Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Sustainability-Based Expert System for Additive Manufacturing and CNC Machining

The development of technologies which enable resource efficient production is of paramount importance for the continued advancement of the manufacturing industry. In order to ensure a sustainable and clean energy future, manufacturers should be able to contrast and validate existing manufacturing technologies on a sustainability basis. In the post COVID-19 era of enterprise management, the use of artificial intelligence to simulate human expert decision making abilities will open new doors to achieving heightened levels of productivity and efficiency. The introduction of innovative technologies such as CNC machining and 3D printing to production systems has redefined the manufacturing landscape in a way that has compelled users to investigate into their sustainability. For the purposes of this study, cost effectiveness, energy and auxiliary material usage efficiency have been considered to be key indicators of manufacturing process sustainability. The objective of this research study is to develop a set of expert systems which can aid metal manufacturing facilities in selecting Binder Jetting, Direct Metal Laser Sintering or CNC Machining based on viable product, process, system parameters and inherent sustainability aspects. The expert systems have been developed using the knowledge automation software, Exsys CorvidÒ. Comprehensive knowledge bases pertaining to the objectives of each expert system have been created using literature reviews and communications with manufacturing experts. An interactive environment which mimics the expertise of a human expert has been developed by the application of suitable logical rules and backward chaining. The programs have been verified by analyzing and comparing the sustainability impacts of Binder Jetting and CNC Machining during fabrication of a stainless steel 316L component. According to the results of the study, Binder Jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC Machining, for fabrication of components feasible for each technology

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

A review

Funding Information: Radu Godina acknowledges Fundação para a Ciência e a Tecnologia ( FCT - MCTES) for its financial support via the project UIDP/00667/2020 and UIDB/00667/2020 (UNIDEMI). JPO acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020 , UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication – i3N. This activity has received funding from the European Institute of Innovation and Technology (EIT) – Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing . This body of the European Union receives support from the European Union's Horizon 2020 research and innovation programme. Publisher Copyright: © 2023 The AuthorsGrowing consciousness regarding the environmental impacts of additive manufacturing (AM) processes has led to research focusing on quantifying their environmental impacts using Life Cycle Assessment (LCA) methodology. The main objective of this paper is to review the state of the art of the existing LCA studies of AM processes. In this paper, a systematic literature review is carried out where a total of 77 papers focusing on LCA, including social-Life Cycle Assessment (S-LCA), are analyzed. Accordingly, the application of LCA methodology to different AM technologies was studied and different research themes such as the goal and scope of LCA studies, life cycle inventory data for different AM technologies, AM part quality and mechanical properties, the environmental, economic, and social performances of various AM technologies, and factors affecting AM´s sustainability potential were analyzed. Based on the critical analysis of the existing research, five major shortcomings of the existing research are realized: (i) some AM technologies are under studied; (ii) more focus only on the environmental sustainability dimension of AM, neglecting its economic and social dimensions; (iii) exclusion of AM pat quality and its mechanical performance from the sustainability assessment; (iv) not enough focus on the life cycle stages after product manufacture by AM; (v) effect of different product variables on AM´s sustainability not studied extensively. Lastly, based on these shortcomings realized, the following research directions for future works are suggested: (i) inclusion of new AM materials and technologies; (ii) transition to a triple-bottom-line sustainability assessment considering environmental, economic, and social dimensions of AM; (iii) extending the scope of LCA studies to post-manufacture stages of AM products; (iv) development of predictive environmental impact and cost models; (v) integration of quality and mechanical characterization with sustainability assessment of AM technologies.publishersversionpublishe

An integrated framework for non-traditional machining process technology selection in healthcare applications

In spite of continuous progress in technical advancement, the conventional machining process became unsatisfactory in the healthcare field due to its disadvantages. This inadequacy lead researchers to consider using the application of nontraditional machining that can machine extremely hard and brittle materials into complicated shapes such as medical devices and implants in healthcare. In this study, the three most popular nontraditional machining process technologies: Laser Beam Machining, Water Jet Machining, and Electrocautery are evaluated to determine the most appropriate technology using the Health Technology Assessment based Multi-criteria Decision-Making framework. HTA is organized evaluation of effects and properties of health technology that enables the application of systematic skills to solve a health problem. HTA's main goal is to raise awareness of new health technologies among decision makers. For these reasons, the HTA core model that enables the production of HTA-related information was utilized.The comparison of selected technologies was carried out via integrating the HTA core model, Best Worst, and Evaluation Based on Distance from Average Solution methods. Finally, a comparison was made to find the most suitable technology to create the necessary infrastructure. As a result, evaluation scores were computed as 0,673; 0,538 and 0,500 for WJM, LBM, and EC, respectively.Vedecká Grantová Agentúra MŠVVaŠ SR a SA