Modeling surface roughness for polishing process based on abrasive cutting and probability theory

The surface roughness is a variable used to describe the quality of polished surface. This article presents a surface roughness model based on abrasive cutting and probability theory, which considers the effects of abrasive grain shape, grit and distribution feature, pressure on surface roughness. The abrasive grain protrusion heights are thought to close to Gaussian distribution, and then the relationship between the indentation depth and the pressure based on Hertz contact theory is obtained. Surface roughness prediction model is established by calculating indentation depth of the abrasive grains on workpiece surface. The maximum surface profile height (R_y) is approximately equal to the maximum indentation depth of the abrasive grain. The arithmetic average surface roughness (R_a) is equal to the average indentation depth of the abrasive grain. The effects of process parameters such as pressure and grit on R_y and R_a were simulated and analyzed in detail.     

ABRASIVE FLOW MACHINING WITH ADDITIONAL CENTRIFUGAL FORCE APPLIED TO THE MEDIA

Abrasive flow machining (AFM) is one of the important non-traditional metal finishing technologies which was introduced during the late 1960s. The process has found applications in a wide range of fields such as aerospace, defence, surgical and tool manufacturing industries. Recently, an effort has been made towards the performance improvement of this process by applying centrifugal force on the abrasive media with the use of a rotating centrifugal force generating (CFG) rod introduced in the workpiece passage. The results have been encouraging. The present paper discusses the results of changing the parameters like shape and rotational speed of CFG rod, extrusion pressure, number of process cycles and abrasive grit size. The results indicate that all the input variables have significant effect on the response parameters, which for the present study were taken as material removal and surface roughness. An analytical model is proposed for the velocity and the angle at which abrasive particles attack the workpiece surface in the process.     

ABRASIVE FLOW MACHINING WITH ADDITIONAL CENTRIFUGAL FORCE APPLIED TO THE MEDIA

Abrasive flow machining (AFM) is one of the important non-traditional metal finishing technologies which was introduced during the late 1960s. The process has found applications in a wide range of fields such as aerospace, defence, surgical and tool manufacturing industries. Recently, an effort has been made towards the performance improvement of this process by applying centrifugal force on the abrasive media with the use of a rotating centrifugal force generating (CFG) rod introduced in the workpiece passage. The results have been encouraging. The present paper discusses the results of changing the parameters like shape and rotational speed of CFG rod, extrusion pressure, number of process cycles and abrasive grit size. The results indicate that all the input variables have significant effect on the response parameters, which for the present study were taken as material removal and surface roughness. An analytical model is proposed for the velocity and the angle at which abrasive particles attack the workpiece surface in the process.     

Morphology and integrity of surfaces finished by centrifugal force assisted abrasive flow machining

Centrifugal force assisted abrasive flow machining (CFAAFM) process has recently been tried as a hybrid machining process with the aim towards performance improvement of assisted abrasive flow machining (AFM) process by applying centrifugal force on the abrasive-laden media with a rotating centrifugal force generating (CFG) rod introduced in the workpiece passage. In the CFAAFM process, the surfaces are generated by erosion from random attack of abrasive grains. CFAAFMed surfaces are unidirectional but random in nature due to transient media flow conditions. In the present paper, surface morphology, surface micro-hardness, X-ray analysis, and surface compressive residual stress produced in the finished surface layer by CFAAFM process is described. The CFAAFM process was performed under different rotational speeds of CFG rod while keeping other input parameters constant during the experiments. The increase in surface microhardness and compressive residual stress of the workpiece with an increase in the rotational speed of CFG rod is attributed to the work-hardening surface that possibly occurs due to ‘throw’ of abrasive particles upon specimen surface.     

