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Conference Presentations by lagouge TARTIBU
This paper deals with the optimization of an assembly model using the parametric variations provi... more This paper deals with the optimization of an assembly model using the parametric variations provided in stress analysis of the Autodesk Inventor software environment. More than ever before, designers operate in highly competitive environment. They must deal with competitive pressure and with conflicting demands from customers and regulatory bodies regarding their prototype functional performance, the environment and societal impact. This, in addition to the quick-time-to-market, forces them to develop products of increasing quality in even shorter time. As a result, designers are under pressure to increasingly limit the weight, the cost, while meeting all design targets for structural integrity and safety. To succeed in such challenging tasks, designers must make upfront decisions based on multi-attribute simulation directly performed on a parametric CAD model. A robot base assembly model is used to illustrate the approach. Finite element analysis (FEA) is used to minimize the mass of the robot base assembly while keeping displacement and stress within allowable range and considering safety criteria and profiles size changes. The development of this parametric optimization framework with a focus on the deployment of such CAD-based design approach constitutes the main contribution of this paper.
Papers by lagouge TARTIBU
Sustainability, Jul 28, 2021
The implementation of nano-additives in machining fluid is significant for manufacturers to attai... more The implementation of nano-additives in machining fluid is significant for manufacturers to attain a sustainable manufacturing process. The material removal rate (MRR) is a significant process of transforming solid raw materials into specific shapes and sizes. This process has many challenges due to friction, vibration, chip discontinuity when machining aluminum alloy, which has led to poor accuracy and affected the fatigue life of the developed material. It is worth noting that aluminum 8112 alloy is currently being applied in most engineering applications due to its lightweight-tostrength ratio compared to some other metals. This research aims to compare the effects of copra oilbased-titanium dioxide (TiO 2 )-and Multi-walled Carbon Nanotubes (MWCNTs)-nano-lubricant with cutting parameter interactions by conducting a study on MRR for advanced machining of aluminum 8112 alloys. The biodegradable nano-additive-lubricants were developed using two-step preparation techniques. The study employed a quadratic rotatable central composite design (QRCCD) to carry out the interaction study of the five machining parameters in the three lubrication environments on MRR. The results show that the copra-based-TiO 2 nano-lubricant increases the MRR by 7.5% and 16% than the MWCNTs and copra-oil-lubrication machining environments, respectively. In conclusion, the eco-friendly nano-additive-lubricant TiO 2 -Copra oil-based should be applied to manufacture machine parts for high entropy applications for sustainable production systems.
Revue d'intelligence artificielle, Apr 30, 2023
California bearing ratio (CBR) is an indispensable parameter in the design of road pavement, repe... more California bearing ratio (CBR) is an indispensable parameter in the design of road pavement, repeated carrying out of this test has been chiefly monotonous and time wasting, also the use of cement as stabilizer has also been increasingly expensive, hence, the need for admixing with agrowaste ash such as rice husk ash (RHA). This research is carried out for the prediction of the CBR of lateritic soil admixed with cement and RHA by means of an artificial neural network (ANN). Six parameters are selected as input variables to obtain results that are accurate and precise. The six input variables are cement, RHA, liquid limit, plasticity index, maximum dry density and optimum moisture content, while CBR Unsoaked and CBR Soaked are the output variables. The study consists of a database of 1288 samples obtained from laboratory experiments which were subdivided into 70% for training, 15% for testing, and 15% for validation. The training operation is performed by a multilayer perceptron-back propagation algorithm. The network topology is achieved after fixing a number of hidden neurons. Thereafter, statistical indices are used in evaluating the performance of the ANN model. It is established that this model is appropriate for accurate prediction of CBR results.
Procedia Manufacturing, 2019
Studies in systems, decision and control, 2023
Cervical cancer is one of the vital and most frequent cancers, but can be cured effectively if di... more Cervical cancer is one of the vital and most frequent cancers, but can be cured effectively if diagnosed in the early stage. This is a novel effort towards effective characterization of cervix lesions from contrast enhanced CT-Scan images to provide a reliable and objective discrimination between benign and malignant lesions. Since, feature selection is inherently multi-objective; this paper proposes two different approaches for multi-objective binary GWO algorithms. One is a scalarized approach to multi-objective GWO (MOGWO) and the other is a Non-dominated Sorting based GWO (NSGWO). The experimental results of presented approaches are compared with similar approaches such as Multiobjective Genetic Algorithm (GA), Multi-objective Firefly Algorithm (FA), Non-dominated Sorting based GA, Non-dominated Sorting based FA. With better diversification and intensification, GWO obtains Pareto solutions, which dominate the solutions obtained by GA and FA for the utilized cases. Cervix lesions are up to 91.1% accurately classified as benign and malignant with only five features selected by NSGWO. Although the methods have been applied for cervix lesion classification, it can be applied on other various domains. This has been verified by applying the methods on high-dimensional microarray gene expression datasets available online.
