This book provides a concept to analyze and increase the energy and resource efficiency of machining systems. Machining systems are widely used to produce workpieces in large quantities and with complex geometrical shapes. These systems, however, are also relevant in terms of energy and resource consumption, which is strongly connected to the choice of cutting fluid strategy. Within the focus of the concept, cutting fluid connects the elements of the machining system and results in interactions between them. Based on this description and an extensive literature review, a modeling approach is developed that comprises the relations between process parameters, cutting fluid strategies, and relevant machining system elements. The performance of the machining system is assessed with regard to environmental, economic as well as technological indicators and improved by various organizational and technical measures. The exemplary application of the developed concept is carried out in the context of two case studies and also indicates the corresponding effects of improvement measures.

This book provides essential information on environmentally benign/sustainable machining processes including innovations and developments in conventional machining, considering economy, safety, and productivity. Developments in machine tools, recent research on green lubricants and lubrication techniques, process hybridization, and the role of optimization techniques are discussed. Green machining of difficult-to-machine materials and composites is also explained with attempts towards making electric discharge and electrochemical machining technologies. Features: Covers up to date and latest information on environmentally benign machining technologies. Includes current approaches regarding the machinability properties of biomaterials, smart materials, and difficult-to-cut materials. Reviews theoretical understanding and practical aspects of using different technological approaches to attain sustainability in machining. Includes sustainability aspects for both conventional and modern machining. Aids industrial users in the optimum selection of machining parameters with regard to sustainability. This book is aimed at researchers and professionals in manufacturing and mechanical engineering, and sustainable processes.

Sustainable production automation, as an effective way to enable and expedite transitions to sustainability and enhance resource utilizations, attracts substantial efforts from researchers in both academy and industry. This book presents the recent development of innovative algorithms, models, heuristics, hardware and software in broad areas of sustainable production systems. It focuses on design, analysis and management of the processes involved in the product life cycle (from design to delivery to return) to have the minimal negative impacts on society (including environmental, economic and social). The contributors are experts from both universities and industrial research centers.

The 19th CIRP Conference on Life Cycle Engineering continues a strong tradition of scientific meetings in the areas of sustainability and engineering within the community of the International Academy for Production Engineering (CIRP). The focus of the conference is to review and discuss the current developments, technology improvements, and future research directions that will allow engineers to help create green businesses and industries that are both socially responsible and economically successful. The symposium covers a variety of relevant topics within life cycle engineering including Businesses and Organizations, Case Studies, End of Life Management, Life Cycle Design, Machine Tool Technologies for Sustainability, Manufacturing Processes, Manufacturing Systems, Methods and Tools for Sustainability, Social Sustainability, and Supply Chain Management.

The three volumes IFIP AICT 438, 439, and 440 constitute the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2014, held in Ajaccio, France, in September 2014. The 233 revised full papers were carefully reviewed and selected from 271 submissions. They are organized in 6 parts: knowledge discovery and sharing; knowledge-based planning and scheduling; knowledge-based sustainability; knowledge-based services; knowledge-based performance improvement, and case studies.

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

Over the last several years, manufacturers have expressed increasing interest in reducing their energy consumption and have begun to search for opportunities to reduce their energy usage. In this book, the authors explore a variety of opportunities to reduce the energy footprint of manufacturing. These opportunities cover the entire spatial scale of the manufacturing enterprise: from unit process-oriented approaches to enterprise-level strategies. Each chapter examines some aspect of this spatial scale, and discusses and describes the opportunities that exist at that level. Case studies demonstrate how the opportunity may be acted on with practical guidance on how to respond to these opportunities.

Energy efficiency represents a cost-effective and immediate strategy of a sustainable development. Due to substantial environmental and economic implications, a strong emphasis is put on the electrical energy requirements of machine tools for metalworking processes. The improvement of energy efficiency is however confronted with diverse barriers, which sustain an energy efficiency gap of unexploited potential. The deficiencies lie in the lack of information about the actual energy requirements of machine tools, a minimum energy reference to quantify improvement potential and the possible actions to improve the energy demand. Therefore, a comprehensive concept for energy performance management of machine tools is developed which guides the transition towards energy efficient machine tools. It is structured in four innovative concept modules, which are embedded into step-by-step workflow models. The capability of the performance management concept is demonstrated in an automotive manufacturing environment. The target audience primarily comprises researchers and practitioners challenged to enhance energy efficiency in manufacturing. The book may also be beneficial for graduate students who want to specialize in this field.