Optimization of surface roughness and machining parameters for turning superalloy GH4169 under high-pressure cooling

Abstract

Superalloys are important structural materials in the aerospace and petrochemical industries. Because of their excellent fatigue and oxidation resistance, superalloys are usually used predominantly in rotor and turbine components. The requirement of good surface quality and high-precision processes has been widely concerned. High-pressure cooling is a commonly used auxiliary processing technology in the metal cutting field, which can efficiently improve the quality of the processed surface. It is of great significance to study the surface roughness of GH4169 processed by cutting under high-pressure cooling conditions. First, the single-factor and orthogonal experiment methods were used. The single factors, interaction effect, and quadratic effect of cutting parameters and cooling pressure were analyzed. Then, the multiple linear regression method was adopted to establish the prediction model for surface roughness of GH4169, and the accuracy of this model was verified. Finally, according to the prediction model, the machining parameters were optimized and verified. The results indicated that the proposed model was accurate and reliable, which could be used to optimize the machining parameters. The optimal parameter combination was achieved, with the cutting speed of 154 m/min, the feed rate of 0.10 mm/r, the cutting depth of 0.46 mm, and the cooling pressure of 52 bar. Under this parameter setting, the prediction error was 4.20%, and the surface roughness was 34.92% less than that obtained under the actually recommended machining parameters. The above results will provide theoretical guidance for the parameter optimization of cutting superalloy under high-pressure cooling conditions and quality control of the processed surface.

Extraction and Analysis of Surface Quality Characteristics of Turning Workpieces Based on Finite Element Methods

“…Orthogonal experiments are efficient and cost-effective strategies that yield accurate and reliable results with fewer experimental trials [27]. In this study, three primary cutting parameters [28] were selected as the factors (H) that have a substantial impact on the cutting surface quality: depth of cut 𝐴 𝑝 , cutting speed 𝑣 , and temperature (ambient temperature 𝑇 𝑒 and initial temperature of the workpiece 𝑇 0 ). Each factor was set to four different levels; for example, 0.5, 1.0, 1.5, and 2 mm were set as values for the factor 𝐴 𝑝 .…”

“…FES analysis not only yields information on the cutting force and cutting temperature but also enables the acquisition of surface properties of the workpiece, a factor typically disregarded in conventional research approaches. Traditionally, researchers have relied on offline measurements [12][13] [14]. However, this approach interrupts the machining process and reduces the cutting efficiency.…”

“…Nevertheless, prevailing methods predominantly involve offline measurements using digital optical microscopy systems or roughness profilers [12] [13]. Zhang et al [14] measured the surface roughness using a portable surface roughness meter TR200 and applied a normalisation method to optimise the parameter interval for the orthogonal experiment. Zhang conducted experimental investigations on a highspeed milled surface and developed a predictive model [5].…”