Simulation of surface generated during abrasive flow finishing of Al/SiCp-MMC using neural networks

Abrasive flow machining (AFM) is a multivariable finishing process which finds its use in difficult to finish surfaces on difficult to finish materials. Near accurate prediction of generated surface by this process could be very useful for the practicing engineers. Conventionally, regression models are used for such prediction. This paper presents the use of artificial neural networks (ANN) for modeling and simulation of response characteristics during AFM process in finishing of Al/SiC_p metal matrix composites (MMCs) components. A generalized back-propagation neural network with five inputs, four outputs, and one hidden layer is designed. Based upon the experimental data of the effects of AFM process parameters, e.g., abrasive mesh size, number of finishing cycles, extrusion pressure, percentage of abrasive concentration, and media viscosity grade, on performance characteristics, e.g., arithmetic mean value of surface roughness (R _a, micrometers), maximum peak–valley surface roughness height (R _t, micrometers), improvement in R _a (i.e., ΔR _a), and improvement in R _t (i.e., ΔR _t), the networks are trained for finishing of Al/SiC_p-MMC cylindrical components. ANN models are compared with multivariable regression analysis models, and their prediction accuracy is experimentally validated.     

Parameters optimization on surface roughness improvement of Zerodur optical glass using an innovative rotary abrasive fluid multi-jet polishing process

With the advance of contemporary technology, high precision surface finishing techniques for optical glasses are of great concern and developing to meet the requirements of the effective industrialized processes. Not only the used tools but also process parameters have great influence on the surface roughness improvements. In this paper, surface roughness improvement of Zerodur optical glass using an innovative rotary abrasive fluid multi-jet polishing process has been presented. For the same purpose, a tool for executing ultra precision polishing was designed and manufactured. Taguchi's experimental approach, an L₁₈ orthogonal array was employed to obtain the optimal process parameters. ANOVA analysis has also been carried out to determine the significant factors. It was observed that about a 98.33% improvement on surface roughness from (R_a) 0.360 μm to (R_a) 0.006 μm has been achieved. The experimental results show that a surface finished achieved can satisfy the requirements for optical-quality surface (R_a < 12 nm). In addition, the influence of significant factors on surface roughness improvement has been discussed in this study.     

Abrasive flow machining applied to plastic gear matrix polishing

The topic of this paper is the application of abrasive flow machining (AFM) to gear tool inserts polishing. Polished surface on plastic gear teeth improves surface geometry stability, and it increases the lifespan, which was proved on the gear testing rig. Experiments have shown that it is an efficient alternative to the hand polishing procedure. Besides significant cost and processing time savings, AFM generates constant surface quality. The achieved roughness is homogeneous on the entire machined surface; it is reduced from R _a?=?0.68 μm to R _a?=?0.08 μm in 120 s. At the same time, the tooth geometry profile is not damaged. The first time, surface polishing should be done at request because of individually manufactured tool inserts. Processing parameters depend on the type of the abrasive machine, the polishing paste and part geometry. Computer-aided abrasive flow analyses and practical experiments assist in setting optimum AFM process parameters. The paper presents a working set of parameters and a detailed report on machined surface measurement data. On the base of better understanding of AFM process, the surface roughness prediction model and thickness of removed material model was setup. It has high accuracy and reliability for specific applications. The use of plastic gears for various applications is widespread; the presented process improvement is an important innovation for injection molding tools manufacturers.     

Prediction of surface roughness during abrasive flow machining

An analytical model is proposed to simulate and predict the surface roughness for different machining conditions in abrasive flow machining (AFM). The kinematic analysis is used to model the interaction between grain and workpiece. Fundamental AFM parameters, such as the grain size, grain concentration, active grain density, grain spacing, forces on the grain, initial topography, and initial surface finish (R _a value) of the workpiece are used to describe the grain-workpiece interaction. The AFM process is studied under a systematic variation of grain size, grain concentration and extrusion pressure with initial surface finish of the workpiece. Simulation results show that the proposed model gives results that are consistent with experimental results.     

Experimental investigation and mechanism of material removal in nano finishing of MMCs using abrasive flow finishing (AFF) process

Aluminum alloy and its composites appear to have a good future as a candidate material for engineering and structural components. Finishing of these materials is a big challenge as they are heterogeneous in nature having abrasive particles randomly distributed and oriented in the matrix material. Metal matrix composite (MMC-aluminum alloy and its reinforcement with SiC) workpieces were initially ground to a surface roughness value in the range of 0.6 ± 0.1 μm, and later were finished to the R_a value of 0.25 ± 0.05 μm by using Abrasive Flow Finishing (AFF) process. The effects of different process parameters, such as extrusion pressure, number of cycles and viscosity of the medium were studied on a change in average surface roughness (ΔR_a) and material removal. The relationship between extrusion pressure and ΔR_a shows an optimum at about 6 MPa. In the same way, the relationship between weight percentage of processing oil (plasticizer) and ΔR_a also shows an optimum at 10 wt%. Further, an increase in workpiece hardness requires more number of cycles to achieve the same level of improvement in ΔR_a. Material removal also increases with an increase in extrusion pressure and number of cycles while it decreases with an increase in processing oil content in the medium. It is also concluded that the mechanism of finishing and material removal in case of alloys is different from that in case of MMC.     

