A comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling

This paper describes the comparison of the burr size predictive models based on artificial neural networks (ANN) and response surface methodology (RSM). The models were developed based on three-level full factorial design of experiments conducted on AISI 316L stainless steel work material with cutting speed, feed, and point angle as the process parameters. The ANN predictive models of burr height and burr thickness were developed using a multilayer feed forward neural network, trained using an error back propagation learning algorithm (EBPA), which is based on the generalized delta rule. The performance of the developed ANN models were compared with the second-order RSM mathematical models of burr height and thickness. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. The details of experimentation, model development, testing, and performance comparison are presented in the paper.     

Integrating Box-Behnken design with genetic algorithm to determine the optimal parametric combination for minimizing burr size in drilling of AISI 316L stainless steel

This paper illustrates the methodology of genetic algorithm (GA) based multi-objective drilling process optimization. The optimal values of cutting speed, feed, point angle and lip clearance angle for a specified drill diameter were determined using GA, which simultaneously minimize burr height and burr thickness at the exit of holes during drilling of AISI 316L stainless steel. The burr size models required for GA optimization were developed using response surface methodology (RSM) with drilling experiments planned as per Box-Behnken design. The GA optimization results reveal that point angle has a significant role in controlling the burr size.     

Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach

Drilling is one of the important machining processes performed extensively in production industry. Literature emphasises that the output process parameters such as burr height, surface roughness, strength, etc. are related to and can be improved by the appropriate settings of the input process parameters. Recently, researchers have applied well-known computational intelligence methods such as regression analysis, artificial neural networks (ANNs), support vector regression (SVR), etc. in the prediction of performance characteristics of the drilling process. Alternatively, an evolutionary approach of multi-gene genetic programming (MGGP) that evolves the model structure and its coefficients automatically can be applied. Despite of being widely applied, MGGP has the limitation for producing models that over-fit on the testing data. One of the reasons attributed for this behaviour is the over-size of the evolved models. Therefore, a statistical-based MGGP (S-MGGP) approach is proposed and applied to the burr height data obtained from the drilling of AISI 316L stainless steel. In this proposed approach, Bayesian information criterion is embedded in its paradigm, which punishes the fitness of larger size models. The performance of S-MGGP and ANN models is found to be better than those of the standardised MGGP and SVR. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the drilling phenomenon by unveiling dominant input process parameters and the hidden non-linear relationships.     

Methodology of Taguchi optimization for multi-objective drilling problem to minimize burr size

Formation of exit burr on part edges during drilling has several undesirable features with regard to product quality and functionality. Hence it is essential to select optimum drilling process parameters to minimize burr size at the production stage. This paper presents the application of the Taguchi optimization method for simultaneous minimization of burr height and burr thickness influenced by cutting conditions and drill geometry. The Taguchi design approach to the multi-objective optimization problem is based on the introduction of a new concept of fitness function for each trial of orthogonal array. The fitness function is derived through mapping the objective functions of the drill optimization problem. In the present work, optimal values of cutting speed, feed, point angle and lip clearance angle are determined for selected drill diameter values to minimize burr height and burr thickness during drilling of AISI 316L stainless steel workpieces. The details of experimentation, analysis of means and analysis of variance are presented in the paper.     

Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology

This investigation presents the use of Taguchi and response surface methodologies for minimizing the burr height and the surface roughness in drilling Al-7075. The Taguchi method, a powerful tool to design optimization for quality, is used to find optimal cutting parameters. Response surface methodology is useful for modeling and analyzing engineering problems. The purpose of this paper was to investigate the influence of cutting parameters, such as cutting speed and feed rate, and point angle on burr height and surface roughness produced when drilling Al-7075. A plan of experiments, based on L₂₇ Taguchi design method, was performed drilling with cutting parameters in Al-7075. All tests were run without coolant at cutting speeds of 4, 12, and 20 m/min and feed rates of 0.1, 0.2, and 0.3 mm/rev and point angle of 90^°, 118°, and 135°. The orthogonal array, signal-to-noise ratio, and analysis of variance (ANOVA) were employed to investigate the optimal drilling parameters of Al-7075. From the analysis of means and ANOVA, the optimal combination levels and the significant drilling parameters on burr height and surface roughness were obtained. The optimization results showed that the combination of low cutting speed, low feed rate, and high point angle is necessary to minimize burr height. The best results of the surface roughness were obtained at lower cutting speed and feed rates while at higher point angle. The predicted values and measured values are quite close to each other; therefore, this result indicates that the developed models can be effectively used to predict the burr height and the surface roughness on drilling of Al-7075.     

Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites

In this study, the application of response surface methodology (RSM) and central composite design (CCD) for modeling, optimization, and an analysis of the influences of dominant machining parameters on thrust force, surface roughness and burr height in the drilling of hybrid metal matrix composites produced through stir casting route. Experiments are carried out using Al 356-aluminum alloy reinforced with silicon carbide of size 25 μm and Mica of size 45 μm. Drilling test is carried out using carbide drill of 6 mm diameter. The design of experiment concept has been used to optimize the experimental conditions. The experimental data are collected based on a three-factor-three-level full central composite design. The multiple regression analysis using RSM is used to establish the input–output relationships of the process. The mathematical models are developed and tested for adequacy using analysis of variance and other adequacy measures using the developed models. The main and interaction effect of the input variables on the predicted responses are investigated. The predicted values and measured values are fairly close, which indicate that the developed models can be effectively used to predict the responses in the drilling of hybrid metal matrix composites. The optimized drilling process parameters have been obtained by numerical optimization using RSM by ensuring the minimum thrust force of 84 N, surface roughness of 1.67 μm, and the burr height of 0.16 mm. After the drilling experiments, a scanning electron microscope (SEM) is used to investigate the machined surface and tool wear.     

Experimental studies and optimization of process parameters for burrs in dry drilling of stacked metal materials

In the drilling of stacked materials, burrs produced on both the surface layer and the interlayer have some undesirable characters with regard to assembly quality, and deburring is a time consuming and costly operation. This paper presented an experimental study on the dry drilling of Ti-6Al-4V titanium alloy and 7075-T6 aluminum alloy stacked materials, which was performed by using uncoated cemented carbide drills. The burr size was evaluated at various spindle speeds, feed rates, stacking sequences, and clamp forces. After which, the burr morphology was observed and analyzed. Finally, a new multi-objective optimization algorithm, which was derived from the game theory, was used to select optimum process parameters to minimize burr size. The best process parameters recorded in this paper were at the spindle speed of 2,000?r/min, the feed rate of 0.075?mm/r, the pressure of 0.3?MPa, and the stacking sequence of the Ti-6Al-4V titanium alloy on top of the 7075-T6 aluminum alloy.     

Analysis and predictive modeling of performance parameters in electrochemical drilling process

In this paper, the effect of feed rate, voltage, and flow rate of electrolyte on some performance parameters such as surface roughness, material removal rate, and over-cut of SAE-XEV-F valve-steel during electrochemical drilling in NaCl and NaNo₃ electrolytic solutions have been studied using the main effect plot, the interaction plot and the ANOVA analysis. In continuation, in this case which the training dataset was small, an investigation has been done on the capability of the optimum presented regression analysis (RA), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) to predict the surface roughness, material removal rate and over-cut. The predicted parameters by the employed models have been compared with the experimental data. The comparison of results indicated that in electrochemical drilling using different electrolytic solutions, CANFIS gives the best results to predict the surface roughness and over-cut as well, while ANN is the best for predicting the material removal rate.     

