OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives

Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization

Carbon Fiber Reinforced Polymer (CFRP) composites are characterized by their excellent mechanical properties (high specific strength and stiffness, light weight, high damping capacity etc.) as compared to conventional metals, which results in their increased utilization especially for aircraft and aerospace applications, automotive, defense as well as sporting industries. With increasing applications of CFRP composites, determining economical techniques of production is very important. However, as compared to conventional metals, machining behavior of composites is somewhat different. This is mainly because these materials behave extremely abrasive during machining operations. Machining of CFRP appears difficult due to their material discontinuity, inhomogeneity and anisotropic nature. Moreover, the machining behavior of composites largely depends on the fiber form, the fiber content, fiber orientations of composites and the variability of matrix material. Difficulties are faced during machining of composites due to occurrence of various modes of damages like fiber breakage, matrix cracking, fiber–matrix debonding and delamination. Hence, adequate knowledge and in-depth understanding of the process behavior is indeed necessary to identify the most favorable machining environment in view of various requirements of process performance yields. In this context, present work attempts to investigate aspects of machining performance optimization during machining (turning and drilling) of CFRP composites. In case of turning experiments, the following parameters viz. cutting force, Material Removal Rate (MRR), roughness average (Ra) and maximum tool-tip temperature generated during machining have been considered as process output responses. In case of drilling, the following process performance features viz. load (thrust), torque, roughness average (of the drilled hole) and delamination factor (entry and exit both) have been considered. Attempt has been made to determine the optimal machining parameters setting that can simultaneously satisfy aforesaid response features up to the desired extent. Using Fuzzy Inference System (FIS), multiple response features have been aggregated to obtain an equivalent single performance index called Multi-Performance Characteristic Index (MPCI). A nonlinear regression model has been established in which MPCI has been represented as a function of the machining parameters under consideration. The aforesaid regression model has been considered as the fitness function, and finally optimized by evolutionary algorithms like Harmony Search (HS), Teaching-Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) etc. However, the limitation of these algorithms is that they assume a continuous search within parametric domain. These algorithms can give global optima; but the predicted optimal setting may not be possible to adjust in the machine/setup. Since, in most of the machines/setups, provision is given only to adjust factors (process input parameters) at some discrete levels. On the contrary, Taguchi method is based on discrete search philosophy in which predicted optimal setting can easily be achieved in reality.However, Taguchi method fails to solve multi-response optimization problems. Another important aspect that comes into picture while dealing with multi-response optimization problems is the existence of response correlation. Existing Taguchi based integrated optimization approaches (grey-Taguchi, utility-Taguchi, desirability function based Taguchi, TOPSIS, MOORA etc.) may provide erroneous outcome unless response correlation is eliminated. To get rid of that, the present work proposes a PCA-FuzzyTaguchi integrated optimization approach for correlated multi-response optimization in the context of machining CFRP composites. Application potential of aforementioned approach has been compared over various evolutionary algorithms

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

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

Ultrasonic assisted machining

A commercially available DMG MORI ULTRASONIC 65 monoBLOCK machining centre was installed in WMG in 2013 and has been primarily used to machine aerospace grade materials such as carbon fibre reinforced plastic (CFRP) and titanium alloy Ti 6Al-4V (individually and stacked) and 2000 / 6000 series aluminium alloys. Rather than discuss a single set of experimental work in detail, this paper discusses some of the issues that have been encountered when applying the technique of ultrasonic assisted machining (UAM) and some of the effects that have been observed using examples from the research conducted so far to illustrate some of the more important findings

Improved COA with Chaotic Initialization and Intelligent Migration for Data Clustering

A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages such as fast convergence rate, intelligent operators and simultaneous local and global search which are the motivations behind choosing this algorithm. In the Extended Cuckoo Algorithm, we have enhanced the operators in the classical version of the Cuckoo algorithm. The proposed operator of production of the initial population is based on a Chaos trail whereas in the classical version, it is based on randomized trail. Moreover, allocating the number of eggs to each cuckoo in the revised algorithm is done based on its fitness. Another improvement is in cuckoos’ migration which is performed with different deviation degrees. The proposed method is evaluated on several standard data sets at UCI database and its performance is compared with those of Black Hole (BH), Big Bang Big Crunch (BBBC), Cuckoo Search Algorithm (CSA), traditional Cuckoo Optimization Algorithm (COA) and K-means algorithm. The results obtained are compared in terms of purity degree, coefficient of variance, convergence rate and time complexity. The simulation results show that the proposed algorithm is capable of yielding the optimized solution with higher purity degree, faster convergence rate and stability in comparison to the other compared algorithms

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Оптимізація та підвищення ефективності планування цеху на основі генетичного алгоритму

