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

Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm

The residual stresses induced during shaping and machining play an important role in determining the integrity and durability of metal components. An important issue of producing safety critical components is to find the machining parameters that create compressive surface stresses or minimise tensile surface stresses. In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The new method employs a hierarchical optimisation structure to improve the modelling efficiency, where two learning mechanisms cooperate together: NSGA-II is used to improve the model’s structure while the gradient descent method is used to optimise the numerical parameters. This hybrid approach is then successfully applied to the problem that concerns the prediction of machining induced residual stresses in aerospace aluminium alloys. Based on the developed reliable prediction models, NSGA-II is further applied to the multi-objective optimal design of aluminium alloys in a ‘reverse-engineering’ fashion. It is revealed that the optimal machining regimes to minimise the residual stress and the machining cost simultaneously can be successfully located

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion. Nowadays, application of design of experiment (DoE), evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein. This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must 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 paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

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

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

Identification of key GMAW fillet weld parameters and interactions using artificial neural networks

Fillet welds are one of the most commonly used weld joints but one of the most difficult to weld consistently. This paper presents a technique using Artificial Neural Networks (ANN) to identify the key Gas Metal Arc Welding (GMAW) fillet weld parameters and interactions that impact on the resultant geometry, when using a metal cored wire. The input parameters to the model were current, voltage, travel speed; gun angle and travel angle and the outputs of the model were penetration and leg length. The model was in good agreement with experimental data collected and the subsequent sensitivity analysis showed that current was the most influential parameter in determining penetration and that travel speed, followed closely by current and voltage were most influential in determining the leg length. The paper also concludes that a ‘pushing’ travel angle is preferred when trying to control the resultant geometry mainly because both the resultant leg length and penetration appear to be less sensitive to changes in heat input