New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks

The rheological properties of the drilling fluid are crucial to the success of the drilling project. The traditional mud experiments normally performed by the mud engineers provide rheological data with a small resolution. Monitoring higher-resolution rheological properties is particularly important for all-oil mud because it is widely used with problematic drilled formations. The design and monitoring of the drilling fluid rheology is a critical issue for drilling, and therefore, this paper is a contribution to the effort to completely automate the process of highly accurate and real-time recording of the rheological mud properties. This paper aims to develop intelligent predictive models for the mud rheological properties using artificial neural networks [ANN] by linking the high-frequency mud parameters such as fluid density or mud weight [MWT] and Marsh funnel viscosity [MFV] with the rheological measurements of low frequency for drilling mud such as plastic viscosity [PV], yield point [YP], behavior indicator [n] and viscosity appearance [AV]. New empirical correlations have additionally been established to assess the rheological properties of water. In order to construct ANN models, data was obtained from 56 different wells during drilling operations of different drilling sections with various sizes. The data was fairly enough for building and testing the models as 369 data points were obtained. The models were optimized by trainlm which was the best training function and tansig was the best transfer function. 42 neurons in the hidden layer optimized AV and PV models where 34 neurons optimized all other rheological models [YP, n, R300, and R600]. ANN models presented good results as correlation coefficient [R] was 0.9 and an average absolute [AAPE] error of less than 8% for training and testing data sets. The new models were used to derive the empirical correlations for the estimation of rheological parameters. The empirical correlations were extracted to easily monitor the rheological properties of an all-oil mud system in real-time, which enables better control of the drilling activity by maintaining rheological properties at optimal values as well as early detection of other problems that might require immediate interactions, including well control and stuck pipe.     

Effects of cutting edge radius and fiber cutting angle on the cutting-induced surface damage in machining of unidirectional CFRP composite laminates

Carbon fiber reinforced polymer composite laminates are anisotropic, inhomogeneous, and mostly prepared in laminate form before undergoing the finishing operations. The edge trimming process is considered as one of the most common finishing operations in the industrial applications. However, the laminate surface is especially prone to damage in the chip formation process, and the most common damage mode is burrs. Burrs may increase cost and production time because of additional machining; they can also damage the surface integrity. Many studies have been done to address this problem, and techniques for reducing burr size in material removal process has been the focus of the research. Nonetheless, the combined effects of the cutting edge radius and the fiber cutting angle on the burr formation have seldom been conducted, which in turn restricts to find out the mechanism of burr formation. The purpose of the present paper is to study the particular mechanism that leads to burr formation in edge trimming of CFRP laminates and investigate the effects of fiber cutting angle and cutting edge radius on burr formation. The results indicate that the burrs are prone to form in the fiber cutting angle range of 0° < χ < 90° when a large cutting edge radius of the tool is used for both milling and drilling of CFRP composites.     

FINITE ELEMENT MODELING OF BURR FORMATION IN METAL CUTTING

In order to advance understanding of the burr formation process, a series of finite element models are introduced. First, a finite element model of the burr formation of two-dimensional orthogonal cutting is introduced and validated with experimental observations. A detailed and thorough examination of the drilling burr forming process is undertaken. This information is then used in the construction of an analytical model and, leads to development of a three-dimensional finite element model of drilling burr formation. Using the model as a template, related burr formation problems that have not been physically examined can be simulated and the results used to control process planning resulting in the reduction of burr formation. We highlight this process by discussing current areas of research at the University of California in collaboration with the Consortium on Deburring and Edge Finishing (CODEF).     

FINITE ELEMENT MODELING OF BURR FORMATION IN METAL CUTTING

In order to advance understanding of the burr formation process, a series of finite element models are introduced. First, a finite element model of the burr formation of two-dimensional orthogonal cutting is introduced and validated with experimental observations. A detailed and thorough examination of the drilling burr forming process is undertaken. This information is then used in the construction of an analytical model and, leads to development of a three-dimensional finite element model of drilling burr formation. Using the model as a template, related burr formation problems that have not been physically examined can be simulated and the results used to control process planning resulting in the reduction of burr formation. We highlight this process by discussing current areas of research at the University of California in collaboration with the Consortium on Deburring and Edge Finishing (CODEF).     

Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis

The theory of grey systems is a new technique for performing prediction, relational analysis and decision making in many areas. In this paper, the use of grey relational analysis for optimising the drilling process parameters for the work piece surface roughness and the burr height is introduced. Various drilling parameters, such as feed rate, cutting speed, drill and point angles of drill were considered. An orthogonal array was used for the experimental design. Optimal machining parameters were determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the surface roughness and the burr height). Experimental results have shown that the surface roughness and the burr height in the drilling process can be improved effectively through the new approach .     

Development of a micro-drilling burr-control chart for PCB drilling

A drilling burr-control chart (DBCC), based on experimental results, is a tool for the prediction and control of drilling burrs for a large range of drilling parameters. A micro-drilling burr-control chart (M-DBCC) was developed for a standard double-sided copper-clad laminated (CCL) printed circuit board (PCB) with laminated fiber-reinforced plastic (FRP) substrate. This chart will assist in the selection of favorable drilling parameters for predicting and achieving preferred types of burrs. Burr classification was carried out according to the burr geometric characteristics, burr formation mechanisms, burr height, and drill bit breakage while drilling. The design of experiment (DOE) technique based on the Taguchi method was used to find the most significant drilling parameter affecting burr height. The results show that the drill diameter makes a statistically significant contribution to burr-height variation.     

Analytical modelling of slot milling exit burr size

A computational model was recently proposed by authors to approximate the tangential cutting force and consequently predict the thickness of the exit up milling side burr. To calculate the cutting force, the specific cutting force coefficient with respect to material properties was used. The model was sensitive to material yield strength and few cutting and tool geometrical parameters. However, the effects of cutting speed, tool coating, and tool rake angle on burr size were neglected. Other phenomena that could affect the burr size such as friction and abrasion were not taken into account either. Therefore, in the current work, a mechanistic force model is incorporated to propose a burr size prediction algorithm. The tangential and radial forces are calculated based on using specific cutting force coefficients in each direction. Furthermore, using the new approach, the burr size is predicated and the effects of a broad range of cutting parameters on burr size and friction angle are evaluated. Experimental values of burr size correlated well with prediction. It was found that the cutting speed has negligible effects on force and burr size. Lower friction angle was recorded when using larger feed per tooth. Consequently, thinner exit up milling side burr was obtained under high friction angle.     

Modeling of burr thickness in milling of ductile materials

Accuracy and surface finish play an important role in modern industry. The presence of undesired projections of materials, known as burrs, negatively affect the part quality and assembly process. To remove burrs, a secondary operation known as deburring is required for the post-processing and edge finishing of machined parts. The thickness of the burr is of interest as it describes the time and method necessary for deburring of the machined part. Burr thickness (B _t) measurements are costly and non-value-added operations that in most cases require the use of a scanning electron microscope for accurate burr characterization. Therefore, to avoid such expenses, the implementation of alternative methods for predicting the burr thickness is strongly recommended. In this research work, an analytical model for predicting the burr thickness in end milling of ductile materials is presented. The model is built on the geometry of burr formation and the principle of continuity of work at the transition from chip formation to burr formation that also takes into account the cutting force influence on burr formation. A very good correlation was found between the modeled and experimental B _t values. The model has shown a great sensitivity to material properties such as yield strength and specific cutting force coefficient (K _c). In addition, the sensitivity of the proposed model to the feed per tooth (f _t) and depth of cut (a _p) was considerably high. The proposed model allows the prediction of the thickness of the exit up milling side burr, without the need for experimental measurement and/or approximation of shear angle (Φ), friction angle (λ), and the tool chip contact length (L), unlike existing analytical burr size prediction models. Besides analytical modeling, statistical analysis is performed on experimental results in order to distinguish dominant process parameters on B _t. It is observed that the depth of cut and feed per tooth are the main parameters which significantly affect the B _t, while the speed has only a negligible effect on it.     

