Karali Patra

Artificial neural network based prediction of drill flank wear from motor current signals

In this work, a multilayer neural network with back propagation algorithm (BPNN) has been applied to predict the average flank wear of a high speed steel (HSS) drill bit for drilling on a mild steel work piece. Root mean square (RMS) value of the spindle motor current, drill diameter, spindle speed and feed-rate are inputs to the network, and drill wear is the output. Drilling experiments have been carried out over a wide range of cutting conditions and the effects of drill wear, cutting conditions (speed, drill diameter, feed-rate) on the spindle motor current have been investigated. The performance of the trained neural network has been tested for new cutting conditions, and found to be in very good agreement to the experimentally determined drill wear values. The accuracy of the prediction of drill wear using neural network is found to be better than that using regression model.

Size effects in micro drilling of carbon fiber reinforced plastic composite

The contribution presents investigations regarding the size effects on cutting forces in micro drilling of carbon fiber reinforced plastic composite. Generally, size effect is described as non-linear increase of specific cutting force with decreasing chip thickness. Specific cutting forces are determined by dividing cutting force components by chip area. In a mathematical model, specific cutting force is expressed as a function of the ratio of undeformed chip thickness to cutting edge radius. The coefficients of the model are determined by regression analysis using experimental results. Non-linear increase of specific cutting force is observed when the ratio decreases, especially when the ratio is less than unity.

Modeling and Simulation of Mechanical Micro-Machining—A Review

This article provides a comprehensive review of literature on modeling and simulation that enhances the understanding of the process physics of the rapidly growing field of mechanical micro-machining. The article focuses on a number of micro-scale machining issues such as size effect, minimum chip thickness effect, micro-structure effects, and cutting tool dynamics which influence the underlying cutting mechanisms and the generated surface finish. We review the recent advances in modeling and simulation techniques, which include molecular dynamics simulation, finite element method, the newly emerging field of multi-scale simulation and mechanistic modeling. Some comments to emphasize the future requirements and directions of the modeling and simulation efforts in this field are also offered.

Experimental Analysis of Cutting Forces in Microdrilling of Austenitic Stainless Steel (X5CrNi18-10)

This article presents an experimental investigation on microdrilling of austenitic stainless steel which is a difficult material for machining because of its properties like high strain-hardening rate, low thermal conductivity, and high fracture toughness. Microholes are produced on X5CrNi18-10 austenitic stainless steel workpiece using 0.5 mm diameter solid carbide microdrills. Two factors (cutting speed and feed) and three levels (low–center–high) full-factorial design of experiment are performed. Response surface methodology is used to developed mathematical models (quadratic and bilinear regression models) for cutting forces in microdrilling. The experimental analysis shows that feed affects the cutting force components (radial and thrust) significantly. Additionally, it also shows that there are only minor effects from cutting speed, square of cutting speed, square of feed and product of speed, and feed on the cutting forces. Finally, the optimized cutting conditions are proposed for minimum cutting forces.

Application of wavelet packet analysis in drill wear monitoring

In this work, an attempt has been made to develop a drill wear monitoring system which is independent to cutting conditions of the drilling process. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. An advanced signal processing technique, the wavelet packet transform has been used on the acquired current signature to extract features for indirect representation to the amount of drill wear. Experimental sensitivity analysis reveals that in comparison to time domain features, wavelet packet features are more sensitive to flank wear and less sensitive to the cutting conditions. A multilayer neural network model has then been developed to correlate the extracted wavelet packet features with drill flank wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the flank wear with acceptable accuracy.

