A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals

Modeling of external cylindrical grinding process using T1 tool steel

The selection of the optimal external cylindrical grinding conditions importantly contributes to increase of productivity and quality of the products. The external cylindrical grinding is a method of finishing machine elements surface with an indeterminate blade shape. External cylindrical grinding can process surfaces that require high gloss and precision, although it can also be used to remove large surplus stock. Therefore, multi objective optimization for the external cylindrical grinding process is a problem with high complexity. In this study, an experimental study was performed to improve the productivity and quality of grinding process. By using the experimental date, the surface roughness, cutting force, and vibrations were modeled. To achieve the minimum value of surface roughness and maximum value of material removal rate, the optimal values of external cylindrical grinding conditions were determined by using the combination of Genetic Algorithms (GAs) and weighting method. The optimum values of surface roughness and material removal rate are 0.510 μm and 5.906 mm2/s, respectively. The obtained optimal values of cutting parameters were a feed rate of 0.3 mm/rev, a workpiece speed of 188.1 rpm, a cutting depth of 0.015 mm, and a workpiece Rockwell hardness of 54.78 HRC. The optimal values of cutting parameters, and workpiece hardness were successfully verified by comparing of experimental and predicted results. The approach method of this study can be applied in industrial machining to improve the productivity and quality of the products in external cylindrical grinding process of the T1 tool stee

A novel hybrid teaching learning based multi-objective particle swarm optimization

How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to promote the diversity and improve search ability. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence

Identification of continuous-time model of hammerstein system using modified multi-verse optimizer

his thesis implements a novel nature-inspired metaheuristic optimization algorithm, namely the modified Multi-Verse Optimizer (mMVO) algorithm, to identify the continuous-time model of Hammerstein system. Multi-Verse Optimizer (MVO) is one of the most recent robust nature-inspired metaheuristic algorithm. It has been successfully implemented and used in various areas such as machine learning applications, engineering applications, network applications, parameter control, and other similar applications to solve optimization problems. However, such metaheuristics had some limitations, such as local optima problem, low searching capability and imbalance between exploration and exploitation. By considering these limitations, two modifications were made upon the conventional MVO in our proposed mMVO algorithm. Our first modification was an average design parameter updating mechanism to solve the local optima issue of the traditional MVO. The essential feature of the average design parameter updating mechanism is that it helps any trapped design parameter jump out from the local optima region and continue a new search track. The second modification is the hybridization of MVO with the Sine Cosine Algorithm (SCA) to improve the low searching capability of the conventional MVO. Hybridization aims to combine MVO and SCA algorithms advantages and minimize the disadvantages, such as low searching capability and imbalance between exploration and exploitation. In particular, the search capacity of the MVO algorithm has been improved using the sine and cosine functions of the Sine Cosine Algorithm (SCA) that will be able to balance the processes of exploration and exploitation. The mMVO based method is then used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. Note that the structure of the linear and nonlinear subsystems is assumed to be known. Moreover, a continuous-time linear subsystem is considered in this study, while there are a few methods that utilize such models. Two numerical examples and one real-world application, such as the Twin Rotor System (TRS) are used to illustrate the efficiency of the mMVO-based method. Various nonlinear subsystems such as quadratic and hyperbolic functions (sine and tangent) are used in those experiments. Numerical and experimental results are analyzed to focus on the convergence curve of the fitness function, the parameter variation index, frequency and time domain response and the Wilcoxon rank test. For the numerical identifications, three different levels of white noise variances were taken. The statistical analysis value (mean) was taken from the parameter deviation index to see how much our proposed algorithm has improved. For Example 1, the improvements are 29%, 33.15% and 36.68%, and for the noise variances, 0.01, 0.25, and 1.0 improvements can be found. For Example 2, the improvements are 39.36%, 39.61% and 66.18%, and for noise variances, the improvements are by 0.01, 0.25 and 1.0, respectively. Finally, for the real TRS application, the improvement is 7%. The numerical and experimental results also showed that both Hammerstein model subsystems are defined effectively using the mMVO-based method, particularly in quadratic output estimation error and a differentiation parameter index. The results further confirmed that the proposed mMVObased method provided better solutions than other optimization techniques, such as PSO, GWO, ALO, MVO and SCA

An Algorithmic Framework for Multiobjective Optimization

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Studies on Some Aspects of Multi-objective Optimization: A Case Study of Electrical Discharge Machining Process

Electrical Discharge Machining (EDM) finds extensive application in manufacturing of dies, molds and critical parts used in the automobile and other industries. The present study investigates the effects of different electrodes, deep cryogenic treatment of tools subjected to different soaking duration and a hybrid approach of powder mixed EDM of cryogenically treated electrodes on machinability of Inconel 718 super alloy. Inconel 718 has been used as the work material owing to its extensive application in aerospace industries. A Box– Behnken design of response surface methodology (RSM) has been adopted to estimate the effect of machining parameters on the performance measures. The machining efficiency of the process is evaluated in terms of material removal rate (MRR), electrode wear ratio (EWR), surface roughness, radial overcut and white layer thickness which are function of process variables viz. open circuit voltage, discharge current, pulse-on-time, duty factor and flushing pressure. In this work, a novel multi-objective particle swarm optimization algorithm (MOPSO) has been proposed to get the Pareto-optimal solution. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence and to improve the solution quality. To avoid subjectiveness and impreciseness in the decision making, the Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory (MDT). Finally, a thermal model based on finite element method has been proposed to predict the MRR and tool wear rate (TWR) when work piece is machined with variety of electrode materials. A coupled thermo-structural model has been also proposed to estimate the residual stresses. The numerical models were validated through experimentations. Parametric study is carried out on the proposed model to understand the influence of important process parameters on the performance measures. The study offers useful insight into controlling the machining parameters to improve the machining efficiency of the EDMed components

On the crashworthiness performance of thin-walled energy absorbers: Recent advances and future developments

Over the past several decades, a noticeable amount of research efforts has been directed to minimising injuries and death to people inside a structure that is subjected to an impact loading. Thin-walled (TW) tubular components have been widely employed in energy absorbing structures to alleviate the detrimental effects of an impact loading during a collision event and thus enhance the crashworthiness performance of a structure. Comprehensive knowledge of the material properties and the structural behaviour of various TW components under various loading conditions is essential for designing an effective energy absorbing system. In this paper, based on a broad survey of the literature, a comprehensive overview of the recent developments in the area of crashworthiness performance of TW tubes is given with a special focus on the topics that emerged in the last ten years such as crashworthiness optimisation design and energy absorbing responses of unconventional TW components including multi-cells tubes, functionally graded thickness tubes and functionally graded foam filled tubes. Due to the huge number of studies that analysed and assessed the energy absorption behaviour of various TW components, this paper presents only a review of the crashworthiness behaviour of the components that can be used in vehicles structures including hollow and foam-filled TW tubes under lateral, axial, oblique and bending loading

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools

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