Surface roughness modeling for grinding of Silicon Carbide ceramics considering co-existence of brittleness and ductility

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Highlights

  • For brittle materials, surface roughness modeling should consider the co-existing of machining-induced ductility and brittleness.

  • Rayleigh distribution function and critical chip thickness model are used to model the chip thickness for a single grit and ductile grinding respectively.

  • The ductile surface roughness is much smaller and more stable than the brittle mode.

  • The ductility-dominant grinding, under a higher wheel speed or lower chip thickness, can help to improve the surface roughness with a lower level of damage.

Abstract

Ground surface roughness is always hard to be predicted due to the random distribution of abrasive grits and various materials properties, especially for brittle materials. In grinding of brittle materials, because of the brittle and hard nature, the material will be removed in both ductile and brittle modes, which make the modeling of surface roughness difficult. Therefore, this paper is intended to propose a new surface roughness model for brittle materials by considering the co-existing of machining-induced ductility and brittleness. A Rayleigh distribution function will be used to model the randomness of abrasive grits by assuming that the chip thickness for a single grit conforms to this distribution. In the meanwhile, the critical chip thickness model, considering both the materials properties and process parameters, will be adopted to divide the ductile and brittle grinding for a single grit. Thus, the probability for ductile mode grinding can be obtained through integral calculation of the Rayleigh distribution function. The grinding damage model from the indentation fracture mechanics is used to model the brittle surface roughness. The surface roughness and chip thickness model was calibrated and validated through a series of experiments on grinding of Silicon Carbide. The results show that the model predictions have a good match with the experimental results and the ductile surface roughness is much smaller than the brittle mode. Therefore, it can be concluded that the surface roughness modeling of brittle materials could benefit from considering the co-existence of brittleness and ductility governed by the machining kinematics, abrasive topology and process parameters, as well as the grinding induced surface damages. Moreover, the ductility-dominant grinding, under a higher wheel speed or lower chip thickness, can help to improve the surface roughness with a lower level of damage.

Introduction

The grinding process has become one of the most widely used precision finishing methods in achieving desired machining quality to meet high quality part requirements [1], [2], [3]. Ground surface roughness is usually the most important evaluation indicator to assess ground surface quality. The ability to precisely predict the machining surface roughness is very helpful in reducing the cost and improving productivity. The factors that affect the surface quality are not only the grinding wheel topology, workpiece, lubrication conditions and operation parameters, also the nature of materials itself [4]. Compared with the metallic materials, the surface roughness modeling for brittle materials, like Silicon Carbide ceramics, is much more complicated for the co-existing nature of both brittleness and ductility in grinding process [5]. For the brittleness, the materials are generally removed in the form of fracture cracks and the ground surface has a lot of pits and caves. While for the ductility, the materials are more tended to be removed through plastic deformation and plastic striations are shown on the ground surface. Moreover, the extremely hard and brittle nature also makes brittle materials difficult to be machined, lowering the surface fracture crack size and surface crack proportion would substantially reduce the surface roughness. A higher material removal rate and good surface finish are the key issues concerned. Therefore, the ability to accurately predict the surface roughness for brittle materials needs further in-depth investigations.

In order to understand the grinding damage mechanism, Esmaeilzare et al. [6] and Agarwal et al. [7] investigated the grinding induced surface/subsurface damages of ceramics under different process parameters. However, microcracks are inevitably produced in grinding of brittle materials. Thus, critical chip thickness model for ductile grinding [8] (below 10% surface damage) was put forward on the basis of the material removal energy. The material will be removed in ductile mode grinding when the chip thickness is below this critical chip thickness. Otherwise it would be brittle mode grinding. In this critical model, the critical value for ductile grinding is independent from the process parameters and only correlated with material mechanical properties. Nevertheless, this does not match the real situation in machining process for the variation of process parameters will definitely change the material property when the grinding wheel interacts with the workpiece [9]. Thus, a new chip thickness model considering both of the material property and process parameters was proposed by Wu et al. [10]. Therefore, the separation between ductility and brittleness should consider both of the material property and process parameters.

A complete prediction of the surface roughness is very complicated for the lack of comprehensive consideration of all the factors that may affect the surface topology [11]. Yet, extensive research work has been carried out to predict the surface topology in grinding. Empirical and analytical methods are the two most frequently used methods to develop the surface roughness models [12]. In the empirical methods, the surface roughness was modeled through an arithmetic relationship with different factors, such as minimum chip thickness [13], the machining kinematics [14], the cutting edge radius [14], dressing [15], vibration effect [16] and etc. Although these empirical models generally have the advantage of minimum efforts to be established, but they cannot be used into different machining conditions and machining methods. Hence the application scope of these empirical models is very limited. In the analytical methods, the chip thickness was first assumed to conform to a probability distribution by Hecker et al. [17] for the stochastic nature of abrasive grits. Then, different surface roughness models [18], [19], [20] based on the probability distribution of chip thickness was developed in grinding of brittle materials. However, the co-existing nature of brittleness and ductility in grinding was not considered in these works. In the subsequent work, Shao et al. [21] modeled the surface roughness for ceramics on the basis of indentation fracture mechanics. In the study of brittle materials grinding, the indentation fracture mechanics approach likens abrasive-workpiece interactions for grinding of ceramics to small-scale indentation events [22]. There exist generally two principal crack systems [23], the lateral and median cracks. It is believed that the lateral cracks and the crack depth have a correlation with the surface roughness [21]. However, there is no detailed work in theoretical surface roughness modeling of brittle materials considering both of the ductility and brittleness.