Genetically evolved radial basis function network based prediction of drill flank wear

The most important factor that governs the performance of a radial basis function network (RBFN) is the optimization of the network architecture, i.e. determining the exact number of radial basis functions (RBFs) in the hidden layer that can best minimize the error between the actual and network outputs. This work presents a genetic algorithm (GA) based evolution of optimal RBFN architecture and compares its performance with the conventional RBFN training procedure employing a two stage methodology, i.e. utilizing the k-means clustering algorithm for the unsupervised training in the first stage, and using linear supervised techniques for subsequent error minimization in the second stage. The validation of the proposed methodology is carried out for the prediction of flank wear in the drilling process following a series of experiments involving high speed steel (HSS) drills for drilling holes on mild-steel workpieces. The genetically grown RBFN not only provides an improved network performance, it is also computationally efficient as it eliminates the need for the error minimization routine in the second stage training of RBFN.

Effect of different basis functions on a radial basis function network in prediction of drill flank wear from motor current signals

The Gaussian kernel has almost exclusively been used as the basis function of the cluster centers (hidden layer nodes) of a radial basis function network (RBFN) in most of its applications, especially in tool condition monitoring (TCM) problems. This study explores the possible usage of a set of five other basis functions in addition to the standard Gaussian function, in one such important TCM problem, i.e., prediction of drill flank wear. The analysis focuses on a comparative study of the wear prediction capabilities of the RBFN employing these six different basis functions for a wide range of the basis width parameter (wherever applicable) and changing the number of cluster centers in the hidden layer. This analysis is carried out following a series of experiments employing high speed steel (HSS) drills for drilling holes on mild steel workpieces, under different sets of cutting conditions (spindle speed, feed-rate and drill diameter) and noting the root mean square (RMS) value of spindle motor current as well as the average flank wear in each case. The results show that other basis functions can also match the performance of the Gaussian kernel, and depending upon the nature of application at hand and the requirements of time and space, the use of basis functions other than the Gaussian kernel may just prove advantageous.

FUZZY RADIAL BASIS FUNCTION (FRBF) NETWORK BASED TOOL CONDITION MONITORING SYSTEM USING VIBRATION SIGNALS

Thriving automation in industries leads to more research on the tool condition monitoring systems for better accuracy and fast recognition/evaluation of tool wear. Research on the applicability of the new advances in the soft-computing as well as in the signal processing fields is the inevitable consequence. In this work, a new soft-computing modeling technique, fuzzy radial basis function (FRBF) network has been applied to the prediction of drill wear using the vibration signal features. This work presents the wear prediction performance comparison of this new model with three other already tried and established soft-computing models, such as back propagation neural network (BPNN), radial basis function network (RBF) and normalized radial basis function network (NRBF), for both time-domain as well as wavelet packet approaches of feature extraction. Experimental results show that FRBF model with wavelet packet approach produces the best performance of predicting flank wear.

Rate-dependent mechanical behavior of VHB 4910 elastomer

ABSTRACT This article experimentally studied the large nonlinear deformation of VHB 4910 elastomer by uniaxial tests. The study reveals that the monotonic tensile stress-strain, hysteresis, cyclic stress softening, and multistep stress relaxation of this elastomer exhibit rate-sensitivity. Toughness, failure stress, and failure strain are shown to vary with strain rate. Maximum cyclic stress, hysteresis loss, residual strains in cyclic loading-unloading, and stress relaxation in multistep relaxation tests are also shown to be rate-sensitive. The analytical models are also proposed to predict certain important parameters, such as dissipative work, cyclic stress softening, cyclic residual strain, and relaxation stress in different states of deformation.

Drill Wear Monitoring through Current Signature Analysis using Wavelet Packet Transform and Artificial Neural Network

In automated manufacturing systems, tool wear monitoring plays an important role in ensuring the dimensional accuracy of the workpiece and automatic cutting process without any failure. It is, therefore, very essential to develop simple, reliable, process condition independent and cost effective online tool wear monitoring system. In this work, an attempt has been made to develop a drill tool wear monitoring system which fulfills most of the above essential requirements. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. A more advanced signal processing technique, wavelet packet transform has been implemented on the acquired current signature to extract features which are more sensitive to drill wear and less sensitive to the process conditions. A multilayer neural network model has been developed to correlate the extracted features with drill wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the drill wear with acceptable accuracy.