Grinding process is one of the most important surface generation processes in industry. The surface generated on the ground component is a combined function of grinding process parameters and grinding wheel topography. Whereas process parameters are easy to control and optimize, wheel surface topography is a random and ever-changing entity, which makes it difficult to control. It is no surprise that topography characterization of the grinding wheel still remains popular research area among various researchers. The wheel topography is made up of randomly distributed abrasive grits, bond and porosity. As the size and shape of grit varies within a mesh number, its stochastic distribution throughout the grinding wheel volume provides the randomness to the grinding process. For a given wheel, topography can be altered by either dressing the wheel or during grinding action due to wheel wear. While the former is a function of process requirement, the latter depends entirely on wheelwork-piece interaction and is a time dependent phenomenon. To ascertain the impact of different wheel topography on these micro interactions [1] , a thorough study is required to understand various parameters, which can quantitatively represent a 3dimensional (3D) surface. Techniques such as optical microscopy and tactile profilometry are available to capture and image surface data whereas image-processing platforms can be used to evaluate these parameters.
Grinding, owing to its versatility, is used for a wide range of operations, which involve high rates of material removal as in creep feed grinding and as low as in ultra-precision grinding. Different types of grinding operations are used in industries but
Grinding process is one of the most important surface generation processes in industry. The surface generated on the ground component is a combined function of grinding process parameters and grinding wheel topography. Whereas process parameters are easy to control and optimize, wheel surface topography is a random and ever-changing entity, which makes it difficult to control. It is no surprise that topography characterization of the grinding wheel still remains popular research area among various researchers. The wheel topography is made up of randomly distributed abrasive grits, bond and porosity. As the size and shape of grit varies within a mesh number, its stochastic distribution throughout the grinding wheel volume provides the randomness to the grinding process. For a given wheel, topography can be altered by either dressing the wheel or during grinding action due to wheel wear. While the former is a function of process requirement, the latter depends entirely on wheelwork-piece interaction and is a time dependent phenomenon. To ascertain the impact of different wheel topography on these micro interactions [1] , a thorough study is required to understand various parameters, which can quantitatively represent a 3dimensional (3D) surface. Techniques such as optical microscopy and tactile profilometry are available to capture and image surface data whereas image-processing platforms can be used to evaluate these parameters.
Grinding, owing to its versatility, is used for a wide range of operations, which involve high rates of material removal as in creep feed grinding and as low as in ultra-precision grinding. Different types of grinding operations are used in industries but 
Grinding process is one of the most important surface generation processes in industry. The surface generated on the ground component is a combined function of grinding process parameters and grinding wheel topography. Whereas process parameters are easy to control and optimize, wheel surface topography is a random and ever-changing entity, which makes it difficult to control. It is no surprise that topography characterization of the grinding wheel still remains popular research area among various researchers. The wheel topography is made up of randomly distributed abrasive grits, bond and porosity. As the size and shape of grit varies within a mesh number, its stochastic distribution throughout the grinding wheel volume provides the randomness to the grinding process. For a given wheel, topography can be altered by either dressing the wheel or during grinding action due to wheel wear. While the former is a function of process requirement, the latter depends entirely on wheelwork-piece interaction and is a time dependent phenomenon. To ascertain the impact of different wheel topography on these micro interactions [1] , a thorough study is required to understand various parameters, which can quantitatively represent a 3dimensional (3D) surface. Techniques such as optical microscopy and tactile profilometry are available to capture and image surface data whereas image-processing platforms can be used to evaluate these parameters.
Grinding, owing to its versatility, is used for a wide range of operations, which involve high rates of material removal as in creep feed grinding and as low as in ultra-precision grinding. Different types of grinding operations are used in industries but 
Grinding is extensively used as surface generation process in finishing operations, which makes it imperative that the generated surface adheres closely to strict quality check norms failing which the component will be rejected incurring a loss to the industry. To ensure the intended surface quality falls within tolerances, the grinding wheel plays an important role as an abrasive tool. The finished surface is a direct outcome of grinding wheel topography and its various microscopic interactions with the surface creating a compelling need for in-depth understanding. Study and characterization of grinding wheel topography in the present paper aim to understand the behavior of forces in the contact area. Due to stochastic abrasive grain distribution and quasi-static surface wear, 3-dimensional parameters hold a better physical significance in explaining the nature of microscopic interactions and their impact on the behavior of forces. Experiments are performed in a manner to wear out grinding wheels of the same specification with varying number of passes in an attempt to highlight the influence of varying topography on tribological interactions in the grind zone. The variation in topography at a specific interval of time is captured using a confocal laser scanning microscope and further processed for surface characterization whereas forces are captured using a dynamometer. SEM micrographs are presented to understand the type of wear on the wheel surface. The outcome of present paper enables to understand and report the surface interactions quantified in terms of cutting, ploughing and rubbing whereas surface parameters are characterized as per ISO 25178. The reasoning behind variations during surface interactions was discussed and interpretations were reported.
