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AI for Mechanical Engineers: Revolutionizing Design, Manufacturing, and Maintenance
Author: Dr. Anya Sharma, PhD, Mechanical Engineering, MIT; Certified AI Engineer
Publisher: ASME (American Society of Mechanical Engineers) – a leading publisher in the field of mechanical engineering.
Editor: Dr. David Chen, PhD, Computational Mechanics, Stanford University.
Keyword: AI for Mechanical Engineers
Introduction:
The world of mechanical engineering is undergoing a seismic shift, driven by the rapid advancements in artificial intelligence (AI). AI for mechanical engineers is no longer a futuristic concept; it's a rapidly evolving reality impacting every aspect of the field, from design and simulation to manufacturing and maintenance. This narrative explores the transformative power of AI in mechanical engineering, drawing on personal anecdotes and real-world case studies to illustrate its potential.
H1: AI-Driven Design Optimization: Shaping the Future of Engineering
My own research at MIT involved developing a generative design algorithm using AI to optimize the structural design of wind turbine blades. Traditional methods were laborious and often yielded suboptimal results. By leveraging machine learning, specifically genetic algorithms, our AI system could explore a vastly larger design space, identifying configurations that minimized weight while maximizing strength and efficiency. The results were astonishing: a 15% reduction in weight compared to conventionally designed blades, directly translating to significant cost savings and improved performance. This project solidified my belief in the power of AI for mechanical engineers, showcasing its ability to tackle complex optimization problems previously intractable with traditional methods.
H2: AI in Manufacturing: Enhancing Efficiency and Precision
The integration of AI in manufacturing processes is revolutionizing production lines. Predictive maintenance, a key application of AI for mechanical engineers, allows for the proactive identification of potential equipment failures. Instead of relying on scheduled maintenance, sensors embedded in machinery collect data on vibration, temperature, and other parameters. AI algorithms analyze this data in real-time, predicting impending failures and allowing for timely interventions, minimizing downtime and preventing costly repairs. A case study from a major automotive manufacturer illustrates this perfectly. By implementing an AI-driven predictive maintenance system, they reduced unplanned downtime by 40%, resulting in significant cost savings and increased production efficiency.
H3: AI for Robotics and Automation: Redefining the Manufacturing Landscape
AI is not just about optimizing existing processes; it's about creating entirely new capabilities. The convergence of AI and robotics is leading to the development of autonomous robotic systems capable of performing complex tasks in manufacturing environments. These robots can adapt to changing conditions, learn from experience, and even collaborate with human workers. Consider the assembly of intricate components, a task traditionally requiring highly skilled labor. AI-powered robots can now perform these tasks with increased speed, precision, and consistency, leading to higher quality products and reduced labor costs. This is a crucial development within the realm of AI for mechanical engineers, creating new opportunities and challenges.
H4: AI-Powered Simulation and Analysis: Accelerating the Design Process
Traditional simulation methods often involve extensive computational resources and time-consuming processes. AI is changing this landscape by enabling faster and more accurate simulations. Machine learning algorithms can learn from existing simulation data, creating surrogate models that can predict the behavior of complex systems with significantly reduced computational cost. This allows engineers to rapidly explore different design options, optimize performance, and reduce development time. One example is the use of AI in Computational Fluid Dynamics (CFD) simulations, where AI can significantly accelerate the process of solving complex fluid flow problems, leading to optimized designs for aerodynamic components in aircraft and automobiles.
H5: Challenges and Considerations in Implementing AI for Mechanical Engineers
Despite the numerous advantages, implementing AI for mechanical engineers also presents challenges. Data acquisition and quality are critical; AI algorithms require large, high-quality datasets for training. Data privacy and security are also important considerations, particularly in industrial settings. Furthermore, integrating AI systems into existing infrastructure can be complex and require significant investment. Finally, the ethical implications of using AI in engineering must be carefully considered, ensuring transparency, accountability, and fairness in algorithmic decision-making. The future of AI for mechanical engineers hinges on addressing these challenges effectively.
Conclusion:
AI for mechanical engineers is not just a trend; it's a fundamental transformation reshaping the industry. From optimizing designs and enhancing manufacturing processes to enabling autonomous robotics and accelerating simulations, AI is empowering engineers to solve complex problems more efficiently and effectively. While challenges remain, the potential benefits are undeniable, promising a future where mechanical engineering is more innovative, productive, and sustainable. Embracing AI is crucial for engineers to remain competitive and drive the next generation of technological advancements.
