Industrial Technology: Sample Titles

Volume is indexed by Thomson Reuters CPCI-S (WoS)Collection of selected, peer reviewed papers from the 2013 International Forum on Materials Science and Industrial Technology (IFMSIT 2013), August 30 - September 1, 2013, Qingdao, China. The 264 papers are grouped as follows: I. Material Science, Advanced Chemical Engineering and Technologies; II. Industrial, Manufacturing Technology and Engineering; III. Intelligent Automation and Control, Electronics, Communications and Information Technology Applications; IV. Computational and Computer Science Technology, Algorithms; V. Management Science Engineering, Economics, Business, Logistics Applications; VI. Medicine and Health Engineering, Biotechnology; VII. Environmental Protection and Technology, Ecology and Resource Development.

Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm's productivity.

Occupational Safety and Health for Technologists, Engineers, and Managers provides a practical teaching resource preparing future safety and health professionals to practice in an age of rising global competition and technological change. In addition to covering OSHA standards and all core safety and health topics, Goetsch explores critical safety issues not covered in OSHA. The text suits postsecondary students of safety engineering, safety management, or occupational safety, plus students who need a comprehensive safety and health course.

The current volume is a collection of peer-reviewed papers presented at the 2nd Postgraduate Symposium on Industrial Science and Technology 2020 (SISTEC 2020, 26th and 27th August 2020, Malaysia). The SISTEC 2020 was explicitly dedicated to advancing industrial materials science and technology in related industries such as biotechnology, biomedicine, industrial chemistry, electronics, photonics, petrochemical, sensor, environment, and others.

This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights.

Industrial technologies have rapidly developed since the Industrial Revolution, and technological progress during the 19th century had been significantly promoted by the great inventors of that century including James Watt, Alfred Nobel and Thomas Edison. In the 20th century, progress was remarkably promoted by activities in corporate research laboratories and advanced products such as nylon and transistors were developed. Industrial products have progressed with the incremental improvements of conventional products while technological advances are associated with progress in science. Understanding human need and the scientific background around technologies are thus indispensable for further advancement in industrial product production. This book presents an overview of the essential concepts relating to industrial technology and is intended to provide essential information for engineers and researchers involved in research and development.

This easy-to-follow guide provides academics and industrial engineers with a state-of-the-art numerical approach to the most frequent technical and economical optimization methods. In an engaging manner, it provides the reader with not only a systematic and comprehensive study, but also with necessary and directly implementable code written in the versatile and readily available platform Matlab.

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.

Automation is undergoing a major transformation in scope and dimension and plays an increasingly important role in the global economy and in our daily lives. Engineers combine automated devices with mathematical and organizational tools to create complex systems for a rapidly expanding range of applications and human activities. This handbook incorporates these new developments and presents a widespread and well-structured conglomeration of new emerging application areas of automation. Besides manufacturing as a primary application of automation, the handbook contains new application areas such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. This Springer Handbook is not only an ideal resource for automation experts but also for people new to this expanding field such as engineers, medical doctors, computer scientists, designers. It is edited by an internationally renowned and experienced expert.