Surjya K. Pal

Flank wear prediction in drilling using back propagation neural network and radial basis function network

In the present work, two different types of artificial neural network (ANN) architectures viz. back propagation neural network (BPNN) and radial basis function network (RBFN) have been used in an attempt to predict flank wear in drills. Flank wear in drill depends upon speed, feed rate, drill diameter and hence these parameters along with other derived parameters such as thrust force, torque and vibration have been used to predict flank wear using ANN. Effect of using increasing number of sensors in the efficacy of predicting drill wear by using ANN has been studied. It has been observed that inclusion of vibration signal along with thrust force and torque leads to better prediction of drill wear. The results obtained from the two different ANN architectures have been compared and some useful conclusions have been made.

Silicon—a new substrate for GaN growth

Generally, GaN-based devices are grown on silicon carbide or sapphire substrates. But these substrates are costly and insulating in nature and also are not available in large diameter. Silicon can meet the requirements for a low cost and conducting substrate and will enable integration of optoelectronic or high power electronic devices with Si based electronics. But the main problem that hinders the rapid development of GaN devices based on silicon is the thermal mismatch of GaN and Si, which generates cracks. In 1998, the first MBE grown GaN based LED on Si was made and now the quality of material grown on silicon is comparable to that on sapphire substrate. It is only a question of time before Si based GaN devices appear on the market. This article is a review of the latest developments in GaN based devices on silicon.

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.

Experimental Studies on Different Cooling Processes to Achieve Ultra-Fast Cooling Rate for Hot Steel Plate

In the current research, different cooling processes for getting an ultra-fast cooling rate for a hot static plain carbon steel plate are studied. Depending upon the initial surface temperature and the final microstructures of dual-phase steel, the first type of ultra-fast cooling is late ultra-fast cooling, and second type is early ultra-fast cooling. Late ultra-fast cooling is studied by a water spray and air-assisted spray cooling process, and early ultra-fast cooling is studied by air-assisted spray only. The heat transfer analysis on the above cooling processes show that air-assisted spray has an excellent control on the rate of cooling.

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.

Mechanical properties of woven jute–glass hybrid-reinforced epoxy composite

As major historical periods such as Stone Age, Bronze Age, and Iron Age, the development of new materials was the fundamental to all the periods. In the present investigation, a new hybrid composite with epoxy as a resin and reinforcing both biowaste (jute) and traditional fiber (glass) as continues layered mat composites and also study experimentally the effect of the stacking sequence on tensile, flexural, and interlaminar shear properties. Composites were prepared by using hand lay-up technique. All the laminates were prepared with a total of four piles, by varying the position of glass and jute. One group of all jute and glass laminate was also fabricated for comparison purpose. Specimen preparation and testing were carried out as per ASTM standards. Tests were conducted on INSTRON H10KS Material Test System at room temperature using automatic data acquisition software. The results indicated that the jute fiber and hybrid composite give encouraging results when compared with the neat epoxy. The morphologies of the composites are also studied by scanning electron microscope.

Monitoring of Weld Penetration Using Arc Acoustics

The weld penetration monitoring is a challenging work in modern automated manufacturing industries. The weld quality can be improved with higher depth of penetration and less weld pool area. In this work, various pulse parameters have been varied to investigate their influence on weld penetration in pulsed metal inert gas (P-MIG) welding. The primary objective was to improve the depth of penetration adjusting the pulse parameters. The sound sensor and an infrared pyrometer were used along with arc sensors to properly monitor the depth of weld penetration. Finally, an attempt has also been made to correlate the time domain statistical features of each sensor with weld bead characteristics.

Radial basis function neural network model based prediction of weld plate distortion due to pulsed metal inert gas welding

Abstract Welding shrinkage and distortion affect the shape, dimensional accuracy and strength of the finished product. This work concerns the prediction of welding distortion in a pulsed metal inert gas welding (PMIGW) process. Six different types of radial basis function network (RBFN) models have been developed to predict the distortion of welded plates. Six process parameters, namely, pulse voltage, background voltage, pulse duty factor, pulse frequency, wire feed rate and the welding speed, along with the root mean square (RMS) values of two sensor signals, namely, the welding current and the voltage signals, are used as input variables of these models. The angular distortion and the transverse shrinkage of the welded plate are considered as the output variables. Inclusion of sensor signals in the models, as developed in this work, results in better output prediction.

Defect identification in friction stir welding using discrete wavelet analysis

Detection of fault occurred during friction stir welding.Analyzed using discrete wavelet transform on force and torque signals.Provides plot of frequency spectra vs. time with varying resolution.Variance and square of errors of detail coefficients of transformed signal are obtained to localize the defected zone. This article discusses on the detection of fault occurred during friction stir welding using discrete wavelet transform on force and torque signals. The work pieces used were AA1100 aluminum alloys of thickness 2.5mm. The plates were 200mm in length and 80mm in width. Presence of defect in welding causes sudden change in force signals (Z-load), thus it is easier to detect such abrupt changes in a signal using discrete wavelet transform. Statistical features like variance and square of errors of detail coefficients are implemented to localize the defective zone properly as it shows better variations (in defective area) than the detail coefficient itself.

Determination of Optimal Pulse Metal Inert Gas Welding Parameters with a Neuro-GA Technique

Optimization of a manufacturing process is a rigorous task because it has to take into account all the factors that influence the product quality and productivity. Welding is a multi-variable process, which is influenced by a lot of process uncertainties. Therefore, the optimization of welding process parameters is considerably complex. Advancement in computational methods, evolutionary algorithms, and multiobjective optimization methods create ever-more effective solutions to this problem. This work concerns the selection of optimal parameters setting of pulsed metal inert gas welding (PMIGW) process for any desired output parameters setting. Six process parameters, namely pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate and table feed rate were used as input variables, and the strength of the welded plate, weld bead geometry, transverse shrinkage, angular distortion and deposition efficiency were considered as the output variables. Artificial neural network (ANN) models were used for mapping input and output parameters. Neuro genetic algorithm (Neuro-GA) technique was used to determine the optimal PMIGW process parameters. Experimental result shows that the designed parameter setting of PMIGW process, which was obtained from Neuro-GA optimization, indeed produced the desired weld-quality.