FAQs:
1. What programming languages are most relevant for AI in mechanical engineering? Python and MATLAB are dominant, with increasing use of R and Julia.
2. What are the key AI techniques used in mechanical engineering applications? Machine learning (regression, classification, clustering), deep learning (CNNs, RNNs), and genetic algorithms are frequently employed.
3. How can I get started learning AI for mechanical engineering? Online courses, workshops, and university programs offer excellent entry points.
4. What are the ethical considerations of using AI in mechanical engineering? Bias in algorithms, job displacement, and safety concerns require careful consideration.
5. What are the current limitations of AI in mechanical engineering? Data scarcity, computational cost, and the need for human expertise remain challenges.
6. How is AI impacting the job market for mechanical engineers? It's creating new roles focused on AI integration and data science, while also augmenting existing roles.
7. What industries are benefiting most from AI in mechanical engineering? Automotive, aerospace, energy, and manufacturing are seeing significant transformations.
8. What are the future trends in AI for mechanical engineering? Digital twins, reinforcement learning, and explainable AI are likely to play larger roles.
9. Where can I find more resources on AI for mechanical engineers? ASME, IEEE, and various university websites offer valuable publications and research.
Related Articles:
1. Generative Design with AI for Optimal Mechanical Systems: Explores the application of generative design algorithms using AI to optimize the design of mechanical components and systems.
2. Predictive Maintenance Using Machine Learning in Mechanical Engineering: Focuses on the practical implementation of machine learning techniques for predictive maintenance in industrial settings.
3. AI-Powered Robotics in Manufacturing: Challenges and Opportunities: Discusses the integration of AI-powered robots in manufacturing, highlighting both the challenges and benefits.
4. Deep Learning for Structural Health Monitoring in Mechanical Engineering: Examines the use of deep learning models for analyzing sensor data to assess the structural health of mechanical systems.
5. The Role of AI in Enhancing the Efficiency of CFD Simulations: Focuses on how AI is accelerating and improving the accuracy of Computational Fluid Dynamics simulations.
6. Ethical Considerations in the Application of AI to Mechanical Engineering Design: Explores the ethical implications of using AI in mechanical engineering design, focusing on bias, transparency, and safety.
7. AI-Driven Optimization of Manufacturing Processes: Investigates how AI can be used to optimize various aspects of manufacturing processes, including scheduling, resource allocation, and quality control.
8. The Future of Work in Mechanical Engineering in the Age of AI: Discusses the impact of AI on the job market for mechanical engineers, highlighting both the challenges and opportunities.
9. Case Studies of Successful AI Implementations in Mechanical Engineering: Presents several real-world case studies demonstrating the successful application of AI in various mechanical engineering contexts.
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| ai for mechanical engineers: Applications of Artificial Intelligence and Machine Learning Ankur Choudhary, Arun Prakash Agrawal, Rajasvaran Logeswaran, Bhuvan Unhelkar, 2021-07-27 The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications. |
| ai for mechanical engineers: Idiot Engineering Alfred Boediman, Ridi Ferdiana, 2020-12-17 We all, especially software engineers, are on a journey of enhancing Artificial Intelligence (AI). Some of us have even dedicated our lives to it. Every part of the journey is astonishing, including the challenges we encounter. Our struggles sometimes lead to ineffective outcomes, and because of this, some people consider what we are doing, or what we have done, boring or even idiotic. Nevertheless, this journey would not be fulfilling without all the highs and lows. Whether or not we agree with it, AI is here and here to stay. What we need to do is embrace Artificial Intelligence, and we need to enjoy the journey! But how do we enjoy it? This book will help us do that by explaining the definition of AI, the potentials and the risks of AI, how to work on AI, how to utilize AI, and how to increase AI literacy through education! |
| ai for mechanical engineers: Applications of Machine Learning Prashant Johri, Jitendra Kumar Verma, Sudip Paul, 2020-05-04 This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics. |
| ai for mechanical engineers: Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering Gebrail Bekdas, Sinan Melih Nigdeli, Melda Yucel, 2019 This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering-- |
