1. Introduction
Surface roughness is one of the most crucial evaluation parameters for surface integrity, and its magnitude plays a significant role in determining the wear resistance, fatigue performance, stress corrosion performance, etc., of components [
1]. Titanium alloys, as typical high-strength alloy materials, possess a range of advantages such as excellent corrosion resistance, low density, stable damping properties, and a high specific strength. Consequently, they have found extensive applications in the aerospace industry [
2]. However, in the process of abrasive belt grinding of titanium alloy surfaces, plastic deformation, work hardening, and the formation of microcracks are prone to occur under the squeezing action of abrasive grains, which can negatively impact the surface quality. This phenomenon is closely related to grinding process parameters [
3]. Scholars from around the world have conducted numerous studies on the surface integrity of titanium alloy processing.
Khellouli et al. [
4] researched the wear mechanism of abrasive belt grinding and the elastic contact between the contact wheel and the workpiece and analysed the effect of process parameters on the surface roughness of the workpiece. Bigerelle et al. [
5] established a model for the wear mechanism of abrasive belt grinding and investigated the effect of process parameters on the surface roughness. Jie Li et al. [
6] established a prediction model of surface roughness based on BP neural networks and used genetic and particle swarm algorithms to optimise the process parameters to obtain the best combination of process parameters. Huang Jiefeng et al. [
7] proposed a feature integration-based grinding surface roughness measurement method. It effectively solves the problem of weak feature information of grinding surface roughness, which is difficult to measure, and the model has high detection accuracy under different lighting environments. Pan Yuhang et al. [
8] developed a real-time surface roughness prediction model based on multi-sensor signal fusion. Lu Enhui et al. [
9] proposed a grinding surface roughness measurement method based on a combination of the full reference (FR) image quality algorithm visual saliency induced index (VSI) and a back propagation (BP) neural network. Zhang Guojun et al. [
10] proposed a roughness measurement method based on generative adversarial and BP neural networks. The features in the image are automatically learned by GAN, eliminating the independent feature extraction step, and the measurement accuracy is improved by a BP neural network. Fang Runji et al. [
11] proposed that the GC index has a strong correlation with the roughness of the grinding surface, and its regression fitting prediction model has a high prediction accuracy; in addition, the correlation between the GC index and roughness is relatively stable under different light intensities. Liu Yin et al. [
12] conducted a large number of comparative tests on the surface roughness of the bottom surface of zirconium-based bulk metallic glass milled grooves using different machining materials, milling cutters with different coatings, milling cutters with different geometric parameters, and different machining conditions. The results show that zirconium-based bulk metallic glass has a good milling performance. Liang Xiaohu et al. [
13] evaluated the effect of roughness on SAW dispersion and attenuation and reduction of bias in the assessment of machined surface damage. In order to investigate the surface integrity of titanium alloys at different roughness levels, Guiyun Jiang et al. [
14] repeatedly ground the surfaces with the same and different types of abrasive belt models. The results showed that at roughness Ra levels of 0.4 μm to 0.2 μm, the compressive residual stresses decreased with increasing linear velocity and large surface morphological defects formed. At a roughness Ra of 0.2 μm or less, grinding improved the surface morphology, the compressive residual stress increased with increasing feed rate, and the surface hardness decreased with increasing linear velocity. Yun Huang et al. [
15] studied the grinding degree (the effect of feed rate, linear speed, and initial grinding pressure on the grinding force) and the influence of the grinding force on the law of material removal and surface integrity.
The research discussed in these studies has focused on various aspects of abrasive belt grinding, particularly regarding the understanding of wear mechanisms, the influence of process parameters on surface roughness, and the development of innovative measurement and prediction methods. This collective research contributes to a deeper understanding of abrasive belt grinding processes and their implications. However, most of the above studies are aimed at analysing the effect of process parameters on surface roughness, and there are fewer adjustment ranges for the parameters of the belt grinding process that can be referred to.
Shao Biao and their colleagues [
16] determined the best distribution method for the inerting system by utilising statistical theory to investigate the distribution of nitrogen-rich gases. They also established a comprehensive methodology for evaluating the oxygen concentration’s reduction rate characteristics and uniformity based on the entropy weighted improved TOPSIS theory. Zhong R et al. [
17] used the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) method and expert scoring method to evaluate the management of energy efficiency in hydropower plants by scoring quantitative and qualitative indicators. Xianglin Zhan et al. [
18] determined the weight coefficients for each index by combining the hierarchical analysis method and entropy weight method. They established a virtual simulation environment to calculate the quantitative indicators and utilised the improved TOPSIS method to evaluate the maintainability of various design schemes for aero engines in a comprehensive manner. To develop a safety risk assessment model for the construction of assembly buildings, Guo Zhen [
19] incorporated the AHP and TOPSIS methods. The housing construction project was subjected to TOPSIS analysis for testing purposes. In order to further study the dynamics of water resources, and for the traditional TOPSIS evaluation of water resource carrying capacity problems, He Li [
20] found that the use of D-S evidence theory can effectively reduce the loss of data variability, improve the integration of the objective results and accuracy advantages, and improve the TOPSIS method for water resource evaluation. Liang Yaodong et al. [
21] devised an extensive evaluation model utilising the Hierarchical Analysis Method (AHP) and the TOPSIS method. The model employed AHP-TOPSIS and selected a total of 11 indicators from technological, economical, and safety aspects. These indicators were used to perform a comprehensive evaluation and determine the most suitable mining method. Rui Han and Xiaoxia Guo [
22] used the TOPSIS model combined with the entropy weighting method to construct a vegetable quality evaluation model for different strains of vegetables. Zhu Jianzhen and Cui Xiwen [
23] employed the entropy weight TOPSIS model to analyse and assess the overall intensity of the oceans and its dynamic variation trend using China’s ocean economy and related statistics. Li Yang [
