To guide process parameter adjustment in actual manufacturing, determining the optimal process parameter ranges for cold spray coatings is of great significance. Al6061 and SiC powders were thoroughly mixed in a powder mixer according to different mass ratios to obtain the cold spray powder. An Al6061 substrate was fixed on a fixture, and a process parameter table was designed according to the central composite design method. The remaining process parameters were set to constant values, and the cold spray process system was configured. The powder was mixed with an inert gas at a specific pressure; the resulting powder-gas mixture was heated to the set temperature with a heater and sprayed at high speed through a Laval nozzle. The powder particles collided with the substrate at high velocity, causing plastic deformation and deposition onto the substrate. After adjusting the equipment and related devices according to the process parameter table, cold spray coatings were manufactured on the substrate. The spray gun followed an "N"-shaped trajectory, spraying three times to obtain a coating approximately 1 mm thick and spraying thirteen times to obtain a coating about 5 mm thick. Metallographic specimens, bond strength specimens, and tensile strength specimens were then prepared accordingly. The microhardness of the coatings produced with different process parameters was measured with a microhardness tester. The bond strength and tensile strength were obtained through tensile tests conducted on a universal testing machine. Based on the experimental results, three quadratic polynomial models were established to describe the influence of powder feeding speed, spraying temperature, spraying pressure, and ceramic content on the microhardness, bond strength, and tensile strength of the coatings. A multi-objective optimization model was constructed to maximize these three mechanical properties. The optimal combination of process parameters was identified using the ParetoSearch algorithm in MATLAB. For cold spray coatings, bond strength is considered the most critical performance parameter. Consequently, the bond strength model was used to solve for the optimal process parameter ranges. For this purpose, any two process parameters in the bond strength model were treated as independent variables, while the other two were fixed at their optimal values obtained earlier. This approach yielded a binary function expression for bond strength in terms of any two process parameters. Response surfaces and contour maps were plotted based on these functions. The solution area and the corresponding process parameter ranges were derived from these response surfaces and contour maps. Several sets of process parameters within the optimal ranges were selected for experimental verification. The results demonstrate that the optimal combination of process parameters is vr=50 g/min, θ=527.18 ℃, p=7 MPa, CA=15%. The optimal process parameter ranges are vr∈[50, 56] g/min, θ∈[495.98, 547.14] ℃, p∈[6.62, 7] MPa, CA∈[0.15, 0.18]. The microhardness of the coatings manufactured with parameters within the optimal ranges exceeds 125HV, the bond strengths are greater than 42 MPa, and the tensile strengths are above 180 MPa. The results show that controlling the parameters within the optimal ranges can ensure the consistency of the mechanical properties of the coating, reduce the dependence on equipment control accuracy, improve the robustness and operability of the process. The optimization method proposed not only provides the theoretical optimal parameter point, but also clarifies the allowable fluctuation ranges of process parameters. The results show that controlling the parameters within the optimal ranges can ensure the consistency of the mechanical properties of the coating, reduce the dependence on equipment control accuracy, improve the robustness and operability of the process, and have stronger guiding significance for parameters adjustment in actual production.
Key words
cold spray /
process parameters /
mechanical properties /
multi-objective optimization /
optimal process parameter ranges
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Funding
Key Projects of the Aerospace Engine and Gas Turbine Basic Science Center (P2022-B-IV-011-001); National Key Research and Development Program of China (2024YFB4609600); Hubei Provincial Natural Science Foundation Innovation and Development Joint Fund Project (2023AFD037)
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