Surface roughness measurement in turning carbon steel AISI 1045 using wiper inserts

Surface roughness of the workpiece is an important parameter in machining technology. Wiper inserts have emerged as a significantly class of cutting tools, which are increasingly being utilized in last years. This study considers the influence of the wiper inserts when compared with conventional inserts on the surface roughness obtained in turning. Experimental studies were carried out for the carbon steel AISI 1045 because of its great application in manufacturing industry. Surface roughness is represented by different amplitude parameters (R_a, R_zD, R_3z, R_q, R_t, R_a/R_q, R_q/R_t, R_a/R_t). With wiper inserts and high feed rate it is possible to obtain machined surfaces with R_a < 0.8 μm (micron). Consequently it is possible to get surface quality in workpiece of mechanics precision without cylindrical grinding operations.     

Surface roughness and roundness of bearing raceway machined by floating abrasive polishing and their effects on bearing’s running noise

As the two most important indexes of bearing raceway, surface roughness and roundness have significant influence on bearing noise. Some researchers have carried out studies in this field, however, reason and extent of the influence of raceway surface geometric characteristics on bearing running noise are not perfectly clear up to now. In this paper, the raceway of 6309 type bearing＇s inner and outer ring is machined by floating abrasive polishing adopting soft abrasive pad. Surface roughness parameters, arithmetical mean deviation of the profile Ra, the point height of irregularities Rz, maximum height of the profile Rmax and roundness fof raceways, are measured before and after machining, and the change rules of the measured results are studied. The study results show that the floating abrasive polishing can reduce the surface geometric errors of bearing raceway evidently. The roundness error is reduced by 25%, Rm~x value is reduced by 35.5%, Rz value is reduced by 22% and Ra value is reduced by 5%. By analyzing the change of the geometrical parameters and the shape difference of the raceway before and after machining, it is found that the floating abrasive polishing method can affect the roundness error mainly by modifying the local deviation of the raceway＇s surface profile. Bearings with different raceway surface geometrical parameter value are assembled and the running noise is tested. The test results show that Ra has a little, Rmax and Rz have a measurable, and the roundness error has a significant influence on the running noise. From the viewpoint of controlling bearings＇ running noise, raceway roundness error should be strictly controlled, and for the surface roughness parameters, R,n~x and Rz should be mainly controlled. This paper proposes an effective method to obtain the low noise bearing by machining the raceway with floating abrasive polishing after super finishing.     

Surface roughness and material removal rate of lapping process on ceramics

Lapping is a widely used surface finishing process for ceramics. An experimental investigation is conducted into the lapping of alumina, Ni−Zn ferrite and sodium silicate glass using SiC abrasive to study the effect of process parameters, such as abrasive particle size, lapping pressure, and abrasive concentration, on the surface roughness and material removal rate during lapping. A simple model is developed based on the indentation fracture and abrasive particle distribution in the slurry to explain various aspects of the lapping process. The model provides predictions for the surface roughness,R _a andR _t , on the machined surface and rough estimation for the material removal rate during lapping. Comparison of the predictions with the experimental measurements reveals same order of magnitude accuracy.     

