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AI in Mechanical Engineering: Revolutionizing Design, Manufacturing, and Maintenance
Author: Dr. Anya Sharma, PhD, PE – Professor of Mechanical Engineering and AI at the Massachusetts Institute of Technology (MIT) and leading researcher in AI-driven design optimization.
Publisher: Springer Nature – A leading global scientific publisher with a strong reputation for high-quality research publications in engineering and computer science. Springer Nature is known for its rigorous peer-review process and wide dissemination of scholarly works.
Editor: Dr. David Chen, PhD – Senior Editor at Springer Nature with over 15 years of experience in engineering publications and a specialization in advanced manufacturing techniques.
Keywords: AI in mechanical engineering, artificial intelligence, machine learning, mechanical engineering design, AI-driven manufacturing, predictive maintenance, digital twins, generative design, robotics, automation, CAD software, Industry 4.0
Abstract: This article explores the transformative potential of AI in mechanical engineering, examining its applications across design, manufacturing, and maintenance. We delve into the opportunities presented by AI, such as improved efficiency, enhanced product quality, and reduced costs. Simultaneously, we address the challenges, including data scarcity, algorithm bias, and ethical considerations. The integration of AI in mechanical engineering promises a future of smarter, more sustainable, and more resilient systems.
1. Introduction: The Rise of AI in Mechanical Engineering
The field of mechanical engineering is undergoing a significant transformation, driven by the rapid advancements in artificial intelligence (AI). AI in mechanical engineering is no longer a futuristic concept; it's a rapidly evolving reality, impacting every stage of the product lifecycle, from initial design to final disposal. This integration promises to revolutionize how we design, manufacture, and maintain mechanical systems, leading to improved efficiency, reduced costs, and enhanced product performance. This article provides a comprehensive overview of AI's role in mechanical engineering, examining both the exciting opportunities and the considerable challenges that lie ahead.
2. AI-Driven Design Optimization: Beyond Traditional CAD
Traditional Computer-Aided Design (CAD) software relies heavily on human expertise and iterative design processes. AI in mechanical engineering is changing this paradigm by enabling automated design optimization. Machine learning algorithms can analyze vast datasets of design parameters and performance characteristics to identify optimal designs that meet specific criteria, often surpassing human capabilities. This includes generative design, where AI algorithms explore a wide range of design possibilities, generating innovative solutions that might not have been considered by human engineers. The use of AI in mechanical engineering design leads to lighter, stronger, and more efficient products.
3. Revolutionizing Manufacturing with AI: Smart Factories and Robotics
The application of AI in mechanical engineering extends to the manufacturing process itself. Smart factories leverage AI-powered systems to optimize production lines, predict equipment failures, and improve overall efficiency. Robotics, integrated with AI, are becoming increasingly sophisticated, capable of performing complex tasks with greater precision and speed than their human counterparts. AI algorithms enable robots to adapt to changing conditions, learn from experience, and even collaborate with human workers, leading to a more flexible and efficient manufacturing environment. AI in mechanical engineering's impact on manufacturing is a cornerstone of Industry 4.0.
4. Predictive Maintenance: Minimizing Downtime and Maximizing Uptime
AI in mechanical engineering is proving invaluable in predictive maintenance. By analyzing sensor data from machinery, AI algorithms can identify patterns that indicate impending failures, allowing for proactive maintenance before catastrophic breakdowns occur. This minimizes downtime, reduces maintenance costs, and extends the lifespan of equipment. This is particularly crucial in industries with expensive and critical machinery where unplanned downtime can lead to significant financial losses. The implementation of AI in mechanical engineering for predictive maintenance significantly improves overall system reliability.
5. Digital Twins: Virtual Prototyping and Simulation
Digital twins are virtual representations of physical systems, created using sensor data and AI-powered simulations. AI in mechanical engineering facilitates the creation and utilization of digital twins, enabling engineers to test and optimize designs in a virtual environment before physical prototyping. This reduces development time, costs, and risks, while allowing for more thorough testing and validation of designs. AI algorithms can analyze data from digital twins to identify potential problems and optimize performance.
6. Challenges and Limitations of AI in Mechanical Engineering
Despite the significant potential of AI in mechanical engineering, several challenges need to be addressed. Data scarcity is a major hurdle, as AI algorithms require large datasets for effective training. Algorithm bias can lead to unfair or inaccurate results, requiring careful consideration of data selection and algorithm design. Ethical considerations, such as job displacement due to automation, must also be carefully addressed. Finally, the integration of AI systems into existing infrastructure can be complex and costly.