Grinding process is one of the most important surface generation processes in industry. The surface generated on the ground component is a combined function of grinding process parameters and grinding wheel topography. Whereas process parameters are easy to control and optimize, wheel surface topography is a random and ever-changing entity, which makes it difficult to control. It is no surprise that topography characterization of the grinding wheel still remains popular research area among various researchers. The wheel topography is made up of randomly distributed abrasive grits, bond and porosity. As the size and shape of grit varies within a mesh number, its stochastic distribution throughout the grinding wheel volume provides the randomness to the grinding process. For a given wheel, topography can be altered by either dressing the wheel or during grinding action due to wheel wear. While the former is a function of process requirement, the latter depends entirely on wheelwork-piece interaction and is a time dependent phenomenon. To ascertain the impact of different wheel topography on these micro interactions [1] , a thorough study is required to understand various parameters, which can quantitatively represent a 3dimensional (3D) surface. Techniques such as optical microscopy and tactile profilometry are available to capture and image surface data whereas image-processing platforms can be used to evaluate these parameters.
Grinding, owing to its versatility, is used for a wide range of operations, which involve high rates of material removal as in creep feed grinding and as low as in ultra-precision grinding. Different types of grinding operations are used in industries but 48th SME North American Manufacturing Research Conference, NAMRC 48 (Cancelled due to COVID-19) the mode of topography alteration remains via wheel wear. Grinding wheel wears out in distinct ways that depend entirely upon the type of wheel composition and work-piece. Attritious wear has long been established for occurrence of wear flats on wheel surface. These wear flats linearly increase in number with increase in grinding forces [2] . Since it was well understood that change in process parameters would simultaneously affect wheel wear, the need to understand mechanics of material removal by abrasive grains developed. Grinding energy is reported as sum of cutting energy due to chip formation and sliding energy due to rubbing between bond-work-piece and chip-work-piece. Wear of grinding wheel due to fracture of grain and bond is a direct function of grinding forces and bond volume in the wheel. Harder grade wheels reportedly have more grain fracture due to higher total force involved [3] .
As the wheel undergoes quasi-static wear, topography varies simultaneously. Since it is impossible to capture topography after every pass, it was imaged after considerable number of grind passes. This process was done in two stages: (i) capturing of topography using appropriate imaging system and (ii) characterizing the surface topography using standard parameter definitions. ISO 25178 categorizes 3D surface parameters under Amplitude, Spatial, Functional and Hybrid parameters which were studied and reported for their physical significance by Stout et.al (1994) [4, 5] . Various researchers have used a variety of techniques for extracting topography viz. stylus-based contact 2D and, 3D profilometry and non-contact imaging systems [4] [5] [6] [7] [8] [9] [10] [11] [12] . Surface roughness parameter A comprehensive study of grinding wheel includes all the elements that contribute to its behavioral aspects respective to its performance as shown in figure 1. Present research paper carried out investigation of variation in tangential and normal force and their ratio with correspondence to variation in grinding wheel surface topography in different worn out conditions. This helps in understanding how different surface conditions will engage abrasive grains in various interactions with work-piece. These interactions comprise ploughing, rubbing and cutting on work-piece surface by abrasive grain.
Each of these interactions have varying effects on forces and surface generated on work-piece. In the end, tribological aspects of grinding wheel used are correlated to various wheel conditions and their impact on the work surface in terms of surface finish.