Surface finish of bulk metallic glass using sequential abrasive jet polishing and annealing processes

Surface finish plays an important role in product quality due to its direct effects on product appearance. Hence, improvement of the surface finish is an essential requirement in industrial products. In an attempt to improve the surface finish of bulk metallic glass (BMG) material, several common methods have been used, such as milling, grinding, and lapping. However, the BMG surface finish has not yet been significantly improved by using these methods. Therefore, this paper proposes sequential abrasive jet polishing (AJP) and annealing processes that can considerably improve the BMG surface finish. In addition, this paper also takes into account optimal parameters for the AJP and annealing processes based on the Taguchi’s L ₁₈ and L ₈ orthogonal array experimental results, respectively. The experimental results show that using optimal AJP parameters, the surface roughness (R _a) of the ground specimens can be significantly improved from 0.675 to 0.016 μm. After the AJP process, the surface roughness (R _a) of the polished specimens can be further improved from 5.7 to 2 nm within an area of 5?×?5 μm by using optimal annealing parameters.     

A novel polishing method for single-crystal silicon using the cavitation rotary abrasive flow

Traditional low-pressure abrasive flow polishing can produce highly smooth surfaces, but the efficiency of this method is too low for polishing of hard-brittle materials parts. This paper proposes a novel cavitation rotary abrasive flow polishing (CRAFP) method. The energy generated from the cavitation bubble collapse is used to increase the kinetic energy of the abrasive particles in the low-pressure abrasive flow and the motion randomness of the abrasive particles near the wall; thereby, the efficiency and quality of low-pressure abrasive flow polishing are improved. The CRAFP mechanism was first introduced, and then the characteristics of the CRAFP process were investigated using computational fluid dynamics (CFD)-based abrasive flow simulation. Subsequently, a single-crystal silicon wafer polishing test was carried outperformed to verify the validity of the CRAFP method. The polishing results were compared with those of the traditional low-pressure abrasive flow polishing method. After 8 h of polishing using the CRAFP method and the traditional low-pressure abrasive flow polishing method, the surface roughness of the workpiece decreased to7.87 nm and 10.53 nm, respectively. Furthermore, by starting at similar initial roughness values, the polishing time required to reduce the roughness to 12 nm was 3.5 h and 6 h, respectively. The experimental results demonstrated that CRAFP can satisfy the surface requirements of single-crystal silicon (R_a < 12 nm) and exhibit high polishing efficiency and good quality.     

Experimental investigations and modeling of drill bit-guided abrasive flow finishing (DBG-AFF) process

Abrasive flow finishing (AFF) is one of the widely used advanced finishing processes in which a small quantity of work material is removed by flowing semisolid abrasive-laden putty over the workpiece surface to be finished. AFF is popular for finishing and deburring of difficult-to-access areas. This process is also used for radiusing, producing compressive residual stresses, and removal of recast layer. In order to enhance productivity of the process, several modifications in AFF process are being tried. In this paper, a concept of rotating the medium along its axis has been introduced to achieve higher rate of finishing and material removal. This process is termed as drill bit-guided abrasive flow finishing (DBG-AFF) process. In order to provide random motion to the abrasives in the medium and to cause frequent reshuffling of the medium, the medium is pushed through a helical fluted drill, which is placed in the finishing zone. The experiments are carried out to compare AFF and DBG-AFF processes with AISI 1040 and AISI 4340 as workpiece materials. The performance of DBG-AFF as compared to AFF is encouraging, specifically with reference to percentage change in average surface roughness (% ΔR _a) and amount of material removed. Modeling using non-linear multi-variable regression analysis and artificial neural networks are carried out to conduct parametric analysis and to understand, in depth, the DBG-AFF process. The simulation data of neural network show a good agreement with experimental results.     

Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology

A study on the radial-mode abrasive waterjet turning (AWJT) of 96 % alumina ceramic is presented and discussed. An experimental investigation is carried out to explore the influence of process parameters (including water pressure, jet feed speed, abrasive mass flow rate, surface speed, and nozzle tilted angle) on the material removal rate (MRR) when turning 96 % alumina ceramic. The experiments are conducted on the basis of response surface methodology (RSM) and sequential approach using face-centered central composite design. The quadratic model of RSM associated with the sequential approximation optimization (SAO) method is used to find optimum values of process parameters in terms of surface roughness and MRR. The results show that the MRR is influenced principally by the water pressure P and the next is abrasive mass flow rate m _a . The optimization results show that the MRR can be improved without increasing the surface roughness when machining 96 % alumina ceramic in the radial-mode abrasive waterjet turning process.     

