https://cronfa.swan.ac.uk/Record/cronfa55117/Download/55117__18125__8de5dc6470b743af9c986a7827a60118.pdf

Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges

Industry 4.0 sustainable supply chains: An application of an IoT enabled scrap metal management solution

Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises

The union between Industry 4.0 and the circular economy (CE) appears relatively recent. In this sense, new trading zones for sharing a common scenario among academics and practitioners are needed. The paper aims to investigate the link between Industry 4.0 and the CE by understanding how Industry 4.0 can foster the impact of the CE on companies. The study proposes a broader perspective that includes thematic and content analysis gathering data on professional documents based on business cases, newspaper articles, press releases and specialised blogs, as well as scientific papers. The joint academic‐practitioners view highlights how Industry 4.0 has the potential to impact on the CE through countless actions: increasing waste disposal; promoting remanufacturing; enhancing the efficiency of critical resources such as water, energy, gas and CO2; and improving business models and the mission of companies. However, barriers still exist in its adoption, stressing the need for holistic and integrated design and a proactive environment of collaboration among stakeholders. Results lead to practical as well as research implications.

Industry 4.0 and circular economy: An exploratory analysis of academic and practitioners' perspectives

Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants

Introducing an application of an industry 4.0 solution for circular supply chain management

In smart manufacturing, the automation of anomaly detection is essential for increasing productivity. Timeseries data from production processes are often complex sequences and their assessment involves many variables. Thus, anomaly detection with deep learning approaches is considered as an efficient and effective methodology. In this work, anomaly detection with deep autoencoders is examined. Three autoencoders are employed to analyze an industrial dataset and their performance is assessed. Autoencoders based on long short-term memory and convolutional neural networks appear to be the most promising. 

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Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset

Intelligent systems and applications on manufacturing domain aim to improve decision-making capabilities, ease complex decision problems, offer predictions related to maintenance activities and provide cost savings to companies. In order to support the aforementioned functionalities, the intelligent prediction and decision support systems are based on machine learning and signal processing techniques, AI algorithms, IoT devices, data mining and modeling techniques, rules and fuzzy logic systems, and advance visualizations. In this paper, we introduce an intelligent information management system that aims to provide predictive maintenance and enhance decision support in a leading lift manufacturer. The proposed solution is a decision support system equipped with analytic tools, IoT sensors and visualizations. The system supports the full cycle of polishing procedures of the lift manufacturer, as it starts from predictive maintenance during the polishing machines' operation and ends in the scrap metals' removal after the operation. Both the intelligent information system and the scenario of its usage in the lift manufacturer's shop floor are presented in this work.

Intelligent Information Management System for Decision Support: Application in a Lift Manufacturer's Shop Floor

In social robotic systems, robots interact with humans to collaborate for different tasks. In this paper we consider industrial scenarios, where a shop floor can be reconfigured and specific tasks can be assigned to robots that operate as assistants to human operators. We propose to use a Knowledge Representation approach to describe the human-robot interaction. In particular, the conceptual framework based on the Generalized World Entities paradigm is adopted to capture both the physical entities of the system and events, situations, behaviours as well as the relationships among them. The paper applies the methodologies to some real case studies of the Kleeman manufacturer to automated bending machine procedures and intra-shop floor transportation with automated guided vehicles. 

Angelos Papadopoulos

Papers

Industry 4.0 sustainable supply chains: An application of an IoT enabled scrap metal management solution

Introducing an application of an industry 4.0 solution for circular supply chain management

Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset

Intelligent Information Management System for Decision Support: Application in a Lift Manufacturer's Shop Floor

Sharing Semantic Knowledge for Autonomous Robots: Cooperation for Social Robotic Systems