During grinding, there are number of interactions taking place between various surfaces and are collectively responsible for quality of surface generated on the work-piece and the amount of forces generated. Earlier works in investigating these forces finds a linear relation between normal and tangential force [13] . Malkin et.al. (1971) resolved vertical and horizontal grinding forces into two components, cutting and sliding for each as given in equation (1, 2) . Subsequently, vertical sliding force was replaced with average contact pressure and real area of contact whereas horizontal sliding force was replaced by average contact pressure, real area of contact and sliding friction coefficient [2] .
With this perspective, surface interactions were defined and reported in accordance with experimental data. It is notable here that surface interactions were limited to cutting and sliding and forces were defined in a similar fashion.
The surface interactions behavior has been explained in an extensive manner by Subramanian et.al (2015) [1] . These surface interactions are documented as microscopic interactions where they are categorized under different classes: (i) abrasive-work-piece, (ii) bond-work-piece, (iii) chip-bond and (iv) chip-work-piece interactions. With cutting/ machining and abrasion as the principle of interaction, primarily 6 such interactions are specified as mentioned in figure 3 [1] . These play a vital role in surface generation for the work. Despite the fact that machining/ cutting is the desired interaction among all for material removal, sliding performs surface modifications on work and is more desirable in producing better surface finish. Ploughing occurs when abrasive penetration in work surface is not enough for cutting and is neither shallow for sliding to occur. Experiments may be designed in a manner to focus attention for study of particular interaction.
Grinding wheel surface topography comprises of abrasive grains, bond and porosity. This surface undergoes modification during grinding process due to wheel wear and debris inclusion which also known as wheel loading. To study this topography, two interconnected parallels are required. First is capturing of wheel surface and second deals with choosing the right parameters that can define the change in topography with physical surface modification interactions . The two parallels are interconnected, as the surface parameter extraction will be effective only when the surface data of topography is captured / imaged with good resolution. Tactile measurement being time consuming [11] , A schematic of LEXT OLS 4000 Confocal laser scanning microscopy used to capture surface data in this study is shown in figure 4 . The images were captured using objective lens of 5x magnification and 20x of laser 3D imag e magnification. At this magnification, an area of 1280 mm x 1280 mm was scanned with a z-resolution of 10 nm.
Although smaller wheel samples are needed for imaging, the method is effective in providing 3-dimensional surface data in lesser amount of time. The surface parameters defined in International Standard ISO 25178 are discussed at length by Stout et al. [4, 5] . Franco and Sinatora (2015) discussed at length towards understanding of functional parameter Spk [12] . Importance and its effectiveness in understanding wheel wear and its cutting ability has been reported [14] . Calculation of this parameter from figure. 5 is given by Franco and Sinatora [12] .
Spk represents the height of triangle whose area equals the area under hill region. Here normalized height is used to represent Y-axis. For Abbot-Firestone curve without normalized height, Sq has to be divided to get normalized reduced peak height. This area, estimated by the equation (5), represents reduced peak height material volume that signifies abrasive material volume taking part in grinding action. Summit density, Sds, is a spatial parameter that has been used by researchers to characterize grinding performance [8] . In this study, standard 5% pruning is used for Sds. 1 Here, normalized height values are used along y-axes. In case bearing curve is plotted against raw height data obtained from topography extraction, eq. 5 needs to be divided with Sq, which is the RMS areal roughness value as shown in eq. 6.
Strategy presented in figure 6 is adapted to ensure that wheels of same specification undergoing similar initial topography generation by dressing process behaves different once topography starts to vary.
A CNC surface grinder was used for conducting this study. Piezo based Kistler made dynamometer was employed for force measurement. Dynamometer was connected to data acquisition via charge amplifier. Figure. 7 represents the setup layout. Table 1 . Process setting used in surface grinding.
Medium dressing condition as specified in table 1 were employed to all 3 alumina grinding wheels for normalizing the topography and respective wheel size was reduced fro m 180mm to 179mm. Mild steel was used as work piece material for grinding experiments. Only 10mm out of total 13mm of grinding width was employed for grinding. Grinding was done on three mild steel specimens using three alumina wheels with total depth of cut 5mm, 10mm, and 15mm respectively. Forces were measured using kistler dynamometer after every 50 passes with a sampling frequency of 300Hz and signal was drift compensated.
