Investigation of degradation and upgradation models for flexible unit systems: a systematic literature review

Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.The project is funded by the Department of Science and Technology, Science & Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

Collaborative and intelligent networks and decision systems and services for supporting engineering and production management

Collaborative networks and systems (CNS) have received much attention in recent decades to reach a competitive advantage [...]This work was supported by national funds through the FCT-Fundacao para a Ciencia e Tecnologia, through the R&D Units Project Scopes: UIDB/00319/2020, UIDB/50014/2020, and EXPL/EME-SIS/1224/2021

Key enabling technologies, methodologies, frameworks, tools and techniques of smart and sustainable systems

For manufacturing systems, Industry 4.0 (I4.0) is currently considered a big challenge and is closely related to intelligent manufacturing or production, alongside a more or less wide set of technologies, methodologies, frameworks, tools and techniques. I4.0 encompasses a diversity of approaches to enable the progress of production systems, resulting in shortened production times, production efficiencies, product quality, customization and flexibility of performance. Due to the general awareness about the importance of enabling intelligent manufacturing alongside sustainable production, sustainability is gaining renewed importance. Due to the importance of the theme, there is a need for an updated review of the main enabling approaches of I4.0 for sustainable manufacturing systems. For this purpose, a literature review was conducted considering the research question: Is there increased attention being given to sustainability issues nowadays in Industry 4.0? Through this work, it was possible to verify the main I4.0 and sustainability pillars considered in academia, which for I4.0 are the integration of horizontal and vertical systems, with 94% of the relevant articles mentioning this pillar. The additive manufacturing and 3D printing was refered in 56% of the relevant articles, and for sustainability, the economic pillar was mentioned with 95%, being the main one, with a large difference from other factors.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

A systematic simulation-based multi-criteria decision-making approach for the evaluation of semi-fully flexible machine system process parameters

Current manufacturing system health management is of prime importance due to the emergence of recent cost-effective and -efficient prognostics and diagnostics capabilities. This paper investigates the most used performance measures viz. Throughput Rate, Throughput Time, System Use, Availability, Average Stay Time, and Maximum Stay Time as alternatives that are responsible for the diagnostics of manufacturing systems during real-time disruptions. We have considered four different configurations as criteria on which to test with the proposed integrated MCDM (Multi-Criteria Decision-Making)-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)-based simulation approach. The main objective of this proposed model is to improve the performance of semi–fully flexible systems and to maximize the production rate by ranking the parameters from most influenced to least. In this study, first, the performance of the considered process parameters are analyzed using a simulation approach, and furthermore the obtained results are validated using real-time experimental results. Thereafter, using an Entropy method, the weights of each parameter are identified and then the MCDM-based TOPSIS is applied to rank the parameters. The results show that Throughput tTme is the most affected parameter and that Availability, average stay time, and max stay time are least affected in the case of no breakdown of machine condition. Similarly, Throughput Time is the most affected parameter and Maximum Stay Time is the least affected parameter in the case of the breakdown of machine condition. Finally, the rankings from the TOPSIS method are compared with the PROMETHEE method rankings. The results demonstrate the ability to understand system behavior in both normal and uncertain conditions.This work has been, also, supported by the FCT within the RD Units Project Scope: UIDP/04077/2020 and UIDB/04077/2020

Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies