Application of integrated soft computing techniques for optimisation of hybrid CO2 laser–MIG welding process

In this paper, artificial neural networks (ANNs), genetic algorithm (GA), simulated annealing (SA) and Quasi Newton line search techniques have been combined to develop three integrated soft computing based models such as ANN–GA, ANN–SA and ANN–Quasi Newton for prediction modelling and optimisation of welding strength for hybrid CO₂ laser–MIG welded joints of aluminium alloy. Experimental dataset employed for the purpose has been generated through full factorial experimental design. Laser power, welding speeds and wires feed rate are considered as controllable input parameters. These soft computing models employ a trained ANN for calculation of objective function value and thereby eliminate the need of closed form objective function. Among 11 tested networks, the ANN with best prediction performance produces maximum percentage error of only 3.21%. During optimisation ANN–GA is found to show best performance with absolute percentage error of only 0.09% during experimental validation. Low value of percentage error indicates efficacy of models. Welding speed has been found as most influencing factor for welding strength.     

Neural-network-based integrated model for predicting burn-through point in lead–zinc sintering process

This paper presents an integrated neural-network-based model for predicting the burn-through point (BTP) of a lead–zinc sintering process. This process features strong nonlinearity and time-varying parameters. First, experiments were carried out to establish a model of the gas temperature distribution (GTD) in the sintering machine; and based on the GTD model, a surface temperature model of the material (STMM) was established. Second, based on the STMM, a method of estimating the BTP that uses a soft-sensing technique was devised. In order to improve the estimation precision, a time-sequence-based model for predicting the BTP was built using grey system theory. Since the BTP is also affected by process parameters, a technological-parameter-based model for predicting the BTP was then built using a neural network. Finally, an integrated model for predicting the BTP was constructed by combining the time-sequence-based and the technological-parameter-based models using a fuzzy classifier. The result of actual runs shows that, compared to the manual control, the integrated prediction model reduced the variation in BTP by about 50%. This guarantees the improvement of the quality and quantity of the sinter.     

The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling

Neural networks have been widely used in manufacturing industry, but they suffer from a lack of structured method to determine the settings of NN design and training parameters, which are usually set by trial and error. This article presents an application of Taguchis Design of Experiments, to identify the optimum setting of NN parameters in a multilayer perceptron (MLP) network trained with the back propagation algorithm. A case study of a complex forming process is used to demonstrate implementation of the approach in manufacturing, and the issues arising from the case are discussed.     

Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique

A method for black-box identification of a Wiener–Hammerstein system is described and applied to a set of Benchmark data originally presented at the 15th IFAC Symposium on System Identification. An incremental nonlinear optimisation procedure is used, which is able to avoid local minima, thus enabling the solution to converge to the global minimum. The use of a dual-polynomial to describe the static nonlinearity allows the number of parameters needed to be significantly reduced compared with the case if a single polynomial is utilised; this also improves robustness against extrapolation errors. The overall approach requires a relatively small number of parameters.     

Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm

Teaching–learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching–learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of a two stage thermoelectric cooler (TEC). Two different arrangements of the thermoelectric cooler are considered for the optimization. Maximization of cooling capacity and coefficient of performance of the thermoelectric cooler are considered as the objective functions. An example is presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization obtained by using the modified TLBO are validated by comparing with those obtained by using the basic TLBO, genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms.     

Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting Teaching–Learning-Based Optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.     

Learning–testing process in classroom: An empirical simulation model

This paper presents an empirical micro-simulation model of the teaching and the testing process in the classroom (Programs and sample data are available – the actual names of pupils have been hidden). It is a non-econometric micro-simulation model describing informational behaviors of the pupils, based on the observation of the pupils’ communication behavior during lessons and tests. The representation of the knowledge process is very simplified. However, we tried to study the involvements of individual motivation, capability and relationship with other pupils of each pupil, to compare them to the new-classical (and keynesian) and Austrian information and knowledge theoretical results. It is a first step and future development should concern expectation behaviors and dynamics. This paper aims too to give, we hope so, some criteria of pupils’ rationality in the classroom.     

Neuro-fuzzy predictive model for surface roughness and cutting force of machined Al–20 Mg2Si–2Cu metal matrix composite using additives

Neural Computing and Applications - Today’s metal matrix composites are widely used due to their excellent properties, which are useful for high-performance applications in the automotive and...     

Prediction and reduction of diesel engine emissions using a combined ANN–ACO method

In this study, the combination of artificial neural network (ANN) and ant colony optimization (ACO) algorithm has been utilized for modeling and reducing NO_x and soot emissions from a direct injection diesel engine. A feed-forward multi-layer perceptron (MLP) network is used to represent the relationship between the input parameters (i.e., engine speed, intake air temperature, rate of fuel mass injected, and power) on the one hand and the output parameters (i.e., NO_x and soot emissions) on the other hand. The ACO algorithm is employed to find the optimum air intake temperatures and the rates of fuel mass injected for different engine speeds and powers with the purpose of simultaneous reduction of NO_x and soot. The obtained results reveal that the ANN can appropriately model the exhaust NO_x and soot emissions with the correlation factors of 0.98, 0.96, respectively. Further, the employed ACO algorithm gives rise to 32% and 7% reduction in the NO_x and soot, respectively. The response time of the optimization process was obtained to be less than 4 min for the particular PC system used in the present work. The high accuracy and speed of the model show its potential for application in intelligent controlling systems of the diesel engines.     

Multi-objective hardware–software partitioning of embedded systems: A case study of JPEG encoder

Embedded systems have become integral parts of today's technology-based life, starting from various home appliances to satellites. Such a wide range of applications encourages for their economic design using optimization-based tools. The JPEG encoder is an embedded system, which is applied for obtaining high quality output from continuous-tone images. It has emerged in recent years as a problem of optimum partitioning of its various processes into hardware and software components. Realizing pairing and conflicting nature among its various cost terms, for the first time the JPEG encoder is formulated and partitioned here as a multi-objective optimization problem. A multi-objective binary-coded genetic algorithm is proposed for this purpose, whose effectiveness is demonstrated through the application to a real case study and a number of large-size hypothetical instances.     

Identification and spatio-temporal analysis of earthquake clusters using SOM–DBSCAN model

Neural Computing and Applications - Seismic catalogs are vital to understanding and analyzing the progress of active fault systems. The background seismicity rate in a seismic catalog, strongly...     

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Neural Computing and Applications - Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research,...     

Analysing wear behaviour of Al–CaCO3 composites using ANN and Sugeno-type fuzzy inference systems

Neural Computing and Applications - Design of experiment for the development of stir cast calcium carbonate-reinforced aluminium composite is a search for optimum combination of material and...     

Hierarchical optimization of boiler–turbine unit using fuzzy stable model predictive control

This paper develops a hierarchical control system structure based on the Takagi–Sugeno fuzzy model to achieve an optimal control of a boiler–turbine unit. In the upper layer of the hierarchy, an optimal reference governor is designed to find the optimal operating point. A disturbance term is introduced to the fuzzy model to lump the modeling mismatch and unknown disturbance. Thus, the effect of plant behavior variation can be removed and the operating point found can be feasible to control. In the lower layer, a stable model predictive controller is developed to track the optimal set-points while guaranteeing the input-to-state stability of the system. Fuzzy Lyapunov function and appropriate slack and collection matrices are used to reduce the conservatism of stability design and improve the performance. Through the estimation of the disturbance term using an observer, the two layers in the hierarchy are coupled and the integrated system can realize a dynamic optimal control of the boiler–turbine unit, even in the case of severe plant behavior variations.     

Structural topology and element sizing design optimisation of tall steel frameworks using a hybrid OC–GA method

This paper presents a hybrid optimisation method in which a local search operator based on a rigorously derived optimality criteria (OC) technique is embedded in the framework of a genetic algorithm (GA). The GA framework is particularly useful in the global exploration for optimal topologies, while the OC technique serves as a local search operator for efficient element sizing optimisation of given topologies. The hybrid OC–GA method was developed to strike a balance between the exploration of global search algorithms and the exploitation of efficient local search methods so as to make the hybrid method suitable for optimising tall building structures involving a large number of structural elements. The applicability and efficiency of the hybrid OC–GA method were tested with two 40-storey steel frameworks. The results show that the hybrid method can generate superior designs to pure GA while exhibiting rapid and smooth convergence, suggesting its great potential for optimising both structural form and element size of practical tall building structures.     

Travellers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories

Despite the rising influence of social media, the use of consumer-generated media (CGM) for the task of travel planning still meets with scepticism among certain online travel consumers. Hence the need to further explore the psychological factors underlying this aspect of online behaviour. The study proposes a model of consumer-generated media acceptance for the purpose of travel planning which integrates the Technology Acceptance Model with the Source Credibility Theory. Using an online survey of 661 valid responses and structural equation modelling, the findings highlight the critical factors relevant to the cognitive processes which determine online travellers’ affective and conative responses to the use of consumer-generated media for travel planning. The results suggest that integrating technology acceptance factors with that of information adoption can enhance the understanding of consumer-generated media usage in the vacation planning context. The study outcome holds implications for theory and practice.