7. The Future of AI in Mechanical Engineering
The future of AI in mechanical engineering is bright. As AI technology continues to advance, we can expect even more sophisticated applications, leading to further improvements in design, manufacturing, and maintenance. The convergence of AI with other emerging technologies, such as the Internet of Things (IoT) and blockchain, will further accelerate the transformation of the field. The ongoing research and development in AI in mechanical engineering promise a future of smarter, more sustainable, and resilient mechanical systems.
8. Conclusion:
AI is rapidly transforming the landscape of mechanical engineering, offering unprecedented opportunities for innovation and efficiency. While challenges remain, the potential benefits of AI-driven design optimization, smart manufacturing, predictive maintenance, and digital twins are undeniable. By addressing the ethical and practical challenges, the mechanical engineering community can harness the power of AI to create a future of more sustainable, resilient, and efficient mechanical systems.
FAQs:
1. What are the most common AI algorithms used in mechanical engineering? Common algorithms include machine learning techniques like support vector machines (SVMs), neural networks (including deep learning), and decision trees, as well as optimization algorithms like genetic algorithms.
2. How can AI improve the safety of mechanical systems? AI can analyze sensor data to detect anomalies and predict potential failures, preventing accidents and improving overall system safety.
3. What are the ethical implications of AI in mechanical engineering? Ethical concerns include job displacement, algorithmic bias, and the responsible use of AI-powered systems.
4. What are the major barriers to widespread adoption of AI in mechanical engineering? Barriers include data scarcity, cost of implementation, lack of skilled workforce, and concerns about data security.
5. How can small and medium-sized enterprises (SMEs) benefit from AI in mechanical engineering? SMEs can leverage cloud-based AI services and readily available software tools to access the benefits of AI without significant upfront investment.
6. What role does data security play in AI applications in mechanical engineering? Data security is paramount, requiring robust cybersecurity measures to protect sensitive data and prevent unauthorized access.
7. What is the future of human-robot collaboration in mechanical engineering? We can expect increased collaboration between humans and AI-powered robots, with robots handling repetitive or dangerous tasks, and humans focusing on creative and strategic problem-solving.
8. How can AI contribute to sustainability in mechanical engineering? AI can optimize designs for energy efficiency, reduce waste in manufacturing, and improve the lifecycle management of products.
9. What are the key skills needed for mechanical engineers working with AI? Key skills include programming (Python, R), data analysis, machine learning fundamentals, and an understanding of AI algorithms and their applications in engineering.
Related Articles:
1. "Generative Design with AI: Optimizing Mechanical Components": This article explores the application of generative design algorithms to optimize the design of mechanical components, focusing on weight reduction, strength enhancement, and manufacturability.
2. "AI-Powered Predictive Maintenance in Manufacturing: A Case Study": This article presents a case study demonstrating the use of AI for predictive maintenance in a manufacturing setting, highlighting the reduction in downtime and cost savings.
3. "The Role of Machine Learning in Robotic Assembly": This article examines the application of machine learning techniques in improving the efficiency and accuracy of robotic assembly processes.
4. "Digital Twins in Mechanical Engineering: Enabling Virtual Prototyping": This article discusses the use of digital twins for virtual prototyping and simulation, enabling engineers to test and optimize designs before physical production.
5. "Addressing the Data Scarcity Challenge in AI-Driven Mechanical Design": This article explores methods for addressing the challenge of limited data availability in AI-driven mechanical design, including data augmentation and transfer learning.
6. "Ethical Considerations in the Deployment of AI in Mechanical Engineering": This article delves into the ethical implications of AI in mechanical engineering, focusing on issues such as bias, transparency, and accountability.
7. "The Impact of AI on the Future of Mechanical Engineering Jobs": This article examines the potential impact of AI on the job market for mechanical engineers, exploring the need for upskilling and reskilling.
8. "AI and the Circular Economy: Optimizing Product Lifecycle Management": This article discusses the role of AI in promoting circular economy principles in mechanical engineering, focusing on design for disassembly and material recovery.
9. "AI-Driven Automation in Manufacturing: Enhancing Flexibility and Efficiency": This article explores how AI-powered automation can lead to greater flexibility and efficiency in manufacturing processes, enhancing responsiveness to market demands.
