Elsevier

Wear

Volumes 382–383, 15 July 2017, Pages 71-77
Wear

A method for prediction of active grits count in surface grinding

https://doi.org/10.1016/j.wear.2017.04.012Get rights and content

Highlights

  • A method was developed for predicting active grits count in surface grinding.

  • Rabinowicz’s abrasive wear model was used in active grits estimation.

  • Effect of depth of cut, grit size, and workpiece hardness considered simultaneously.

  • Effect of dressing operation also considered in active cutting edges prediction.

Abstract

The ability to estimate grinding process responses, like the surface roughness, specific energy, cutting forces and temperature, is essential to understand and optimize abrasive grinding. Several models to predict the process behavior with various input parameters have been published. In all of them, the number of active grits per unit area is an important parameter; however, this parameter has been given limited attention. Due to the difficulties associated with the accurate prediction and measurement of grits per unit area, very few grinding models have considered this factor, and most treat it as a constant. The active grits count depends on the depth of cut, the type of wheel, and the nature of the workpiece material. In contrast, the cutting edges count depends mainly on wheel-dressing conditions. A method has been developed to predict the active grits count by assuming a stochastic distribution of abrasive grits and their physical interaction with the workpiece based on a presumed abrasive wear mechanism. In addition, the count of active cutting edges has been included by using dressing kinematics. The variation in the number of active grits count with the depth of cut, grit size, dressing conditions and work material nature was used to validate the efficacy of this newly-proposed, predictive method.

Introduction

In a metal cutting process, the knowledge about the mechanism of material removal and surface generation is essential for the optimizing the process. In the case of machining, known geometry and position of the cutting tool over the workpiece surface define the material removal mechanism. Advanced simulation [1] and experimental techniques [2] are available to give an insight into the chip formation mechanism in machining operations. However, in the case of grinding, the study of material removal mechanism is complicated due to several factors associated with the grits such as grits stochastic distribution on a grinding wheel, undefined geometry, and unknown number of cutting edges. In spite of the complexities, some physical analogous studies like indentation and scratch tests have been adopted to understand the abrasive grit and work material behavior, and material removal mechanism during the grinding [3], [4].

In grinding, the sequential motion of individual grits associated with micron level depth of cut leads to a macroscopic material removal. As a result, the grinding performance can only be described by individual grits behavior. In this aspect, the most important parameter which describes the grinding performance is the active cutting edges count on wheel surface. However, due to the abrasive grits stochastic distribution on a grinding wheel, an exact determination of this number is not possible. Therefore, individual grits position with wheel surface, number, and shape of the abrasive grits are analyzed stochastically and related to the process kinematics. In order to compare a variety of grinding processes (surface or cylindrical), researchers defined general and comparable process parameters such as geometric contact length, chip length, and the chip thickness.

Fig. 1 demonstrates the path of cutting edge over a work surface. The path the center travels between the two engagements of the wheel depends on the feed movement and the time required. The cutting edge engagement and the resulting uncut chip parameters such as uncut chip thickness (hcu) and uncut chip length (hch) depend on the cutting edge density (C) on the grinding wheel and other parameters as given in Eq. (1).hcu=k[1C]α[vwvc]β[aedeq]γwhere ‘vw’ is the speed of the workpiece, ‘vc’ is the speed of the wheel, ‘ae’ is the depth of cut, and ‘deq’ is equivalent wheel diameter. On the basis of uncut chip parameters values, a theoretical assessment has been made on the grinding process.

A thorough literature search shows that several researchers revealed the significance of uncut chip thickness and depth of cut in relating the process responses. Several equations had been proposed for analytical prediction of process responses in terms of uncut chip thickness. A basic relation between the uncut chip thickness and process responses such as grinding force, specific energy, surface roughness and the temperature is given below [6]:

  • Force per grit (fg)hcu1.7

  • Temperature (Tmax)ae.vs.C

  • Specific energy (ec)1hcun

  • Surface roughness (Rt)hcu4/3ae1/3

From the above relations, it can be observed that the uncut chip thickness and hence cutting edges count on a grinding wheel is an important parameter in estimating the process output. All the abrasive grits protruding from the bond and passing through the grinding zone can be called as static grits, and the static grits may have one or more than one cutting edge depend on the wheel micro-topography, which can be decided by the dressing operation. The nomenclature Sstat, Nstat, and Cstat represent the number of static grits per unit length, per unit surface area, and per unit volume respectively. However, active grits (grits which are participating in the cutting process) count is different from the static grits count, which mainly depends on the grinding conditions such as process type, parameters, and the nature of work material. Hence, the number and density of the active grits is smaller than the static grits.

There are several methods available to measure the cutting edges count (Static and active) experimentally. Based on the method of measurement technique, they can be classified into three types such as pre-processing method, in-process method, and post-process method (Table 1).

The method in which information about only static grits can be obtained is called as a static method. In static methods all abrasive grits on the surface of the grinding tool are considered, there is no distinction, whether a cutting edge of the grinding process is actively involved or not in the cutting process. Static methods are independent of the grinding conditions. Whereas in dynamic methods, the number of actual abrasive grits (active grits) engaged with workpiece are considered. The active cutting edge number is the totality of cutting edges involved in the cutting process [6].

A complete review on the importance of the number of active grits [7], analytical models [8], [9], [10] for its calculation and experimental techniques [7], [10], [11] to measure the active grits are available in the literature. Some of the commonly used analytical models for predicting the cutting edges density have been given below [12], [13], [14].C=6βVgπdg2C=(6Vgπdg3)2/3C=4fdg2(4π/3Vg)2/3where ‘Vg’ is the volume fraction of grits on the wheel, ‘dg’ is the diameter of abrasive grit, ‘f’ is the fraction of abrasive particles that actively cut in grinding, and ‘β’ is a constant related to the holding strength of the abrasive grits in the wheel and has a value from 0 to 1 [9].

It can be observed in Eqs. (2), (3), (4) that the researchers had made an attempt to predict the cutting edges count as static grits count only. Unfortunately, none of the available models consider the abrasive grit size, grits concentration, depth of cut and work material nature characteristics simultaneously which has an influence on active grits count as shown in Fig. 2. Hence, in this work, an attempt has been made to develop a method for determining the static grits and active grits count stochastically by considering the effect of wheel nature (grit size and concentration), depth of cut given during the grinding process and work material nature. Moreover, the effect dressing condition has also considered on cutting edges count.

Section snippets

Stochastic approach for determining grit count

The method developed in the present investigation should be treated as an early attempt by considering the grinding wheel grit size, wheel structure, and depth of cut and work material nature to determine the active grits count. Hence, keeping in the view of the complex nature of the grinding process, several assumptions have been made in this analysis:

  • i.

    The analysis is based on the statistical distribution (normal) of the abrasive grits height on the grinding wheel surface; the same distribution

Conclusions

A new method for predicting active grits was presented in this study, and the influence of process parameters, including depth of cut, and particularly the abrasives grit size, abrasives concentration and the hardness of work material was considered simultaneously. Moreover, incorporation of dressing effect also taken care in predicting the cutting edges count. This has considerably improved the model predictability and making it more realistic. Predicted results corresponded with the available

References (22)

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