24] analysed the commonly used digital imaging technology for machining surface roughness detection to provide a reference. Yang Deyu et al. [
25] used an orthogonal test method and response surface method to study the cutting force and surface roughness of coated cemented carbide tools after milling and machining 1J50 soft magnetic alloys; the influence laws of cutting parameters on cutting force and surface roughness were obtained through orthogonal analysis. Xiao Guijian et al. [
26] conducted an experimental study on the surface finish quality of titanium alloys before and after abrasive belt wear using surface integrity as an evaluation index; they revealed the influence of abrasive belt wear on the surface roughness, residual stress, and surface hardness of grinding TC17 and the underlying mechanism. By adjusting the tilt direction of the grinding belt wheel to alter the normal contact force between the blade and wheel, Ren Hongzhang and Li Jing [
27] maintained a constant load on the grinding belt wheel via the load system throughout the machining process. This achieved collaborative control of the grinding process that was independent of both the robotic and load systems. Song Weiwei et al. [
28] conducted a series of experimental studies exploring the effect of various process parameters on the surface roughness of TC17 titanium alloys. Their investigation examined the effects of feed rate, abrasive belt line speed, and downward pressure on the surface roughness. Yuan Lujie [
29] analysed the characteristics of belt grinding and the margin distribution of aircraft blades to address the challenge of quantitatively controlling the grinding pressure of aircraft blade belt grinding equipment. Tian Fengjie and Si Dasheng [
30] established a surface profile model of abrasive belt grinding workpieces through the research and analysis of the trajectory of abrasive belt abrasive grains and verified the theoretical analysis by establishing a mathematical model of surface roughness regression and blade grinding tests using the one-factor test method. In order to investigate the material removal mechanism of metal workpieces in abrasive belt grinding processing, Wang Hang and Luo Minfeng [
31] established a geometric model after simplifying the abrasive belt grinding system and set up the model parameters and motions to establish a discrete element dynamic simulation of abrasive belt grinding processing. Dong Haosheng et al. [
32] carried out orthogonal experiments on screw rotor belt grinding using axial feeding of the workpiece, established a model for predicting the surface roughness value of the screw rotor after belt grinding, and predicted and analysed the surface quality of the workpiece after grinding. Duan Jihao et al. [
33] revealed the influence laws of different contact wheel suppleness levels on blade surface grinding contact pressure distribution, contact normal force, blade processing deformation, and other states, combined with the contact wheel force–deformation curves. Hu Changhao [
34] carried out the analysis of the removal mechanism of material elastic contact grinding, combined with the Hertzian contact theory of abrasive belt grinding, and further deduced a mathematical model for the material multi-body Coulomb friction factor. Kong Xianjun et al. [
35] conducted an orthogonal test on TC11 titanium alloys to investigate the impact of each turning parameter on cutting temperature, cutting force, and surface roughness. Liu Cen et al. [
36] applied mathematical statistics and probability theory to establish a direct method to analyse the degree of influence of processing methods or changes in the working conditions on the interval values of the distribution parameters of normally distributed random variables at a certain two-sided confidence level. Xia Wang [
37] proposed a three-parameter interval grey number multi-attribute decision-making method based on an improved TOPSIS model for multi-attribute decision-making problems.
As a result, various studies have been conducted on different aspects of belt grinding, including evaluation methods, effects on surface quality, and material removal mechanisms. Researchers utilised various methods such as statistical analysis, mathematical modelling, and experimental testing to study different aspects of the grinding process and its impact on the workpiece surface.
However, currently, there is a relatively limited amount of literature available on the identification of key processing parameters in the blade grinding process. Moreover, the practical achievements in this area are not very prominent. Therefore, this study aimed to fill this gap by focusing on the current state of blade processing and its requirements. We aimed to identify the key processing parameters in the process of belt grinding blades. By integrating theoretical research with practical processing, we aimed to reduce the reliance on manual decision making, guide the selection of processing parameters, and ultimately improve the efficiency and quality of blade belt grinding processes.
The main content of this research project is as follows: Firstly, based on the analysis of the process parameters for belt grinding, we used a balanced weight approach to identify the process parameters that have a significant impact on the surface roughness of titanium alloys when subjected to belt grinding. In addition, we designed an orthogonal experiment based on these process parameters and established a mathematical model to estimate surface roughness. Secondly, we examined the effect of the process parameters within specific ranges, determining both stable and unstable domains for these parameters. Finally, we proposed a method for determining the optimum range of process parameters and ascertained the optimal range for these parameters.
This study is of great theoretical and practical importance to engineers and manufacturers for the following reasons.
- (1)
In traditional belt grinding processes, the selection of grinding process parameters typically relies on the experience and skill level of the operators, making it difficult to ensure the quality of the finished workpieces. Therefore, this study conducted an in-depth investigation of the process parameters involved in belt grinding blades through theoretical analysis, with the aim of identifying critical process parameters and reducing the reliance on manual decision making.
- (2)
By studying the effect of machining parameters on surface quality and categorising these parameter combinations into different intervals, it becomes possible to select machining parameters more quickly in actual production. This will help reduce preparation time, minimise the defect rate, and increase the utilisation efficiency of grinding machines.
- (3)
By establishing a surface roughness prediction model that clarifies the relationship between surface roughness and processing parameters, it facilitates the setting of grinding process parameters. This is important for the rapid and rational selection of grinding process parameters.
In summary, controlling surface roughness not only helps enhance product quality and performance, but it also contributes to cost reduction, prolonging product lifespan, and reducing resource wastage. Therefore, it holds significant importance for industrial and engineering applications.