Multi-objective Ant Lion Optimizer for Improved Machining Performance
Studies in systems, decision and control, 2023
Mathematical modelling of engineering problems, Apr 28, 2022
Dynamic analysis and frequency response of cylindrical roller bearing of an airflow root blower
Cogent engineering, Feb 1, 2022
Cylindrical roller bearing is an important component of the airflow root blower of a power genera... more Cylindrical roller bearing is an important component of the airflow root blower of a power generation plant, and its malfunction has been identified as one of the root causes of poor quality demineralized water produced during operation. Hence, there is need to study the dynamic behaviour of a typical cylindrical roller bearing of an airflow root blower. In this study, the dynamic analysis of a cylindrical roller bearing subjected to different rotational speeds was simulated using finite element analysis software, Abaqus. The frequency response of the bearing was determined experimentally and analytically, and the modal frequency results obtained from both analyses were compared. The outcome of the dynamic analysis showed that the maximum temperature and Hertzian stress was developed on the outer ring of the bearing during operation, thus making this component most prone to failure. It was observed that the value of the temperature and stress developed increase with an increase in rotational speed. However, at a rotational speed greater than 503 rad/s, a drop in the Hertzian stress was developed due to the stress relaxation the bearing experience at the higher temperatures. A good agreement was obtained when the modal frequency of the frequency response obtained numerically was compared with those obtained experimentally.
A Review and Bibliometric Analysis of Sorting and Recycling of Plastic Wastes
International Journal of Design & Nature and Ecodynamics, Feb 28, 2023
Global plastic pollution is a negative impact on the environment as the production and use of pla... more Global plastic pollution is a negative impact on the environment as the production and use of plastic are increasing rapidly. Plastic recycling is a significant step towards a circular economy. Over the decades, much plastic has been in circulation for various applications. Recycling plastic wastes (PW) entails waste sorting using some physical properties including plastic types, colors, and shapes, to produce high-quality recycled plastics. Classification of PWs includes common plastic types: Polyethylene terephthalate (PET), High-density polyethylene (HDPE), Polyvinyl chloride (PVC), Low-density polyethylene (LDPE), Polypropylene (PP), Polystyrene (PS), and others. The traditional method of sorting PW achieves good accuracy but low throughput at an excessive cost. Automated processes in plastic sorting are developed to overcome this. This study analyzes automated sorting techniques and examines bibliometric data on plastic waste research over the past four decades. The Scopus database was used to retrieve statistics on the subject, which were then examined using the bibliometric program in the VOSviewer software. The data visualization was also carried out with VOSviewer. The results of this study can guide future research and provide crucial details to improve plastic waste management.
Development and Performance Assessment of a Hydraulic Hybrid System
The automotive industry has for some years considered hydraulic regeneration systems for use in h... more The automotive industry has for some years considered hydraulic regeneration systems for use in hybrid vehicles; combining the concurrent use of an internal combustion engine and a hydraulic system to reduce fuel consumption and increase performance. This study describes the development of a hydraulic hybrid system using a small passenger vehicle (SPV) model and a hydraulic hybrid pump/motor (P/M) model. A model of an SPV vehicle was developed and validated using MATLAB Simulink. Simulations were performed to analyse and test the performance of the hybrid system. This study addresses the gap related to the prediction of the performance of the hydraulic hybrid system for use on an SPV. A novel control system was developed to simulate the drive cycle and predict the fuel-saving of the hydraulic hybrid system. Variables included accumulator size, hydraulic P/M displacement and accumulator pre-charge, which were adjusted to optimise the hydraulic hybrid system. The Fuel consumption of the model before the implementation of the hybrid system was calibrated with the recorded fuel consumption of the test vehicle. The hydraulic hybrid system was then developed and implemented to the SPV model along with a revised control system. The fuel-saving of a novel hydraulic hybrid control was estimated using MATLAB Simulink. A controller was developed to manage the distribution of energy between the hydraulic system and the diesel engine. The effects of the hybrid system on the brakes and the engine demand were analyzed. The results indicated a 45% reduction in diesel engine demand and a 65% decrease in brake usage throughout the drive cycle. The model of the hydraulic hybrid passenger vehicle predicted a fuel saving of approximately 17%. This study shows that the hydraulic hybrid system can potentially improve fuel consumption and optimize engine performance in passenger vehicles.
