Modeling and prediction of surface roughness in belt polishing based on artificial neural network

Qi, Junde; Zhang, Dinghua; Li, Shan; Chen, Bing

doi:10.1177/0954405416683737

Cited by 26 publications

(10 citation statements)

References 23 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al [2] proposed a prediction model for the surface roughness after grinding, considering the material ductile and brittle removal mechanisms, grain protrusion conditions, material properties, and machining parameters. Qi et al [3] presented a prediction model of the surface roughness in belt polishing that comprehensively considered the characteristics of the polishing tool and workpiece. Lu et al [4] established two comprehensive mathematical models to provide a better scientific understanding of the surface texture generation in the dual-axis wheel polishing (DAWP) process.…”

Section: Introductionmentioning

confidence: 99%

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Pan

Ren

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

The relational model between machined surface roughness (MSR) and the adopted key machining parameters (KMPs) significantly influences the predictability and controllability of the machining process; therefore, it has attracted considerable attention. However, two critical problems still persist in this field. First, although most existing studies focus on the prediction model for MSR (forward model), wherein the MSR is dependent on input KMPs values, the inverse model that can calculate the KMP based on input MSR value is equally important; however, the inverse model has not been investigated as extensively as the forward model. The second issue is that most of the existing forward models are mainly established based on mechanism analysis; however, due to the complexity of most machining processes, the accuracy and generality of the model are not optimal. Therefore, this paper proposes a universal method for mathematically establishing the inverse model of the relation between the MSR and KMP. Initially, based on the response surface methodology, orthogonal experiments were designed and conducted, and the results were used to establish the forward model between the MSR and KMP. Subsequently, by combining the forward model with a self-developed genetic algorithm-based multi-objective optimization algorithm, an establishing method for inverse model between MSR and KMPs was proposed. Finally, experiments were conducted to validate the developed models. The experimental results show that for the forward model, all the 10 experimental MSR values approach the MSR values predicted by the forward model, and the average deviation was only approximately 7%. Contrarily, for the inverse model, the average deviation was only approximately 7.64%. Both these results verify the accuracy and effectiveness of the proposed models. With this method, as long as the desired processing results and constraints are given, the process parameters can be accurately derived.

show abstract

Section: Introductionmentioning

confidence: 99%

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Pan

Ren

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Due to its high flexibility and compliance, 17,18 belt grinding naturally has the mixed effect of grinding and polishing. Hence, it has become an effective machining process in industries, including aerospace, ship, nuclear power and automobile, through kinds of handheld devices, robotics 19–22 and computer numerical control (CNC). 23,24 The abrasive belt grinding process also has those advantages in rail maintenance, as demonstrated in Datong–Qinhuangdao heavy haul railway in China, which was ground by an innovative rail grinding device using abrasive belt, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Microscopic contact pressure and material removal modeling in rail grinding using abrasive belt

Fan

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Recently, the emerging rail grinding method using abrasive belt has been proposed to efficiently achieve the required geometric profile and the surface quality of the railhead. Although the abrasive features indeed have a great influence on this rail grinding process, the surface topography of abrasive belt regarding grits at the microscopic scale is neglected. In this article, a microscopic contact pressure model was developed to reveal the contact behavior of every active grit based on the digital representation of the surface topography of abrasive belt. Then a numerical model of material removal quantity was also established based on the consideration of the characteristics of abrasive grits and their interactions. Finally, the series of finite element simulations and grinding tests were successively implemented. The normal load and the surface topography of abrasive belt significantly affected the microscopic contact behavior of grits, thus confirming the proposed theoretical models of microscopic contact pressure and material removal quantity.

show abstract

“…More accurate predictions can be achieved by means of the desirability function method 25 and the artificial neural network (ANN) method. 15,17,26 However, the current research mainly focuses on the single dimension surface roughness parameters for the optimization of cutting parameters. The effect of surface defects on machined surfaces is usually not considered.…”

Section: Introductionmentioning

confidence: 99%

Investigation of surface damage and roughness for nickel-based superalloy GH4169 under hard turning processing

Pan

Fang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Machined surface condition of nickel-based superalloys has an important influence on the functional performance of the components. Proper selection of cutting parameters could improve surface finish and increase service life of parts and components. This research work bases on an experimental and statistical study of turning GH4169 nickel-based superalloy with cemented carbide tool. Surface damages like feed marks, tips, and surface tearing were discussed. The second-order polynomial model was used to describe the surface roughness response. Variance analysis was selected to eliminate the insignificant variables in the roughness model. The response surface methodology was used to investigate the combined effect of cutting parameters on two different dimensions surface roughness parameters. The optimization of cutting parameters for minimum surface roughness was obtained using desirability function method. The results demonstrate that feed rate has the most significant effect on surface roughness. High cutting speed and low feed rate result in better surface quality, but too low feed rate exacerbates built-up edge phenomenon and deteriorates surface condition. Optimal cutting parameters leading to the minimum surface roughness were highlighted.

show abstract

Modeling and prediction of surface roughness in belt polishing based on artificial neural network

Cited by 26 publications

References 23 publications

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Microscopic contact pressure and material removal modeling in rail grinding using abrasive belt

Investigation of surface damage and roughness for nickel-based superalloy GH4169 under hard turning processing

Contact Info

Product

Resources

About