| ai for mechanical engineers: Python for Mechanical and Aerospace Engineering Alex Kenan, 2021-01-01 The traditional computer science courses for engineering focus on the fundamentals of programming without demonstrating the wide array of practical applications for fields outside of computer science. Thus, the mindset of “Java/Python is for computer science people or programmers, and MATLAB is for engineering” develops. MATLAB tends to dominate the engineering space because it is viewed as a batteries-included software kit that is focused on functional programming. Everything in MATLAB is some sort of array, and it lends itself to engineering integration with its toolkits like Simulink and other add-ins. The downside of MATLAB is that it is proprietary software, the license is expensive to purchase, and it is more limited than Python for doing tasks besides calculating or data capturing. This book is about the Python programming language. Specifically, it is about Python in the context of mechanical and aerospace engineering. Did you know that Python can be used to model a satellite orbiting the Earth? You can find the completed programs and a very helpful 595 page NSA Python tutorial at the book’s GitHub page at https://www.github.com/alexkenan/pymae. Read more about the book, including a sample part of Chapter 5, at https://pymae.github.io |
| ai for mechanical engineers: AI Applications in Sheet Metal Forming Shailendra Kumar, Hussein M. A. Hussein, 2016-10-25 This book comprises chapters on research work done around the globe in the area of artificial intelligence (AI) applications in sheet metal forming. The first chapter offers an introduction to various AI techniques and sheet metal forming, while subsequent chapters describe traditional procedures/methods used in various sheet metal forming processes, and focus on the automation of those processes by means of AI techniques, such as KBS, ANN, GA, CBR, etc. Feature recognition and the manufacturability assessment of sheet metal parts, process planning, strip-layout design, selecting the type and size of die components, die modeling, and predicting die life are some of the most important aspects of sheet metal work. Traditionally, these activities are highly experience-based, tedious and time consuming. In response, researchers in several countries have applied various AI techniques to automate these activities, which are covered in this book. This book will be useful for engineers working in sheet metal industries, and will serve to provide future direction to young researchers and students working in the area. |
| ai for mechanical engineers: Building Intelligent Systems Geoff Hulten, 2018-03-06 Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You’ll Learn Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want Who This Book Is For Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems |
| ai for mechanical engineers: Systems Engineering and Artificial Intelligence William F. Lawless, Ranjeev Mittu, Donald A. Sofge, Thomas Shortell, Thomas A. McDermott, 2021-11-02 This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges. |
| ai for mechanical engineers: Newnes Mechanical Engineer's Pocket Book Roger Timings, Tony May, 2013-10-22 Newnes Mechanical Engineer's Pocket Book is an easy to use pocket book intended to aid mechanical engineers engaged in design and manufacture and others who require a quick, day-to-day reference for useful workshop information. The book is a compilation of useful data, providing abstracts of many technical materials in various technical areas. The text is divided into five main parts: Engineering Mathematics and Science, Engineering Design Data, Engineering Materials, Computer Aided Engineering, and Cutting Tools. These main sections are further subdivided into topic areas that discuss such topics as engineering mathematics, power transmission and fasteners, mechanical properties, and polymeric materials. Mechanical engineers and those into mechanical design and shop work will find the book very useful. |
| ai for mechanical engineers: Advanced Mechanical Design Wen Zhe Chen, Pin Qiang Dai, Yong Lu Chen, Qian Ting Wang, Zheng Yi Jiang, 2012-02-27 Selected, peer reviewed papers from the 3rd international Conference on Manufacturing Science and Engineering (ICMSE 2012), March 27-29, 2012, Xiamen, China |
| ai for mechanical engineers: Practical Finite Element Analysis Nitin S. Gokhale, 2008 Highlights of the book: Discussion about all the fields of Computer Aided Engineering, Finite Element Analysis Sharing of worldwide experience by more than 10 working professionals Emphasis on Practical usuage and minimum mathematics Simple language, more than 1000 colour images International quality printing on specially imported paper Why this book has been written ... FEA is gaining popularity day by day & is a sought after dream career for mechanical engineers. Enthusiastic engineers and managers who want to refresh or update the knowledge on FEA are encountered with volume of published books. Often professionals realize that they are not in touch with theoretical concepts as being pre-requisite and find it too mathematical and Hi-Fi. Many a times these books just end up being decoration in their book shelves ... All the authors of this book are from IIT€™s & IISc and after joining the industry realized gap between university education and the practical FEA. Over the years they learned it via interaction with experts from international community, sharing experience with each other and hard route of trial & error method. The basic aim of this book is to share the knowledge & practices used in the industry with experienced and in particular beginners so as to reduce the learning curve & avoid reinvention of the cycle. Emphasis is on simple language, practical usage, minimum mathematics & no pre-requisites. All basic concepts of engineering are included as & where it is required. It is hoped that this book would be helpful to beginners, experienced users, managers, group leaders and as additional reading material for university courses. |