Material Removal Rate Optimization Under ANN and QRCCD
Studies in systems, decision and control, 2023
Overview of Advanced Machining Process
Studies in systems, decision and control, 2023
Global Machining Prediction and Optimization
Studies in systems, decision and control, 2023
Application of Hybrid ANN and PSO for Prediction of Surface Roughness Under Biodegradable Nano-lubricant
Studies in systems, decision and control, 2023
A Multi-objective Optimization Approach for Improving Machining Performance Using the General Algebraic Modelling System (GAMS)
Studies in systems, decision and control, 2023
Development and Application of Nano-lubricant in Machining: A Review
Studies in systems, decision and control, 2023
Cutting Fluid and Its Application with Different Delivering Machining Techniques
Studies in systems, decision and control, 2023
Multi-objective Grasshopper Optimizer for Improved Machining Performance
Studies in systems, decision and control, 2023
Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness Under Biodegradable Nano-lubricant
Studies in systems, decision and control, 2023
This paper deals with the optimization of an assembly model using the parametric variations provi... more This paper deals with the optimization of an assembly model using the parametric variations provided in stress analysis of the Autodesk Inventor software environment. More than ever before, designers operate in highly competitive environment. They must deal with competitive pressure and with conflicting demands from customers and regulatory bodies regarding their prototype functional performance, the environment and societal impact. This, in addition to the quick-time-to-market, forces them to develop products of increasing quality in even shorter time. As a result, designers are under pressure to increasingly limit the weight, the cost, while meeting all design targets for structural integrity and safety. To succeed in such challenging tasks, designers must make upfront decisions based on multi-attribute simulation directly performed on a parametric CAD model. A robot base assembly model is used to illustrate the approach. Finite element analysis (FEA) is used to minimize the mass of the robot base assembly while keeping displacement and stress within allowable range and considering safety criteria and profiles size changes. The development of this parametric optimization framework with a focus on the deployment of such CAD-based design approach constitutes the main contribution of this paper.
Sustainability, Jul 28, 2021
The implementation of nano-additives in machining fluid is significant for manufacturers to attai... more The implementation of nano-additives in machining fluid is significant for manufacturers to attain a sustainable manufacturing process. The material removal rate (MRR) is a significant process of transforming solid raw materials into specific shapes and sizes. This process has many challenges due to friction, vibration, chip discontinuity when machining aluminum alloy, which has led to poor accuracy and affected the fatigue life of the developed material. It is worth noting that aluminum 8112 alloy is currently being applied in most engineering applications due to its lightweight-tostrength ratio compared to some other metals. This research aims to compare the effects of copra oilbased-titanium dioxide (TiO 2 )-and Multi-walled Carbon Nanotubes (MWCNTs)-nano-lubricant with cutting parameter interactions by conducting a study on MRR for advanced machining of aluminum 8112 alloys. The biodegradable nano-additive-lubricants were developed using two-step preparation techniques. The study employed a quadratic rotatable central composite design (QRCCD) to carry out the interaction study of the five machining parameters in the three lubrication environments on MRR. The results show that the copra-based-TiO 2 nano-lubricant increases the MRR by 7.5% and 16% than the MWCNTs and copra-oil-lubrication machining environments, respectively. In conclusion, the eco-friendly nano-additive-lubricant TiO 2 -Copra oil-based should be applied to manufacture machine parts for high entropy applications for sustainable production systems.
