This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework. - This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework.

This book reports on intelligent methods and solutions in engineering production and maintenance. It describes advanced tools for optimizing production processes, increasing their automation, safety and sustainability. Contributions cover different stages of the production process, such as product design, supply chain, and equipment maintenance and safety. This is one of the two volumes based on the 4th International Conference on Intelligent Systems in Production Engineering and Maintenance, ISPEM 2023, held on September 13-15, 2023, in Wroclaw, Poland.

In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials.

The book contains six chapters and covers topics dealing with biomedical applications of titanium alloys, surface treatment, relationships between microstructure and mechanical and technological properties, and the effect of radiation on the structure of the titanium alloys.

The topic of this book is Cold Spray technology. Cold Spray is a process of applying coatings by exposing a metallic or dielectric substrate to a high velocity (300 to 1200 m/s) jet of small (1 to 50 μm) particles accelerated by a supersonic jet of compressed gas. This process is based on the selection of the combination of particle temperature, velocity, and size that allows spraying at the lowest temperature possible. In the Cold Spray process, powder particles are accelerated by the supersonic gas jet at a temperature that is always lower than the melting point of the material, resulting in coating formation from particles in the solid state. As a consequence, the deleterious effects of high-temperature oxidation, evaporation, melting, crystallization, residual stresses, gas release, and other common problems for traditional thermal spray methods are minimized or eliminated. This book is the first of its kind on the Cold Spray process. Cold Spray Technology covers a wide spectrum of various aspects of the Cold Spray technology, including gas-dynamics, physics of interaction of high-speed solid particles with a substrate as well as equipment, technologies, and applications. Cold Spray Technology includes the results of more than 20 years of original studies (1984-2005) conducted at the Institute of Theoretical and Applied Mechanics of the Siberian Division of the Russian Academy of Science, as well as the results of studies conducted at most of the research centres around the world. The authors' goal is threefold. The first goal is to explain basic principles and advantages of the Cold Spray process. The second goal is, to give practical information on technologies and equipment. The third goal is to present the current state of research and development in this field over the world. The book provides coverage and data that will be of interest for users of Cold Spray technology as well as for other coating experts. At the present time the Cold Spray method is recognized by world leading scientists and specialists. A wide spectrum of research is being conducted at many research centres and companies in many countries. New approach to spray coatings Results are exceptionally pure coatings Low spray temperature without degradation of powder and substrate materials High productivity, high deposition efficiency High operational safety because of absence of high temperature gas jets, radiation and explosive gases Excellent thermal and electrical conductivity Wide spectrum of applications because of important advantages of the process

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers

This book is an attempt to provide a uni?ed methodology to derive models for fatigue life. This includes S-N, ?-N and crack propagation models. This is not a conventional book aimed at describing the fatigue fundamentals, but rather a book in which the basic models of the three main fatigue approaches, the stress-based, the strain-based and the fracture mechanics approaches, are contemplated from a novel and integrated point of view. On the other hand, as an alternative to the preferential attention paid to deterministic models based on the physical, phenomenological and empirical description of fatigue, their probabilistic nature is emphasized in this book, in which stochastic fatigue and crack growth models are presented. This book is the result of a long period of close collaborationbetween its two authors who, although of di?erent backgrounds, mathematical and mechanical, both have a strong sense of engineering with respect to the fatigue problem. When the authors of this book ?rst approached the fatigue ?eld in 1982 (twenty six years ago), they found the following scenario: 1. Linear, bilinear or trilinear models were frequently proposed by relevant laboratoriesandacademiccenterstoreproducetheW ̈ ohler?eld. Thiswas the case of well known institutions, which justi?ed these models based on clientrequirementsorpreferences. Thisledtotheinclusionofsuchmodels and methods as, for example, the up-and-down, in standards and o?cial practical directives (ASTM, Euronorm, etc.), which have proved to be unfortunate.