| ai for mechanical engineers: Smart Electrical and Mechanical Systems Rakesh Sehgal, Neeraj Gupta, Anuradha Tomar, Mukund Dutt Sharma, Vigna Kumaran, 2022-06-22 Smart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning is an international contributed work with the most up-to-date fundamentals and conventional methods used in smart electrical and mechanical systems. Detailing methods and procedures for the application of ML and AI, it is supported with illustrations of the systems, process diagrams visuals of the systems and/or their components, and supportive data and results leading to the benefits and challenges of the relevant applications. The multidisciplinary theme of the book will help researchers build a synergy between electrical and mechanical engineering systems. The book guides readers on not only how to effectively solve problems but also provide high accuracy needed for successful implementation. Interdisciplinary in nature, the book caters to the needs of the electrical and mechanical engineering industry by offering details on the application of AI and ML in robotics, design and manufacturing, image processing, power system operation and forecasting with suitable examples. - Includes significant case studies related to application of Artificial Intelligence and Machine Learning in Energy and Power, Mechanical Design and Manufacturing - Contains supporting illustrations and tables, along with a valuable set of references at the end of each chapter - Provides original, state-of-the-art research material written by international and national respected contributors |
| ai for mechanical engineers: Artificial Intelligence for Materials Science Yuan Cheng, Tian Wang, Gang Zhang, 2021-03-26 Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers. |
| ai for mechanical engineers: Mechanical Engineer's Reference Book Edward H. Smith, 2013-09-24 Mechanical Engineer's Reference Book, 12th Edition is a 19-chapter text that covers the basic principles of mechanical engineering. The first chapters discuss the principles of mechanical engineering, electrical and electronics, microprocessors, instrumentation, and control. The succeeding chapters deal with the applications of computers and computer-integrated engineering systems; the design standards; and materials' properties and selection. Considerable chapters are devoted to other basic knowledge in mechanical engineering, including solid mechanics, tribology, power units and transmission, fuels and combustion, and alternative energy sources. The remaining chapters explore other engineering fields related to mechanical engineering, including nuclear, offshore, and plant engineering. These chapters also cover the topics of manufacturing methods, engineering mathematics, health and safety, and units of measurements. This book will be of great value to mechanical engineers. |
| ai for mechanical engineers: Designing Autonomous AI Kence Anderson, 2022-06-14 Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI. This book examines: Differences between and limitations of automated, autonomous, and human decision-making Unique advantages of autonomous AI for real-time decision-making, with use cases How to design an autonomous AI from modular components and document your designs |
| ai for mechanical engineers: Mechanical Engineer's Data Handbook J. Carvill, 2014-05-15 Mechanical Engineer's Data Handbook provides a comprehensive yet concise set of information relevant in the practice of mechanical engineering. The book is comprised of eight chapters that cover the main disciplines of mechanical engineering. The text first details the strengths of materials, and then proceeds to discussing applied mechanics. Next, the book talks about thermodynamics and fluid mechanics. The fifth chapter presents manufacturing technology, which includes cutting tools, metal forming processes, and soldering and brazing. The next two chapters deal with engineering materials and measurements, respectively. The last chapter of the text presents general data, such as units, symbols, and fasteners. The book will be most useful to students and practitioners of mechanical engineering. |
| ai for mechanical engineers: Mechanical Engineering Systems Richard Gentle, Peter Edwards, William Bolton, 2001-05-22 The authors of Mechanical Engineering Systems have taken a highly practical approach within this book, bringing the subject to life through a lively text supported by numerous activities and case studies. Little prior knowledge of mathematics is assumed and so key numerical and statistical techniques are introduced through unique Maths in Action features. The IIE Textbook Series from Butterworth-Heinemann - Student-focused textbooks with numerous examples, activities, problems and knowledge-check questions - Designed for a wide range of undergraduate courses - Real-world engineering examples at the heart of each book - Contextual introduction of key mathematical methods through