Effect of material microstructure and tool geometry on surface generation in single point diamond turning

There is a strong desire in industry to improve surface finish when performing ultra-precision, single point diamond turning (SPDT) to reduce the amount of post process polishing required to meet final product specifications. However there are well known factors in SPDT which limit achievable surface finish. This paper focuses on the role of material microstructure, including grain boundary density and the presence of inclusions, as well as tool design on surface roughness using the concept of size effect. Size effect can be described as an interplay between the material microstructure dimension and the relative size of the uncut chip thickness with respect to the cutting edge radius. Since one of the controllable parameters in size effect is grain size and dislocation density, controlled studies were performed on samples whose microstructure was refined by mechanical strain hardening through rolling and a friction stir process (FSP). The use of the ultra-fine grained workpiece prepared using an FSP was observed to reduce side flow as well as grain boundary and inclusion induced roughness. The role of tool geometry on material induced roughness was investigated using a tool with a rounded primary cutting edge and a flat secondary edge. The use of the flat secondary edge was observed to improve surface finish when machining a flat surface. This improvement was primarily attributed to a reduction in side flow and material microstructural effects. By combining these approaches an average surface roughness R_a value of 0.685 nm was achieved when SPDT a flat surface. Furthermore the custom tool has the potential to significantly improve the productivity of SPDT by allowing for a much higher feed rate while still achieving a high quality surface finish.     

Influence of surface preparation on roughness parameters,friction and wear

The aim of the present research was to investigate influence of surface preparation on roughness parameters and correlation between roughness parameters and friction and wear. First the correlation between different surface preparation techniques and roughness parameters was investigated. For this purpose 100Cr6 steel plate samples were prepared in terms of different average surface roughness, using different grades of grinding, polishing, turning and milling. Different surface preparation techniques resulted in different R_a values from 0.02 to 7 μm. After this, correlation between surface roughness parameters and friction and wear was investigated. For this reason dry and lubricated pin-on-disc tests, using different contact conditions, were carried out, where Al₂O₃ ball was used as counter-body. It was observed that parameters R_ku, R_sk, R_pk and R_vk tend to have influence on coefficient of friction.     

Improved measure of workpiece surface deterioration during turning using non-contact vision method

The surface finish quality of a machined workpiece is commonly measured using the average roughness parameter, R_a. This parameter, however, is insensitive to the lateral changes undergone by the surface in the feed direction as a consequence of tool wear. In this work, the effectiveness of four methods of workpiece surface analysis, namely autocorrelation, cross-correlation, and two new methods, called lateral material shift (LMS) ratio and profile slope ratio (PSR) analyses are investigated. Dry machining experiments were carried out on 316 stainless steel. Images of tool nose and workpiece profiles were captured using digital camera, and the edges were extracted using sub-pixel edge detection. In the autocorrelation approach, each workpiece profile was correlated with a shifted version of the same profile. In the cross-correlation approach, the workpiece profiles at different stages of machining were correlated with a reference profile generated using the unworn tool edge. In the LMS ratio method, the material shift ratios were determined from each waveform on the workpiece profile at various stages of tool wear, while in the PSR method the slopes at the right and left part of the waveform were compared. Among the four methods, the LMS ratio method produced the best correlation with tool flank wear with the maximum R-squared value of 0.9461, while average roughness R_a showed no correlation at all with both major and nose flank wear.     

Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach

In the past, roughness values measured directly on machined surfaces were used to develop mathematical models that are used in predicting surface roughness in turning. This approach is slow and tedious because of the large number of workpieces required to obtain the roughness data. In this study, 2-D images of cutting tools were used to generate simulated workpieces from which surface roughness and dimensional deviation data were determined. Compared to existing vision-based methods that use features extracted from a real workpiece to represent roughness parameters, in the proposed method, only simulated profiles of the workpiece are needed to obtain the roughness data. The average surface roughness R _a, as well as dimensional deviation data extracted from the simulated profiles for various feed rates, depths of cut, and cutting speeds were used as the output of response surface methodology (RSM) models. The predictions of the models were verified experimentally using data obtained from measurements made on the real workpieces using conventional methods, i.e., surface roughness tester and a micrometer, and good correlation between the two methods was observed.