Since the size of wheel is quite large to image it using confocal laser scanning microscope, smaller pieces were prepared from all the three wheels . As a means to measure the exact topography taking part in micro-interactions, the cut sections were not cleaned to remove entrapped debris from it. To remove the noise present in the images, basic jagged filter available in lext software was used. An area of a 1280 mm x 1280 mm was scanned over the cut sections of grinding wheel. Within one specimen, multiple sampling was done with appropriate spacing between them [7] . 3 samples for dressed as well as worn out region were taken for each wheel and values were averaged.
These images were subjected to a series of operations, shown in figure 8(a) , for estimating 3D surface parameters. Objective was to extract the signal wavelength corresponding to the abrasive grain dimension. Surface roughness and was filtered using a cut off wavelength of 0.15 mm and so were the higher wavelength corresponding to form of the wheel by using a 2 nd degree polynomial in form removal operator on Mountainmap platform. The parameters were extracted fro m these images using mountains map software. The final 3dimension reconstruction of the extracted surface was done and is represented in figure 8(b) .
Image acquired from confocal laser microscope contains topography as well as intensity layer for the grinding wheel surface. Intensity layer was processed to obtain to wear flat for respective wheel surfaces and area under these wear flats were obtained.
As already discussed in section 2.2, bearing ratio is an extremely helpful tool in comparing different wheel surfaces subjected to various grinding conditions. Figure. 9 shows the bearing curve for grinding wheel captured after 500, 1000 and 1500 passes. As seen in curve, there is a considerable decrease in hill area (region shown within red circle) is seen across the trend. Even though reduced peak height parameter (Spk) has increased, it is physically useful to compare only when normalized with Sq. Since the hill area has decreased, this serves as an indication in increase of wear flats on the wheel signifying a worn-out wheel. The 3D reconstructed wheel surface can also be seen against respective bearing curves giving a better idea on increased amount of wear flats with decreased Sds (table 2) . A decrease in Sds and hill area is recorded from the observation and calculation that has resulted in considerable increase in specific cutting force. Since the wheel has worn out, more energy is required to cut the work material as considerable part of total energy is being consumed in ploughing and sliding too. With a finer grade wheel, sliding modifies the work surface in a manner to achieve better surface finish. After 500 P asses After 1000 P asses After 1500 P asses Reduction in hill area can be physically understood as an increase in wear flats on the surface of grinding wheel. As the wheel tends to wear out, the bearing ratio will become gradually smoother which results in decreased hill area. The wheel may wear out due to various mechanisms owing to type of wheel and process settings used during the operation. Grinding wheels used in present study have hardness specification of Grade K, which is a hard wheel. The abrasive grit is strongly held in bond matrix and thus Attritious and grit fracture are the major modes of wheel wear. Attritious wear gives rise to wear flats on the wheel surface that result in wheel becoming blunt. These wear flats are optically captured and the area under wear flats is calculated for the 3 wheels in both dressed and worn out condition. The percentage area under wear flats is shown in figure 10 . An increasing trend on worn surfaces from 500 passes to 1500 passes can be seen which corresponds to increased attritious wear of the wheels. Corresponding surface parameters are also shown in figure 10 . These parameters substantiate the wear condition of the respective wheels. An example of the comparison of wear flats obtained on dressed and worn out surface for Wheel 3 is shown in figure 11 .
Normal and tangential forces measured were used to calculate the force ratio. The data is plotted in figure 12 for all 4 different condition of wheel viz. dressed wheel, after 500, 1000 and 1500 passes respectively. As discussed already, Ft/Fn provides the necessary insight about the type of interaction occurring in the grind zone. In practice, it is near impossible to ascertain if sliding is occurring in between abrasive-work or bond-work or chip-bond or chip-work. Although for an abrasive-work sliding interaction, tangential force is 1/6 th of normal force as the penetration depth for abrasive is shallow [1] . Such interactions are required for surface modification is high precision grinding where the grain size is much smaller accounting for a fine grinding wheel that will not be possible with a coarser wheel. In this study, it is observed that frequency of such interactions is very less and rare as the grinding wheel in use is of 60 mesh which is quite coarse and is mostly used for high material removal or rough grinding in industries. This study entails the division of grind zone interactions to cutting/ machining, ploughing and rubbing. Figure 12 shows the demarcation that is followed for further analysis of friction coefficient. All the interactions occurring at or above Ft/Fn=1 signifies cutting action by abrasive. With 0.8 > Ft/Fn > 1, ploughing action is occurring as the penetration of abrasive in work surface is not enough to cut out the deformed material. Finally, all the occurrences for Ft/Fn<0.8 can be ascribed to sliding interaction which include all the sliding action between chip-bond, bond-work and chip-work.
As seen in figure 13 , since the wheel is put to grinding after dressing, many abrasive grains will be loosely held in the bond which will account for increased rubbing action (67.21%) of the wheel in the initial stages of grinding even though it has higher Sds (4.89 mm -2 ) than wheel at other stages. This is the duration where grinding wheel moves toward stabilizing fro m the effects of dressing and this is why obtaining a consis tent performance on work is not possible at this stage. After 500 grind passes, wheel has recovered from dressing effect and cutting action is much higher (12.17%) than at any other stage that can be understood from the fact that since abrasives are no longer loosely held, at given penetration depth, they are more likely to either cut or plough the work surface. As the wheel finishes 1000 grind passes, it is now occupied by considerable wear flats as evident from reduced Sds (1.83 mm -2 ), increased Sk (35.34µm) and increase wear flat area (4.88%). This results in lesser cutting action and significant increase in sliding behavior. The trend continues after 1500 grind passes, as the cutting interaction is least (2.7%) with most occurrences of sliding interactions (84.68%). With increased amount of wear flats on wheel surface (decrease in hill area), it is no longer able to remove material by cutting through the work and sliding occurs which interferes with finish on the work surface.
There are strong indications of force ratio portraying a trend in understanding and comparing the tribological interactions which is corroborated further by parameters obtained fro m surface characterization of grinding wheel. After comparing various worn out stages of different wheels for their microinteractions, same wheel is analyzed. An interesting trend of force ratio behaviour is observed in figure 14 displaying the variations of micro-interaction s between dressed and worn out surface of same wheel. Figure   14 (a) for wheel 1 shows that fresh dressed wheel surface with higher summit density parameter, and reduced peak height parameter is unable to cut through the work material whereas after 500 passes, the topography generated can perform better cutting action due to wheel wear and friability of grains while show-casing reduced amount of rubbing. Figure 15 (a) displays a SEM micrograph of wheel 1 surface after 500 passes where instances of grit fracture can be seen suggesting that wheel is still undergoing self-sharpening. Furthermore, by analysing figure 14 (b) it is evident that both dressed as well as worn out surface are tribologically behaving in the same manner. They, however, have marked differences in their topography parameters as seen by summit density comparison and yet both the surfaces are interacting with the work surface in similar fashion. More instances of attritious wear can be seen in SEM micrograph from figure 15(b) . While looking at figure 14(c) , both dressed as well as worn out surface of wheel 3 are still behaving in similar manner, just like wheel 2. Taking a comparative look at SEM micrograph of worn surface of wheel 3 in figure 15(c) , attritious, grit fracture as well as gain pull out can be seen. In addition, there are indications of debris entrapment in the voids suggesting high wear and loading of wheel. Both wheel 2 and wheel 3 worn surfaces have increased wear flat area and decreased summit density indicative of a blunt wheel. As the dressed surface topography and wear parameters are not similar to their worn surface counterpart, it can be safely suggested that even with different topography characteristics, wheel surfaces might behave quite differently than expected.
Grinding experiments were carried out to demonstrate the impact of change in grinding wheel performance with variation in wheel topography. 3 alumina wheels were used for dry surface grinding of mild steel sample whereas forces were measured during operation using dynamometer. Grindin g wheels were cut in to smaller sections and were imaged using laser scanning confocal microscope. Images obtained were processed using Mountainmap software for extracting 3D surface parameters and wear flat area. Tribological changes in wheel work interaction was studied using force ratio while directly correlating with wheel topography. It has been found that two different grinding wheel surfaces may have different topography and still they might perform in a similar tribological fashion. It is imperative to understand that the method of topography generation greatly affects the tribological characteristics of the grinding wheel. A dressed surface that has undergone mechanical shock during dressing operation is observed to behave the same way as a worn-out surface owing to inability of cutting capability while having stark difference in their topographical characterization. The present study is an attempt to explain the change in behavior of microscopic interactions as a direct result of change in topography of wheel surface that occurs due to wheel wear.