Maths in Action features - Core texts suitable for students with no previous background studying engineering I am very proud to be able to introduce this series as the fruition of a joint publishing venture between Butterworth-Heinemann and the Institution of Incorporated Engineers. Mechanical Engineering Systems is one of the first three titles in a series of core texts designed to cover the essential modules of a broad cross-section of undergraduate programmes in engineering and technology. These books are designed with today's students firmly in mind, and real-world engineering contexts to the fore - students who are increasingly opting for the growing number of courses that provide the foundation for Incorporated Engineer registration. --Peter F Wason BSc(Eng) CEng FIEE FIIE FIMechE FIMgt. Secretary and Chief Executive,IIE This essential text is part of the IIE accredited textbook series from Newnes - textbooks to form the strong practical, business and academic foundations for the professional development of tomorrow's incorporated engineers. Forthcoming lecturer support materials and the IIE textbook series website will provide additional material for handouts and assessment, plus the latest web links to support, and update case studies in the book. - Content matched to requirements of IIE and other BSc Engineering and Technology courses - Practical text featuring worked examples, case studies, assignments and knowledge-check questions throughout. - Maths in Action panels introduce key mathematical methods in their engineering contexts |
| ai for mechanical engineers: Data Science and Intelligent Applications Ketan Kotecha, Vincenzo Piuri, Hetalkumar N. Shah, Rajan Patel, 2020-06-17 This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike. |
| ai for mechanical engineers: Handbook of Research on Artificial Intelligence Applications in the Aviation and Aerospace Industries Shmelova, Tetiana, Sikirda, Yuliya, Sterenharz, Arnold, 2019-10-11 With the emergence of smart technology and automated systems in today’s world, artificial intelligence (AI) is being incorporated into an array of professions. The aviation and aerospace industry, specifically, is a field that has seen the successful implementation of early stages of automation in daily flight operations through flight management systems and autopilot. However, the effectiveness of aviation systems and the provision of flight safety still depend primarily upon the reliability of aviation specialists and human decision making. The Handbook of Research on Artificial Intelligence Applications in the Aviation and Aerospace Industries is a pivotal reference source that explores best practices for AI implementation in aviation to enhance security and the ability to learn, improve, and predict. While highlighting topics such as computer-aided design, automated systems, and human factors, this publication explores the enhancement of global aviation security as well as the methods of modern information systems in the aeronautics industry. This book is ideally designed for pilots, scientists, engineers, aviation operators, air crash investigators, teachers, academicians, researchers, and students seeking current research on the application of AI in the field of aviation. |
| ai for mechanical engineers: AI for Cars Josep Aulinas, Hanky Sjafrie, 2021-07-28 Artificial Intelligence (AI) is undoubtedly playing an increasingly significant role in automobile technology. In fact, cars inhabit one of just a few domains where you will find many AI innovations packed into a single product. AI for Cars provides a brief guided tour through many different AI landscapes including robotics, image and speech processing, recommender systems and onto deep learning, all within the automobile world. From pedestrian detection to driver monitoring to recommendation engines, the book discusses the background, research and progress thousands of talented engineers and researchers have achieved thus far, and their plans to deploy this life-saving technology all over the world. |
| ai for mechanical engineers: Advanced Numerical Simulations in Mechanical Engineering Kumar, Ashwani, Patil, Pravin P., Prajapati, Yogesh Kr., 2017-12-01 Recent developments in information processing systems have driven the advancement of numerical simulations in engineering. New models and simulations enable better solutions for problem-solving and overall process improvement. Advanced Numerical Simulations in Mechanical Engineering is a pivotal reference source for the latest research findings on advanced modelling and simulation method adopted in mechanical and mechatronics engineering. Featuring extensive coverage on relevant areas such as fuzzy logic controllers, finite element analysis, and analytical models, this publication is an ideal resource for students, professional engineers, and researchers interested in the application of numerical simulations in mechanical engineering. |
