A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Special Session on Industry 4.0

No abstract available

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

On the role of Prognostics and Health Management in advanced maintenance systems

The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems’ condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.EU Framework Programme Horizon 2020, 645733 - Sustain-Owner - H2020-MSCA-RISE-201

Using discrete simulation to support internal logistics process design

The objective of this paper is to present the developments of an ongoing project that aims at implementing an internal materials movement system using autonomous vehicles for supporting logistics processes. In particular the project focus on the movement of final products, from assembly lines to the expedition warehouse and the supply of packaging materials (customer packages), from the raw materials warehouse to the assembly lines. This process is currently carried out by two stackers, in a milk-run environment and, in the context of this project, an innovative solution is proposed to improve the performance of the supply and pickup processes, transforming and optimizing an entirely manual process into an automated one. The main challenge was to investigate the impact on the existing internal logistic system of the new solution and, simultaneously, to explore if new management strategies are needed to satisfy both throughput levels and overall supply chain needs. A simulation model was developed using ARENA Simulation Software to get insights concerning the new challenges posed by the solution to be adopted. Results have revealed that the use of simulation allowed the assessment of the impact of the new internal logistics solution and played a critical role to identify the best dispatching rules to schedule transportation orders in such a complex system. This research shows the flexibility of the simulation tool to address several complex management issues simultaneously that arises in the context of innovative solutions aligned with industry 4.0 challenges.This work has been financially supported by the Portugal Incentive System for Research and Technological Development in scope of the projects in co-promotion nº 002814/2015 (iFACTORY 20152018).info:eu-repo/semantics/publishedVersio

Exploring the Role of Industry 4.0 on Global Logistics:Challenges in Big Data and IoT

The globalization of the economy and consumers' increasing demand for customized products have led to a rise in the complexity of global logistics chains. In response to these challenges, the integration of logistics with the Internet of Things (IoT) and Big Data, known as Logistics 4.0, offers opportunities for more efficient planning, control, and adaptation of logistics processes. Industry 4.0, with its emphasis on IoT and Big Data analytics, significantly influences the logistics sector, making it a pioneer in the digital transformation of the entire economy. This paper seeks to review and analyze the role and impacts of Industry 4.0 on global logistics, while also addressing the main challenges related to IT security, data protection, and successful implementation of Big Data Analytics in the supply chain. Additionally, it aims to explore the changes in work systems within companies due to digital transformation in logistics

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

Sustainable engineering challenges towards Industry 4.0: A comprehensive review

This article reviews Industry 4.0, its emerging phase, implementation, challenges, benefits, etc. It combines various fields where it has any influence and leaves some changes and where it requires some adaptation. Papers from the last 4 years are taken and analyzed, what is written about this topic in various countries with different backgrounds and economic development. Industry 4.0 affects the production environment by introducing new technologies which require a better-educated workforce so it affects education and requires some changes in curricula and ways of teaching. It brings new challenges and asks for a new approach from management to be able to handle fast and big changes in the business environment and to implement such innovation in production effectively