Структура роботи. Магістерська робота містить 4 розділи з висновками до кожного з них, загальні висновки, список використаних джерел, який викладено на 88 сторінці тексту, включає 32 рисунки, 29 таблиць та 28 використаних джерел. Актуальність дослідження. Зі стрімким розвитком світової обробної промисловості конкуренція між машинобудівними компаніями ставала все більш жорсткою, а проблема необгрунтованого розташування цехів механічної обробки ставала все більш серйозною. Виробництво підприємств потребує підвищення ефективності виробництва. Щоб підвищити корпоративну ефективність, зменшити виробничі й експлуатаційні витрати та підвищити ефективність цеху, оптимізація компонування цеху стала актуальною проблемою, яку необхідно вирішити в машинобудівній промисловості. Мета й завдання дослідження . Виробнича система цехів є важливою складовою виробничої системи підприємства, а системи в різних типах цехів становлять виробничу систему підприємства. У цеху, як основної одиниці виробничої системи, компонування обладнання має важливий вплив на ефективність, вартість і використання площі виробництва. Грамотно влаштована система виробництва цеху може не тільки підвищити ефективність логістики та заощадити витрати на обробку матеріалів, але й максимально використовувати виробничі потужності в цеху, при цьому раціонально використовувати простір, покращити робоче середовище працівників та підвищити ефективність роботи. Завдання дослідження 1 ─ Провести детальний аналіз компонування цеху з обробки крильчатки відцентрового компресора та специфічної логістики виробництва. ─ За допомогою методу SLP отримати імітаційну схему попереднього макета цеху. ─ Оптимізувати компонування обладнання майстерні за допомогою генетичного алгоритму. ─ Використовуйте програмне забезпечення моделювання заводу для створення моделі компонування обладнання цеху. ─ Перевірити ефективність програми оптимізації моделювання. Методи дослідження Візуальне моделювання в середовищі Siemens Tecnomatix Plant Simulation 14.0, Попередній макет цеху отримано методом SLP, а оптимальний макет цеху – за допомогою генетичного алгоритму. Використовуйте програмне забезпечення Plant Simulation для перевірки моделювання. Наукова новизна отриманих результатів. Результати дослідження, проведені в рамках магістерської роботи, мають такі наукові новинки: ─ Використання CATIA для віртуального моделювання обробки робочого колеса відцентрового компресора ─ Використання методу SLP для моделювання макету майстерні ─ Використовуйте генетичні алгоритми для оптимізації макета семінару. ─ Використовуйте Plant Simulation для моделювання та оптимізації макета майстерні. Шляхом порівняльного аналізу отримано оптимальне розташування цеху обробки робочих колес відцентрового компресора. ─ У поєднанні зі статистичною функцією Tecnomatix Plant Simulation Публікації. 2 1. Сюхон Вей, Воронцов Б.С. Моделювання процесу автоматичної виробничої лінії на базі Tecnomatix / Wei Xuhong, Б.С. Воронцов // Молода наука - робота і нанотехнології сучасного машинобудування: зб. наук. праць Міжнар. молодіжної наук.техн. конф., 14-15 квітня 2021 р. – Краматорськ : ДДМА, 2021. – С.36-39. 2. Сюхон Вей, Воронцов Б.С. Дослідження нового типу відцентрового токарного кріплення / Wei Xuhong, Б.С. Воронцов // Комплексне забезпечення якості технологічних процесів та систем (КЗЯТПС – 2021): XІ Міжнар. наук.-практ. конф., 26-27 травня 2021 р.: тези доп. – Чернігів : НУ «Чернігівська політехніка», 2021. –Т. 1. – С. 29-30.Structure of work. The Master's Thesis contains 4 sections with conclusions to each of them, general conclusions, a list of sources used, which outlined in 88 pages of text, includes 32 figures, 29 tables and 28 used sources. Actuality of the research. With the rapid development of the global manufacturing industry, competition among machinery manufacturing companies has become increasingly fierce, and the problem of unreasonable layout of mechanical processing workshops has become more and more serious. The production of enterprises needs to improve production efficiency. In order to improve corporate efficiency, reduce production and operation costs, and increase Workshop efficiency, so the optimization of workshop layout has become an urgent problem to be solved in the machinery manufacturing industry. The purpose and objectives of the study . Workshop production system is an important component of the enterprise's manufacturing system, and the systems in different types of workshops constitute the enterprise's manufacturing system. In the workshop, as the most basic unit of the production system, the layout of the equipment has an important influence on the efficiency, cost and space utilization of production. A well-arranged workshop manufacturing system can not only improve logistics efficiency and save material handling costs, but also maximize the use of production facilities in the workshop, while rationally using space, improving the working environment of workers, and improving work efficiency. Research objectives: Carry out a detailed analysis of the layout of the centrifugal compressor impeller machining workshop and the specific production logistics. 4 ─ Using the SLP method to obtain a simulation diagram of the preliminary layout of the workshop. ─ Optimize the layout of workshop equipment through genetic algorithm. ─ Use Plant Simulation software to build the equipment layout model of the workshop. ─ Verify the effectiveness of the simulation optimization program. Research methods. Visual modeling in the environment of Siemens Tecnomatix Plant Simulation 14.0, The preliminary layout of the workshop is obtained through the SLP method, and the optimal layout of the workshop is obtained through the genetic algorithm. Use Plant Simulation software for simulation verification. Scientific novelty of the obtained results. The research results carried out as part of the master's degree thesis have the following scientific novelties: ─ Using CATIA for virtual simulation of centrifugal compressor impeller processing. ─ Using SLP method for workshop layout simulation. ─ Use genetic algorithms to optimize the layout of the workshop. ─ Use Plant Simulation to simulate and optimize the layout of the workshop. Through comparative analysis, the optimal layout of the centrifugal compressor impeller processing workshop is obtained. ─ Combined with the statistical function of Tecnomatix Plant Simulation. Publications. 1. Xuhong Wei, Vorontsov B.S. Process simulation of automatic workshop based on Tecnomatix / Wei Xuhong, Б.С. Воронцов // Молода наука - роботизація і нано- технології сучасного машинобудування: зб. наук. праць Міжнар. молодіжної наук.- техн. конф., 14-15 квітня 2021 р. – Краматорськ : ДДМА, 2021. – С.36-39. 5 2. Xuhong Wei, Vorontsov B.S. Research on a new type of centrifugal lathe fixture / Wei Xuhong, Б.С. Воронцов // Комплексне забезпечення якості технологічних процесів та систем (КЗЯТПС – 2021): XІ Міжнар. наук.-практ. конф., 26-27 травня 2021 р.: тези доп. – Чернігів : НУ «Чернігівська політехніка», 2021. – Т. 1. – С. 29-30

Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost