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

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm

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

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Model fusion using fuzzy aggregation: Special applications to metal properties

To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments

Adaptive control optimization in micro-milling of hardened steels-evaluation of optimization approaches

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high-quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micro-manufacturing. In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: (1) a monitoring sensor system composed of a dynamometer, (2) an estimation module with Artificial Neural Networks models, (3) an optimization module with evolutionary optimization algorithms, and (4) a CNC interface module. In order to operate in a nearly real-time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as particle swarm optimization (PSO), genetic algorithms (GA), and simulated annealing (SA) in terms of accuracy, precision, and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3 and 29 % in comparison with conservative and high-production strategies, respectively

Modeling and Optimization of Micro-EDM Operation for Fabrication of Micro Holes

Based on the experimental results, an analysis was made to identify the performance of various electrodes during fabrication of micro holes considering Inconel 718 as well as titanium as workpiece materials. It was found that that platinum followed by graphite and copper as electrode material exhibited higher MRR for both the workpiece materials but on the other hand platinum showed higher values of OC, RCL and TA respectively when compared to graphite and copper. The variation of temperature distribution in radial and depth direction with different process parameters has been determined for Inconel 718 and Titanium 5. Theoretical cavity volume was calculated for different process parameter settings for both workpiece materials and it was found that Titanium 5 exhibited higher cavity volume then Inconel 718. This research work offers new insights into the performance of micro-µ-EDM of Inconel 718 and Titanium5 using different electrodes. The optimum process parameters have been identified to determine multi-objective machinability criteria such as MRR, angle of taper of micro-hole, the thickness of recast-layer and overcut for fabrication of micro-holes

Industrial time series modelling by means of the neo-fuzzy neuron

Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the Neo-Fuzzy Neuron is configured and trained according by means of the auxiliary signal, past instants and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modelled. The obtained results indicate the suitability of the Neo-Fuzzy Neuron method for industrial process modelling.Postprint (published version

Application of ”ART SIMULATOR” for Manufacturing Similarity Identification in Group Technology Design - Chapter 10

This chapter 10 carried out the exceptional implementation of ART-1 neural network in the analysis of the manufacturing similarity of the cylindrical parts within the group technology design. Established concept of the group technology design begins from the complex part of the group or the group representative. Group representative has all the geometrical elements of the parts in group, and manufacturing procedure may be applied to the machining of any part in the group. The complex part may be realistic or a hypothetical one. The ART-1 artificial neural network provided manufacturing classification according to the geometrical similarities of work-pieces for the group of cylindrical parts. For the manufacturing similarity identification within the group technology design, software package "ART Simulator" is developed and presented in this chapter

The Application of ANN and ANFIS Prediction Models for Thermal Error Compensation on CNC Machine Tools

Thermal errors can have significant effects on Computer Numerical Control (CNC) machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This thesis first reviews different methods of designing thermal error models, before concentrating on employing Artificial Intelligence (AI) methods to design different thermal prediction models. In this research work the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the backbone for thermal error modelling. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this thesis, temperature measurement was supplemented by direct distortion measurement at accessible locations. The location of temperature measurement must also provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this thesis, a new intelligent system for reducing thermal errors of machine tools using data obtained from thermography data is introduced. Different groups of key temperature points on a machine can be identified from thermal images using a novel schema based on a Grey system theory and Fuzzy C-Means (FCM) clustering method. This novel method simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. An Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means clustering (ANFIS-FCM) is then employed to design the thermal prediction model. In order to optimise the approach, a parametric study is carried out by changing the number of inputs and number of Membership Functions (MFs) to the ANFIS-FCM model, and comparing the relative robustness of the designs. The proposed approach has been validated on three different machine tools under different operation conditions. Thus the proposed system has been shown to be robust to different internal heat sources, ambient changes and is easily extensible to other CNC machine tools. Finally, the proposed method is shown to compare favourably against alternative approaches such as an Artificial Neural Network (ANN) model and different Grey models