Revue d'intelligence artificielle, Apr 30, 2023
California bearing ratio (CBR) is an indispensable parameter in the design of road pavement, repe... more California bearing ratio (CBR) is an indispensable parameter in the design of road pavement, repeated carrying out of this test has been chiefly monotonous and time wasting, also the use of cement as stabilizer has also been increasingly expensive, hence, the need for admixing with agrowaste ash such as rice husk ash (RHA). This research is carried out for the prediction of the CBR of lateritic soil admixed with cement and RHA by means of an artificial neural network (ANN). Six parameters are selected as input variables to obtain results that are accurate and precise. The six input variables are cement, RHA, liquid limit, plasticity index, maximum dry density and optimum moisture content, while CBR Unsoaked and CBR Soaked are the output variables. The study consists of a database of 1288 samples obtained from laboratory experiments which were subdivided into 70% for training, 15% for testing, and 15% for validation. The training operation is performed by a multilayer perceptron-back propagation algorithm. The network topology is achieved after fixing a number of hidden neurons. Thereafter, statistical indices are used in evaluating the performance of the ANN model. It is established that this model is appropriate for accurate prediction of CBR results.
Procedia Manufacturing, 2019
Studies in systems, decision and control, 2023
Cervical cancer is one of the vital and most frequent cancers, but can be cured effectively if di... more Cervical cancer is one of the vital and most frequent cancers, but can be cured effectively if diagnosed in the early stage. This is a novel effort towards effective characterization of cervix lesions from contrast enhanced CT-Scan images to provide a reliable and objective discrimination between benign and malignant lesions. Since, feature selection is inherently multi-objective; this paper proposes two different approaches for multi-objective binary GWO algorithms. One is a scalarized approach to multi-objective GWO (MOGWO) and the other is a Non-dominated Sorting based GWO (NSGWO). The experimental results of presented approaches are compared with similar approaches such as Multiobjective Genetic Algorithm (GA), Multi-objective Firefly Algorithm (FA), Non-dominated Sorting based GA, Non-dominated Sorting based FA. With better diversification and intensification, GWO obtains Pareto solutions, which dominate the solutions obtained by GA and FA for the utilized cases. Cervix lesions are up to 91.1% accurately classified as benign and malignant with only five features selected by NSGWO. Although the methods have been applied for cervix lesion classification, it can be applied on other various domains. This has been verified by applying the methods on high-dimensional microarray gene expression datasets available online.
Multi-objective Ant Lion Optimizer for Improved Machining Performance
Studies in systems, decision and control, 2023
Mathematical modelling of engineering problems, Apr 28, 2022
Dynamic analysis and frequency response of cylindrical roller bearing of an airflow root blower
Cogent engineering, Feb 1, 2022
Cylindrical roller bearing is an important component of the airflow root blower of a power genera... more Cylindrical roller bearing is an important component of the airflow root blower of a power generation plant, and its malfunction has been identified as one of the root causes of poor quality demineralized water produced during operation. Hence, there is need to study the dynamic behaviour of a typical cylindrical roller bearing of an airflow root blower. In this study, the dynamic analysis of a cylindrical roller bearing subjected to different rotational speeds was simulated using finite element analysis software, Abaqus. The frequency response of the bearing was determined experimentally and analytically, and the modal frequency results obtained from both analyses were compared. The outcome of the dynamic analysis showed that the maximum temperature and Hertzian stress was developed on the outer ring of the bearing during operation, thus making this component most prone to failure. It was observed that the value of the temperature and stress developed increase with an increase in rotational speed. However, at a rotational speed greater than 503 rad/s, a drop in the Hertzian stress was developed due to the stress relaxation the bearing experience at the higher temperatures. A good agreement was obtained when the modal frequency of the frequency response obtained numerically was compared with those obtained experimentally.
A Review and Bibliometric Analysis of Sorting and Recycling of Plastic Wastes
International Journal of Design & Nature and Ecodynamics, Feb 28, 2023
Global plastic pollution is a negative impact on the environment as the production and use of pla... more Global plastic pollution is a negative impact on the environment as the production and use of plastic are increasing rapidly. Plastic recycling is a significant step towards a circular economy. Over the decades, much plastic has been in circulation for various applications. Recycling plastic wastes (PW) entails waste sorting using some physical properties including plastic types, colors, and shapes, to produce high-quality recycled plastics. Classification of PWs includes common plastic types: Polyethylene terephthalate (PET), High-density polyethylene (HDPE), Polyvinyl chloride (PVC), Low-density polyethylene (LDPE), Polypropylene (PP), Polystyrene (PS), and others. The traditional method of sorting PW achieves good accuracy but low throughput at an excessive cost. Automated processes in plastic sorting are developed to overcome this. This study analyzes automated sorting techniques and examines bibliometric data on plastic waste research over the past four decades. The Scopus database was used to retrieve statistics on the subject, which were then examined using the bibliometric program in the VOSviewer software. The data visualization was also carried out with VOSviewer. The results of this study can guide future research and provide crucial details to improve plastic waste management.