From the above literature review, it is obvious that prediction of surface roughness for brittle materials needs more comprehensive consideration in theoretical analysis of their ductility and brittleness. This study aims to propose a new surface roughness model for brittle materials by considering both the brittleness and ductility. In this work, a newly proposed critical chip thickness model will be adopted to divide the ductile mode grinding from brittle mode grinding and further calculate the proportion of different grinding mode. The grinding damage models based on indentation fracture mechanics will also be used to model the surface roughness in brittle mode grinding. Thus, the correlation between the grinding crack size and surface roughness could be established. Finally, experiments on diamond grinding of Silicon Carbide will be conducted to validate the proposed surface roughness model. This paper will provide guidance for design and manufacturing of brittle materials in grinding process.

Section snippets

Rayleigh chip thickness model

As a precision machining method, grinding is featured by the stochastic nature of the abrasive grits, which makes the same stochastic feature for the chip thickness. Rayleigh distribution for grinding wheel surface was first statistically proposed by Younis et al. [24]. In the subsequent study, Hecker et al. [20] further established and validated Rayleigh chip thickness model. Raphael et al. [25] modeled the chip thickness (grain protrusion height) as a normal distribution, which showed a much

General description of damage formation in grinding processes

In the grinding of brittle materials, the interaction process between the grit and workpiece can be regarded as a scratch process [31]. From the indentation fracture mechanics, two different crack systems will be produced when the grit scratches across the workpiece, the lateral crack and median crack [22]. The geometrical description of damage formation can be found in Fig. 2. It can be seen from the description in Fig. 2 that the surface damage caused by the lateral cracks will be the main

General description

In grinding of brittle materials, the material removal process shows a co-existence of both brittleness and ductility. Thus, the modeling for the surface roughness can be divided into two different parts. When the material is removed in brittle mode, the material will be removed in the form of fracture cracks and the surface roughness should be modeled with consideration of the ground surface lateral fracture crack length and depth based on the indentation mechanics approach. However, when the

Grinding experiments setup

The grinding experiments are conducted to calibrate the predictive surface roughness model for brittle materials. The detailed grinding experiments were given in Fig. 5. Fig. 5(b) is the part enlarged picture of Fig. 5(a). The MGKS1332/H CNC cylindrical grinder in Fig. 5 can reach up to 8000 RPM with a 400 mm diamond wheel, which is 167 m/s of linear wheel velocity. Reaction-sintered silicon carbide workpiece, with the elastic modulus of 350 GPa, hardness 23 GPa, static fracture toughness

Model validation

In order to validate the established surface roughness model in Eq. (24) and chip thickness model in Eq. (1), a series of grinding experiments (6 sets) are developed. However, numerous microcracks were produced in grinding of SiC ceramics, which makes the chip thickness hard to be measured. Therefore, in order to validate the chip thickness model, the validation of ductile grinding probability can be used as a substitution. From the Rayleigh chip thickness model in Eq. (1), the chip thickness h

Sensitivity analysis of process parameters

In order to analyze the effect of process parameters on surface roughness, grinding experiments in Table 2 are depicted in Figs. 7–9. Fig. 7 gives the wheel speed effect on the ground surface roughness. It can be found from Fig. 7 that the increase of wheel speed will cause a positive decrease of the surface roughness value, from 0.533 µm at wheel speed Vs of 60 m/s, 0.256 µm at 100 m/s to 0.185 µm at 140 m/s. Moreover, the brittle surface roughness shows the same downward trend. While the

Conclusions

In grinding process of brittle materials, it is inevitably to produce fracture cracks that will certainly lead to a decrease of surface quality. In this paper, a new predictive model for surface roughness in grinding of brittle materials was established by considering co-existing of brittleness and ductility. Considering about the randomness of grinding wheel, the chip thickness for a single grit was assumed to conform to a Rayleigh distribution function. A new critical chip thickness

Acknowledgments

This paper is supported by the National Major Projects(2013ZX04001-141), National Natural Science Foundation (51675096) and Morris M. Bryan Jr. Professorship for Advanced Manufacturing Systems. The authors wish to record their gratitude to their generous supports.

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