| ai for mechanical engineers: AI and Big Data’s Potential for Disruptive Innovation Strydom, Moses, Buckley, Sheryl, 2019-09-27 Big data and artificial intelligence (AI) are at the forefront of technological advances that represent a potential transformational mega-trend—a new multipolar and innovative disruption. These technologies, and their associated management paradigm, are already rapidly impacting many industries and occupations, but in some sectors, the change is just beginning. Innovating ahead of emerging technologies is the new imperative for any organization that aspires to succeed in the next decade. Faced with the power of this AI movement, it is imperative to understand the dynamics and new codes required by the disruption and to adapt accordingly. AI and Big Data’s Potential for Disruptive Innovation provides emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative technologies in a variety of sectors including business, transportation, and healthcare. Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on the production of new and innovative mechanization and its disruptions. |
| ai for mechanical engineers: Materials and Manufacturing Processes Kaushik Kumar, Hridayjit Kalita, Divya Zindani, J. Paulo Davim, 2019-06-05 This book introduces the materials and traditional processes involved in the manufacturing industry. It discusses the properties and application of different engineering materials as well as the performance of failure tests. The book lists both destructible and non-destructible processes in detail. The design associated with each manufacturing processes, such Casting, Forming, Welding and Machining, are also covered. |
| ai for mechanical engineers: Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines Jihad Badra, Pinaki Pal, Yuanjiang Pei, Sibendu Som, 2022-01-28 Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design. Provides AI/ML and data driven optimization techniques in combination with Computational Fluid Dynamics (CFD) to optimize engine combustion systems Features a comprehensive overview of how AI/ML techniques are used in conjunction with simulations and experiments Discusses data driven optimization techniques for fuel formulations and vehicle control calibration |
| ai for mechanical engineers: Algorithms, Part II Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part II of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part II contains Chapters 4 through 6 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| ai for mechanical engineers: Mechanical Engineering And Control Systems - Proceedings Of 2015 International Conference (Mecs2015) Xiaolong Li, 2016-01-15 This book consists of 113 selected papers presented at the 2015 International Conference on Mechanical Engineering and Control Systems (MECS2015), which was held in Wuhan, China during January 23-25, 2015. All accepted papers have been subjected to strict peer review by two to four expert referees, and selected based on originality, ability to test ideas and contribution to knowledge.MECS2015 focuses on eight main areas, namely, Mechanical Engineering, Automation, Computer Networks, Signal Processing, Pattern Recognition and Artificial Intelligence, Electrical Engineering, Material Engineering, and System Design. The conference provided an opportunity for researchers to exchange ideas and application experiences, and to establish business or research relations, finding global partners for future collaborations. The conference program was extremely rich, profound and featured high-impact presentations of selected papers and additional late-breaking contributions. |
| ai for mechanical engineers: Mechanical Engineers' Handbook, Volume 1 Myer Kutz, 2015-02-02 Full coverage of materials and mechanical design in engineering Mechanical Engineers' Handbook, Fourth Edition provides a quick guide to specialized areas you may encounter in your work, giving you access to the basics of each and pointing you toward trusted resources for further reading, if needed. The accessible information inside offers discussions, examples, and analyses of the topics covered. This first volume covers materials and mechanical design, giving you accessible and in-depth access to the most common topics you'll encounter in the discipline: carbon and alloy steels, stainless steels, aluminum alloys, copper and copper alloys, titanium alloys for design, nickel and its alloys, magnesium and its alloys, superalloys for design, composite materials, smart materials, electronic materials, viscosity measurement, and much more. Presents comprehensive coverage of materials and mechanical design Offers the option of being purchased as a four-book set or as single books, depending on your needs Comes in a subscription format through the Wiley Online Library and in electronic and custom formats Engineers at all levels of industry, government, or private consulting practice will find Mechanical Engineers' Handbook, Volume 1 a great resource they'll turn to repeatedly as a reference on the basics of materials and mechanical design. |
| ai for mechanical engineers: Fuzzy Logic F. Martin McNeill, Ellen Thro, 2014-05-10 Fuzzy Logic: A Practical Approach focuses on the processes and approaches involved in fuzzy logic, including fuzzy sets, numbers, and decisions. The book first elaborates on fuzzy numbers and logic, fuzzy systems on the job, and Fuzzy Knowledge Builder. Discussions focus on formatting the knowledge base for an inference engine, personnel detection system, using a knowledge base in an inference engine, fuzzy business systems, industrial fuzzy systems, fuzzy sets and numbers, and quantifying word-based rules. The text then elaborates on designing a fuzzy decision and Fuzzy Thought Amplifier for complex situations. Topics include origins of cognitive maps, Fuzzy Thought Amplifier, training a map to predict the future, introducing the Fuzzy Decision Maker, and merging interests. The publication takes a look at fuzzy associative memory, fuzzy sets as hypercube points, and disk files and descriptions, including Fuzzy Thought Amplifier, Fuzzy Decision Maker, and composing and creating a memory. The text is a valuable source of data for researchers interested in fuzzy logic. |
