepared by electro-magnetic compound field assisted laser cladding10.1016/j.matchemphys.2022.126678 ↗
37. Modeling the predictive values of ultimate tensile strength in welded joint by response surface methodology10.1016/j.matpr.2021.02.798 ↗
38. Multi-objective optimization of laser cladding process parameters for Q345B steel10.1016/j.mtcomm.2024.108679 ↗
39. Statistical-experimental modeling of the effect of process parameters on geometric characteristics of laser cladding of Stellite 6 on SS316 using second-order regression10.1016/j.jmrt.2023.12.201 ↗
40. Turning of AISI 9260 using MAC nano coolant: Multi-objective optimization and modeling of cutting parameters using MOGA-RSM10.1007/s12008-024-02168-4 ↗
41. Optimizing hydrodesulfurization of naphtha using NiMo/graphene catalyst10.1016/j.jiec.2024.01.066 ↗