A hybrid approach of anfis—artificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monel™ 400 alloy

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes

Multi objective machining estimation model using orthogonal and neural network

Much hard work has been done to model the machining operations using the neural network (NN). However, the selection of suitable neural network model in machining optimization area especially in multi objective area is unsupervised and resulted in pointless trials. Thus, a combination of Taguchi orthogonal and NN modeling approach is tested on two types of electrical discharge machining (EDM) operations; Cobalt Bonded Tungsten Carbide (WC-Co) and Inconel 718 to observe the efficiency of proposed approach on different numbers of objectives. WC-Co EDM considered two objective functions and Inconel 718 EDM considered four objective functions. It is found that one hidden layer 4-8-2 layer recurrent neural network (LRNN) is the best estimation model for WC-Co machining and one hidden layer 5-14-4 cascade feed forward back propagation (CFBP) is the best estimation model for Inconel 718 EDM. The results are compared with trial-error approach and it is proven that the proposed modeling approach is able to improve the machining performances and works efficiently on two-objective problems

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availability. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have previously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature

Prediction of welding responses using AI approach : adaptive neuro-fuzzy inference system and genetic programming

Laser welding of thin sheets has widespread application in various fields such as battery manufacturing, automobiles, aviation, electronics circuits and medical sciences. Hence, it is very essential to develop a predictive model using artificial intelligence in order to achieve high-quality weldments in an economical manner. In the present study, two advanced artificial intelligence techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP), were implemented to predict the welding responses such as heat-affected zone, surface roughness and welding strength during joining of thin sheets using Nd:YAG laser. The study attempts to develop an appropriate predictive model for the welding process. In the proposed methodology, 70% of the experimental data constitutes the training set whereas remaining 30% data is used as testing set. The results of this study indicated that the root-mean-square error (RMSE) of tested data set ranges between 7 and 16% for MGGP model, while RMSE for testing data set lies 18–35% for ANFIS model. The study indicates that the MGGP predicts the welding responses in a superior manner in laser welding process and can be applied for accurate prediction of performance measures

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Multi-Objective Optimization of Wire Electro Discharge Machining (WEDM) Process Parameters Using Grey-Fuzzy Approach

Wire electro discharge machining (WEDM) is a versatile non-traditional machining process that is extensively in use to machine the components having intricate profiles and shapes. In WEDM, it is very important to select the optimal process parameters so as to enhance the machine performance. This paper emphasizes the selection of optimal parametric combination of WEDM process while machining on EN31 steel, using grey-fuzzy logic technique. Process parameters such as servo voltage, wire tension, pulse-on-time and pulse-off-time were considered while taking into account several multi-responses such as material removal rate (MRR) and surface roughness (SR). It was found that pulse-on-time of 115 µs, pulse-off-time of 35 µs, servo voltage of 40 V and wire tension of 5 kgf results in a larger value of grey fuzzy reasoning grade (GFRG) which tends to maximize MRR and improve SR. Finally, analysis of variance (ANOVA) is applied to check the influence of each process parameters in the estimation of GFRG

Adaptive Neuro-Fuzzy Inference System modelling of surface topology in ultra-high precision diamond turning of rapidly solidified aluminium grade (RSA 443)

Surface roughness prediction is a crucial stage during product manufacturing since it acts as a quality indicator. This investigative research thesis presents an online surface roughness prediction, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model during Ultra-High Precision Diamond Turning (UHPDT) of Rapidly Solidified Aluminium (RSA-443) using water and kerosene as coolants. Based on the Taguchi L9 orthogonal array, the cutting parameters (spindle speed, depth of cut and feed rate) are varied at three levels. Acoustic Emission (AE) signals are detected during the UHPDT process using a piezoelectric sensor. Spindle speed, depth of cut, feed rate, AE root mean square, prominent frequency and peak rate are considered as model inputs in this thesis. The experimental results reveal that a better surface finish is obtained using water coolant in comparison to kerosene coolant. Mean Absolute Percentage Error (MAPE) based comparison between ANFIS and Response Surface Method (RSM) is carried out. In this study, the ANFIS model has a prediction accuracy of 79.42% and 69.40% on water-based and kerosene-based results respectively. The RSM model yields higher prediction accuracies of 98.59% and 95.55% on water-based and kerosene-based results respectively

Adaptive Neuro-Fuzzy Inference System modelling of surface topology in ultra-high precision diamond turning of rapidly solidified aluminium grade (RSA 443)

Surface roughness prediction is a crucial stage during product manufacturing since it acts as a quality indicator. This investigative research thesis presents an online surface roughness prediction, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model during Ultra-High Precision Diamond Turning (UHPDT) of Rapidly Solidified Aluminium (RSA-443) using water and kerosene as coolants. Based on the Taguchi L9 orthogonal array, the cutting parameters (spindle speed, depth of cut and feed rate) are varied at three levels. Acoustic Emission (AE) signals are detected during the UHPDT process using a piezoelectric sensor. Spindle speed, depth of cut, feed rate, AE root mean square, prominent frequency and peak rate are considered as model inputs in this thesis. The experimental results reveal that a better surface finish is obtained using water coolant in comparison to kerosene coolant. Mean Absolute Percentage Error (MAPE) based comparison between ANFIS and Response Surface Method (RSM) is carried out. In this study, the ANFIS model has a prediction accuracy of 79.42% and 69.40% on water-based and kerosene-based results respectively. The RSM model yields higher prediction accuracies of 98.59% and 95.55% on water-based and kerosene-based results respectively