An experimental investigation of the effect of coatings and cutting parameters on the dry drilling performance of aluminium alloys

As a result of the need to automate assembly in the aircraft industry, along with economic and ecological reasons, industry and research institutions have been pushed to develop dry drilling for aluminium alloys to eliminate the need for cooling fluids. The main difficulties in dry drilling are accelerated tool wear due to workpiece material adhesion on the tool and the formation of bigger-sized burrs. This paper describes an experimental research study on machinability in the dry drilling of aluminium alloys and on the potential of the new design of tools and coatings. Dry drilling tests were performed using uncoated drills and two different coatings produced by means of an arc evaporation PVD process. Experiments consisted of machining with a 10-mm diameter three-edged drill to produce 25-mm deep holes. Tool wear evolution and burr size were analysed, as well as the impact of the process parameters on torque, power, feed force and tool temperature.     

A study on using scanning acoustic microscopy and neural network techniques to evaluate the quality of resistance spot welding

This paper shows that the quality of resistance spot welds can be evaluated using scanning acoustic microscopy (SAM). Two-layered coated spot-welded samples are investigated utilising a wide-field short-pulse scanning acoustic microscope with operation frequencies of 25, 50 and 100 MHz. Geometrical parameters, e.g. nugget area, maximum axis of nugget, and minimum axis of nugget, are acquired from C-scan images of weld nuggets using mathematical morphology techniques. These parameters serve as inputs for an artificial neural network (ANN) model to evaluate the quality of spot welds. The output of the model during the training process comprises the results of nugget peeling tests and expert opinions. The ANN can provide suggestions on weld quality with a higher than 95% correctness. A JAVA computer program is developed for image processing, ANN training, and ANN testing. With this model, the computer program can render the quality of spot welds that are close to those achieved using off-line destructive method.     

Mechanical microdrilling of negative-tapered laser-predrilled holes: a new approach for burr minimization

Despite considerable research effort being concentrated on improving hole quality, minimisation of burrs still remains a key challenge in mechanical microdrilling of ductile materials. Recently, a sequential laser-mechanical microdrilling technique, which was developed by the authors, has proved effective in improving tool life and reducing burr size. The improvement in burr size was achieved by means of laser predrilling a pilot hole before utilising a twist drill to finish the hole. This paper presents further development of the sequential laser-mechanical microdrilling process based on negative-tapered laser-drilled pilot holes. In the sequential laser-mechanical microdrilling process, although a large predrilled hole reduces the burr size, it has a negative effect on roundness and cylindricity of the hole. The results of further studies highlight that a smaller entry hole size supports the drill and suppresses tool wander which, in turn, improves hole cylindricity. Furthermore, a larger exit hole size significantly reduces burr size. Compared to pure mechanical drilling without any pilot hole, the reduction in burr size for the mechanical microdrilling of near zero tapper and negatively tapered laser-predrilled holes was observed to be 2.5 and 6 times, respectively. Moreover, the mechanical finishing of these holes does not compromise tool life and/or surface integrity. Thus, the use of a negative taper laser-predrilled hole presents a significant improvement in burr control in microdrilling of nickel-based super alloys in addition to providing a step change increase in tool life.     

Influence of size effect on burr formation in micro cutting

Burr is an important character of the surface quality for machined parts, and it is even more severe in micro cutting. Due to the uncut chip thickness and the cutting edge radius at the same range in micro cutting process, the tool extrudes the workpiece with negative rake angle. The workpiece flows along the direction of minimum resistance, and Poisson burr is formed. Based on the deformation analysis and experiment observations of micro cutting process, the factor for Poisson burr formation is analyzed. It is demonstrated that the ratio of the uncut chip thickness to the cutting edge radius plays an important role on the height of Poisson burr. Increasing the uncut chip thickness or decreasing the cutting edge radius makes the height of exit burr reduce. A new model of micro exit burr is established in this paper. Due to the size effect of specific cutting energy, the exit burr height increases. The minimum exit burr height will be obtained when the ratio of uncut the chip thickness to the cutting edge radius reaches 1. It is found that the curled radius of the exit burr plays an important role on the burr height.