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| ai in mechanical engineering: 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 in mechanical engineering: Applications of Artificial Intelligence in Engineering Xiao-Zhi Gao, Rajesh Kumar, Sumit Srivastava, Bhanu Pratap Soni, 2021-05-10 This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8–10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence. |
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| ai in mechanical engineering: 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 in mechanical engineering: 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 in mechanical engineering: 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 in mechanical engineering: 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 in mechanical engineering: 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 in mechanical engineering: Control Systems Jitendra R. Raol, Ramakalyan Ayyagari, 2019-07-12 Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional control system, chemical reactor, head-disk assembly, pitch control of an aircraft, yaw-damper control, helicopter control, and tidal power control. Decentralized control, game-theoretic control, and control of hybrid systems are discussed. Also, control systems based on artificial neural networks, fuzzy logic, and genetic algorithms, termed as AI-based systems are studied and analyzed with applications such as auto-landing aircraft, industrial process control, active suspension system, fuzzy gain scheduling, PID control, and adaptive neuro control. Numerical coverage with MATLAB® is integrated, and numerous examples and exercises are included for each chapter. Associated MATLAB® code will be made available. |
| ai in mechanical engineering: 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 in mechanical engineering: Software Engineering: Artificial Intelligence, Compliance, and Security Brian D'Andrade, 2021-02-16 Information security is important in every aspect of daily life. This book examines four areas where risks are present: artificial intelligence (AI), the internet of things (IoT), government and malware. The authors channel their experience and research into an accessible body of knowledge for consideration by professionals.AI is introduced as a tool for healthcare, security and innovation. The advantages of using AI in new industries are highlighted in the context of recent developments in mechanical engineering, and a survey of AI software risks is presented focusing on well-publicized failures and US FDA regulatory guidelines.The risks associated with the billions of devices that form the IoT grow with the availability of such devices in consumer products, healthcare, energy infrastructure and transportation. The risks, software engineering risk mitigation methods and standards promoting a level of care for the manufacture of IoT devices are examined because of their importance for software developers.Strategic insights for software developers looking to do business with the US federal government are presented, considering threats to both public and private sectors as well as governmental priorities from recent executive and legislative branch actions.Finally, an analysis of malicious software that infects numerous computer systems each day and causes millions of dollars in damages every year is presented. Malicious software, or malware, is software designed with hostile intent, but the damage may be mitigated with static and dynamic analyses, which are processes for studying how malware operates and analyzing potential impacts. |
| ai in mechanical engineering: 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 in mechanical engineering: 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 in mechanical engineering: 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 in mechanical engineering: Shigley's Mechanical Engineering Design ISE Richard Budynas, 2024-04-02 |
| ai in mechanical engineering: Watershed Management and Applications of AI Sandeep Samantaray, Abinash Sahoo, Dillip K. Ghose, 2021-05-16 Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations. Features: Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques Provides analysis of land erosion and sediment transport in watersheds from small to large scale Includes estimations for runoff using different methodologies with systematic approaches for each Discusses water harvesting and development of water yield catchments This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields. |
| ai in mechanical engineering: 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 in mechanical engineering: Artificial Intelligence in Industrial Decision Making, Control and Automation S.G. Tzafestas, H. B. Verbruggen, 2012-12-06 This book is concerned with Artificial Intelligence (AI) concepts and techniques as applied to industrial decision making, control and automation problems. The field of AI has been expanded enormously during the last years due to that solid theoretical and application results have accumulated. During the first stage of AI development most workers in the field were content with illustrations showing ideas at work on simple problems. Later, as the field matured, emphasis was turned to demonstrations that showed the capability of AI techniques to handle problems of practical value. Now, we arrived at the stage where researchers and practitioners are actually building AI systems that face real-world and industrial problems. This volume provides a set of twenty four well-selected contributions that deal with the application of AI to such real-life and industrial problems. These contributions are grouped and presented in five parts as follows: Part 1: General Issues Part 2: Intelligent Systems Part 3: Neural Networks in Modelling, Control and Scheduling Part 4: System Diagnostics Part 5: Industrial Robotic, Manufacturing and Organizational Systems Part 1 involves four chapters providing background material and dealing with general issues such as the conceptual integration of qualitative and quantitative models, the treatment of timing problems at system integration, and the investigation of correct reasoning in interactive man-robot systems. |
| ai in mechanical engineering: Artificial Intelligence in Engineering Design Bozzano G Luisa, 2012-12-02 Artificial Intelligence in Engineering Design is a three-volume edited collection of key papers from the field of AI and design, aimed at providing a state-of-the art description of the field, and focusing on how ideas and methods from artificial intelligence can help engineers in the design of physical artifacts and processes. The books survey a wide variety of applications in the areas of civil, chemical, electrical, computer, VLSI, and mechanical engineering. |
| ai in mechanical engineering: 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 in mechanical engineering: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| ai in mechanical engineering: 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 in mechanical engineering: Artificial Intelligence in Process Engineering Michael Mavrovouniotis, 2012-12-02 Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers. |
| ai in mechanical engineering: Artificial Intelligence in Engineering Robert A. Adey, Duvvuru Sriram, 1987-08-01 |
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