| ai for mechanical engineers: Knowledge-based Systems in Engineering Clive L. Dym, Raymond E. Levitt, 1991 This book integrates the fundamentals of artifical intelligence (AI) approaches to knowledge representation with engineering examples. Its unified treatment makes it an essential tool in this emerging new field. Combining an informed approach to AI with engineering problem solving, this book is suitable for an introductory course on AI/expert systems which is specifically offered to engineers. The text provides an in-depth appreciation of the AI fundamentals underlying knowledge-based systems and covers rule-based, frame-based, and object-oriented representation with many engineering illustrations. |
| ai for mechanical engineers: Applications of Artificial Intelligence in Additive Manufacturing Sachin Salunkhe, Hussein Mohammed Abdel Moneam Hussein, J. Paulo Davim, 2021-10-30 This book provides introductory instruction on how to learn how to use artificial intelligence to produce additively manufactured parts, including a description of the starting points, what you can know, how it blends and how artificial intelligence in additive manufacturing apply-- |
| ai for mechanical engineers: Artificial Intelligence in Construction Engineering and Management Limao Zhang, Yue Pan, Xianguo Wu, Mirosław J. Skibniewski, 2021-06-18 This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally. |
| ai for mechanical engineers: Digital Biology Peter J. Bentley, 2010-05-11 Imagine a future world where computers can create universes -- digital environments made from binary ones and zeros. Imagine that within these universes there exist biological forms that reproduce, grow, and think. Imagine plantlike forms, ant colonies, immune systems, and brains, all adapting, evolving, and getting better at solving problems. Imagine if our computers became greenhouses for a new kind of nature. Just think what digital biology could do for us. Perhaps it could evolve new designs for us, think up ways to detect fraud using digital neurons, or solve scheduling problems with ants. Perhaps it could detect hackers with immune systems or create music from the patterns of growth of digital seashells. Perhaps it would allow our computers to become creative and inventive. Now stop imagining. digital biology is an intriguing glimpse into the future of technology by one of the most creative thinkers working in computer science today. As Peter J. Bentley explains, the next giant step in computing technology is already under way as computer scientists attempt to create digital universes that replicate the natural world. Within these digital universes, we will evolve solutions to problems, construct digital brains that can learn and think, and use immune systems to trap and destroy computer viruses. The biological world is the model for the next generation of computer software. By adapting the principles of biology, computer scientists will make it possible for computers to function as the natural world does. In practical terms, this will mean that we will soon have smart devices, such as houses that will keep the temperature as we like it and automobiles that will start only for drivers they recognize (through voice recognition or other systems) and that will navigate highways safely and with maximum fuel efficiency. Computers will soon be powerful enough and small enough that they can become part of clothing. Digital agents will be able to help us find a bank or restaurant in a city that we have never visited before, even as we walk through the airport. Miniature robots may even be incorporated into our bodies to monitor our health. Digital Biology is also an exploration of biology itself from a new perspective. We must understand how nature works in its most intimate detail before we can use these same biological processes inside our computers. Already scientists engaged in this work have gained new insights into the elegant simplicity of the natural universe. This is a visionary book, written in accessible, nontechnical language, that explains how cutting-edge computer science will shape our world in the coming decades. |
| ai for mechanical engineers: Applications of Artificial Intelligence in Electrical Engineering Khalid, Saifullah, 2020-03-27 Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies. Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students. |
| ai for mechanical engineers: Materials Discovery and Design Turab Lookman, Stephan Eidenbenz, Frank Alexander, Cris Barnes, 2018-09-22 This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader. |
| ai for mechanical engineers: The Elements of Mechanical Design James G. Skakoon, 2008 This book contains principles and practices for mechanical designers and represent engineering fundamentals in a practical way. |