Development and Performance Assessment of a Hydraulic Hybrid System
The automotive industry has for some years considered hydraulic regeneration systems for use in h... more The automotive industry has for some years considered hydraulic regeneration systems for use in hybrid vehicles; combining the concurrent use of an internal combustion engine and a hydraulic system to reduce fuel consumption and increase performance. This study describes the development of a hydraulic hybrid system using a small passenger vehicle (SPV) model and a hydraulic hybrid pump/motor (P/M) model. A model of an SPV vehicle was developed and validated using MATLAB Simulink. Simulations were performed to analyse and test the performance of the hybrid system. This study addresses the gap related to the prediction of the performance of the hydraulic hybrid system for use on an SPV. A novel control system was developed to simulate the drive cycle and predict the fuel-saving of the hydraulic hybrid system. Variables included accumulator size, hydraulic P/M displacement and accumulator pre-charge, which were adjusted to optimise the hydraulic hybrid system. The Fuel consumption of the model before the implementation of the hybrid system was calibrated with the recorded fuel consumption of the test vehicle. The hydraulic hybrid system was then developed and implemented to the SPV model along with a revised control system. The fuel-saving of a novel hydraulic hybrid control was estimated using MATLAB Simulink. A controller was developed to manage the distribution of energy between the hydraulic system and the diesel engine. The effects of the hybrid system on the brakes and the engine demand were analyzed. The results indicated a 45% reduction in diesel engine demand and a 65% decrease in brake usage throughout the drive cycle. The model of the hydraulic hybrid passenger vehicle predicted a fuel saving of approximately 17%. This study shows that the hydraulic hybrid system can potentially improve fuel consumption and optimize engine performance in passenger vehicles.
Material Removal Rate Optimization Under ANN and QRCCD
Studies in systems, decision and control, 2023
Overview of Advanced Machining Process
Studies in systems, decision and control, 2023
Global Machining Prediction and Optimization
Studies in systems, decision and control, 2023
Application of Hybrid ANN and PSO for Prediction of Surface Roughness Under Biodegradable Nano-lubricant
Studies in systems, decision and control, 2023
A Multi-objective Optimization Approach for Improving Machining Performance Using the General Algebraic Modelling System (GAMS)
Studies in systems, decision and control, 2023
Development and Application of Nano-lubricant in Machining: A Review
Studies in systems, decision and control, 2023
Cutting Fluid and Its Application with Different Delivering Machining Techniques
Studies in systems, decision and control, 2023
Multi-objective Grasshopper Optimizer for Improved Machining Performance
Studies in systems, decision and control, 2023
Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness Under Biodegradable Nano-lubricant
Studies in systems, decision and control, 2023
Cutting Force Optimization Under ANN and QRCCD
Studies in systems, decision and control, 2023
Studies in Computational Intelligence (SCI, volume 1184), 2025
Preface Optimization lies at the heart of engineering, driving the design of systems that are not... more Preface
Optimization lies at the heart of engineering, driving the design of systems that are not only efficient but also innovative and robust. By identifying optimal solutions within given constraints, engineers can enhance the performance of systems in ways that were previously unattainable. This book is a comprehensive guide to engineering optimization, covering fundamental principles, advanced methods, and cutting-edge applications that span multiple domains within engineering.
In recent years, advancements in optimization have introduced an array of tools, from classical approaches to metaheuristics, capable of tackling highly complex problems across fields as diverse as manufacturing, renewable energy, and thermal systems. By exploring the applications of these techniques in real-world case studies, this book equips readers with both the theoretical and practical skills needed to approach modern engineering challenges.
Chapter 1 provides a thorough foundation in engineering optimization theory, beginning with the key elements of optimization, such as objective functions, decision variables, constraints, and the concept of the feasible region. Readers are introduced to optimality conditions that guide the selection of solutions, followed by a comprehensive overview of classical and modern optimization techniques, including heuristic and metaheuristic methods. This chapter also covers sensitivity analysis, which is crucial for understanding the impact of variations in input parameters on outcomes, and concludes with an introduction to multi-objective optimization.
Following this foundational overview, each subsequent chapter applies a specific optimization algorithm to a practical engineering scenario, allowing readers to see these concepts in action. For instance, Chapter 2 explores the optimization of machining performance using the Harris Hawk Optimization algorithm, showcasing its ability to handle multi-objective optimization challenges.
In Chapter 3, the Whale Optimization Algorithm is applied to enhance natural convection in a triangular chamber, demonstrating how evolutionary algorithms can manage complex thermal dynamics. These case studies illustrate how modern algorithms can be customized and fine-tuned to address specific needs, from performance improvements to energy efficiency.
Chapter 4 focuses on the optimization of parabolic trough collectors in solar energy systems, utilizing the Grey Wolf Optimization algorithm. This case highlights the application of bio-inspired algorithms in renewable energy, showcasing their effectiveness in maximizing energy capture while balancing design constraints.
Chapter 5 introduces the Sunflower Optimization algorithm to optimize the design of honeycomb heat sinks, which are essential components in thermal management. This chapter emphasizes the importance of optimizing heat transfer and efficiency in cooling systems, providing valuable insights for applications in electronics and power systems.
Chapter 6 examines the optimization of a small-sized solar PV-T (photovoltaic-thermal) water collector, using the Imperialistic Competitive Algorithm. This chapter highlights the application of optimization techniques to hybrid energy systems, where multi-objective optimization can significantly enhance both power generation and thermal efficiency.
Chapter 7 addresses the multi-objective optimization of a multi-channel cold plate under intermittent pulsating flow using the Jaya Algorithm. This case study explores the optimization of thermal management systems for applications requiring precise temperature control, such as electronics cooling and HVAC systems.
In Chapter 8, the Thermal Exchange Optimization Algorithm is applied to the optimization of a rectangular microchannel heat sink. This chapter demonstrates how optimized microchannel designs can improve heat dissipation in compact devices, highlighting applications in high-density electronics and microfluidics.
Chapter 9 presents a study on the optimization of a solar-driven generation plant using the Grasshopper Optimization Algorithm. This chapter focuses on maximizing the efficiency of solar-based power systems, balancing constraints related to environmental factors and energy storage.
Chapter 10 explores the optimization of cutting parameters and tool geometry using the Cuckoo Search Algorithm. By improving machining efficiency and tool performance, this chapter provides practical insights into manufacturing optimization and cost-effective machining processes.
In addition to showcasing advanced algorithms, this book addresses comparative optimization methods. Each case study includes a comparison between the primary algorithm and alternative approaches, such as the Epsilon constraint method, offering insights into the relative advantages and limitations of each approach. These comparisons provide readers with valuable guidance for selecting the most appropriate algorithm based on the unique requirements of their own projects.
Chapter 11 provides a forward-looking perspective on the future of engineering optimization, discussing emerging trends and the challenges posed by increasingly complex optimization tasks. The chapter explores the latest developments in metaheuristic optimization, addressing topics such as constrained optimization, multi-objective optimization, and the integration of advanced algorithms in engineering contexts. This chapter serves as a valuable resource for those interested in the ongoing evolution of optimization techniques and their expanding role in engineering.
To assist readers with practical implementation, the appendices include comprehensive MATLAB and GAMS models corresponding to each chapter. These resources offer a solid foundation for experimenting with the algorithms and concepts discussed, and they enable readers to apply these models to their own projects, enhancing the book's practical utility.
This book is intended for engineers, researchers, and advanced students seeking to deepen their understanding of optimization in engineering. By bridging theory and application, it provides a holistic view of optimization, demonstrating both the fundamental principles that underpin the field and the advanced methods that drive innovation in today’s engineering landscape. Whether your focus is on designing efficient energy systems, improving manufacturing processes, or optimizing thermal management solutions, this book offers valuable insights and tools to help you achieve your goals.
In a world where engineering solutions must increasingly balance performance, cost, and sustainability, optimization remains a crucial element in achieving success. I hope that this book serves as a guide and inspiration for engineers and researchers, empowering them to leverage optimization as a powerful tool for tackling the complex challenges of modern engineering.
Prof. Lagouge K. Tartibu
Professor of Mechanical engineering
Department of Mechanical and Industrial Engineering Technology
University of Johannesburg
South Africa