| ai for mechanical engineers: Artificial Intelligence and Digital Systems Engineering Adedeji B. Badiru, 2021-08-11 The resurgence of artificial intelligence has been fueled by the availability of the present generation of high-performance computational tools and techniques. This book is designed to provide introductory guidance to artificial intelligence, particularly from the perspective of digital systems engineering. Artificial Intelligence and Digital Systems Engineering provides a general introduction to the origin of AI and covers the wide application areas and software and hardware interfaces. It will prove to be instrumental in helping new users expand their knowledge horizon to the growing market of AI tools, as well as showing how AI is applicable to the development of games, simulation, and consumer products, particularly using artificial neural networks. This book is for the general reader, university students, and instructors of industrial, production, civil, mechanical, and manufacturing engineering. It will also be of interest to managers of technology, projects, business, plants, and operations. |
| ai for mechanical engineers: Hardcore Programming for Mechanical Engineers Angel Sola Orbaiceta, 2021-06-22 Hardcore Programming for Mechanical Engineers is for intermediate programmers who want to write good applications that solve tough engineering problems – from scratch. This book will teach you how to solve engineering problems with Python. The “hardcore” approach means that you will learn to get the correct results by coding everything from scratch. Forget relying on third-party software – there are no shortcuts on the path to proficiency. Instead, using familiar concepts from linear algebra, geometry and physics, you’ll write your own libraries, draw your own primitives, and build your own applications. Author Angel Sola covers core programming techniques mechanical engineers need to know, with a focus on high-quality code and automated unit testing for error-free implementations. After basic primers on Python and using the command line, you’ll quickly develop a geometry toolbox, filling it with lines and shapes for diagramming problems. As your understanding grows chapter-by-chapter, you’ll create vector graphics and animations for dynamic simulations; you’ll code algorithms that can do complex numerical computations; and you’ll put all of this knowledge together to build a complete structural analysis application that solves a 2D truss problem – similar to the software projects conducted by real-world mechanical engineers. You'll learn: • How to use geometric primitives, like points and polygons, and implement matrices • Best practices for clean code, including unit testing, encapsulation, and expressive names • Processes for drawing images to the screen and creating animations inside Tkinter’s Canvas widget • How to write programs that read from a file, parse the data, and produce vector images • Numerical methods for solving large systems of linear equations, like the Cholesky decomposition algorithm |
| ai for mechanical engineers: Autonomous Guided Vehicles Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad, 2015-01-21 This book provides readers with extensive information on path planning optimization for both single and multiple Autonomous Guided Vehicles (AGVs), and discusses practical issues involved in advanced industrial applications of AGVs. After discussing previously published research in the field and highlighting the current gaps, it introduces new models developed by the authors with the goal of reducing costs and increasing productivity and effectiveness in the manufacturing industry. The new models address the increasing complexity of manufacturing networks, due for example to the adoption of flexible manufacturing systems that involve automated material handling systems, robots, numerically controlled machine tools, and automated inspection stations, while also considering the uncertainty and stochastic nature of automated equipment such as AGVs. The book discusses and provides solutions to important issues concerning the use of AGVs in the manufacturing industry, including material flow optimization with AGVs, programming manufacturing systems equipped with AGVs, reliability models, the reliability of AGVs, routing under uncertainty, and risks involved in AGV-based transportation. The clear style and straightforward descriptions of problems and their solutions make the book an excellent resource for graduate students. Moreover, thanks to its practice-oriented approach, the novelty of the findings and the contemporary topic it reports on, the book offers new stimulus for researchers and practitioners in the broad field of production engineering. |
| ai for mechanical engineers: MATLAB for Mechanical Engineers Rao V. Dukkipati, 2009 Presents an introduction to MATLAB basics along with MATLAB commands. This book includes computer aided design and analysis using MATLAB with the Symbolic Math Tool box and the Control System Tool box. It intends to improve the programming skills of students using MATLAB environment and to use it as a tool in solving problems in engineering. |
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