Fundamentals of Modern Manufacturing: Materials, Processes and Systems

With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.

A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives

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Automation Production Systems And Computer Integrated Manufacturing

https://research.aalto.fi/files/36595757/CHEM_Kabugo_et_al_Industry_2020_ElePowEnrSyst.pdf

Industry 4.0 based process data analytics platform: A waste-to-energy plant case study

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information wit...

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2020.1859636

Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

Nowadays, governments and companies are looking for solutions to increase the collection level of various waste types by using new technologies and devices such as smart sensors, Internet of Things (IoT), cloud platforms etc. In order to fulfil this need, this paper presents solutions provided by a research project involving the design, development and implementation of fully automated waste collection systems with an increased usage degree, productivity and storage capacity. The paper will focus on the main results of this research project in turning the automated waste collection system into a smart system so that it can be easily integrated in any smart city infrastructure. For this purpose, the Internet of Things platform for the automated waste collection system provided by the project will allow real time monitoring and communication with central systems. Details about each module are sent to the central systems: various modules’ statuses (working, blocked, needs repairs or maintenance etc.); equipment status; storage systems status (allowing full reports for all waste types); the amount of waste for each module, allowing optimal discharging; route optimization for waste discharging etc. To do that, we describe here an IoT cloud solution integrating device connection, data processing, analytics and management.

https://www.mdpi.com/2071-1050/9/11/2064/pdf

Smart City Platform Development for an Automated Waste Collection System

Moving to Industry 4.0 involves the collection of massive amounts of data and the development of big data applications that can ensure a quick data flow between different systems, including massive amounts of data and information collection from smart sensors, and sending them to cloud applications that allow real-time data monitoring and processing. Securing and protecting the transmitted data represents a big issue to be discussed and resolved. In the paper, we propose a new method of data encoding and encryption for cloud applications using PNG format images. The proposed method is described in comparison with one of the classical methods of data encoding and transmission used currently. The paper includes a case study in which the proposed method was used to collect and transmit data from an automated waste collection system. The results show that the proposed method represents a secure, fast and efficient way to send and store the data in the cloud compared to the methods currently used. The proposed method is not limited to being used only in waste management but can be used successfully for any type of manufacturing system from smart factories.

An Innovative Industry 4.0 Cloud Data Transfer Method for an Automated Waste Collection System

The main goal of the paper is to present how CAD and the simulation results of a virtual model were used to develop and adapt an educational platform to various manufacturing scenarios in an Industrial Logistics laboratory optimizing the performances in terms of productivity. Our paper is divided in two parts. The first part describes the design and development of an educational platform containing an AS/RS (automated storage/retrieval system) system and a RGV (Rail-Guided Vehicle). All the phases of the platform development are presented, starting with 3D modelling, and ending with the platform testing and its integration in a manufacturing cell. The second part demonstrates the platform performance diagnosis and optimization in different functional scenarios using material flow simulation. The problems that occurred (when the platform becomes part of different types of manufacturing architectures) are analysed using the simulation reports diagnosis and a new simulation validates the optimization solutions. (Received in December 2014, accepted in May 2015. This paper was with the authors 1 month for 1 revision.)

Costel Emil Cotet

Papers

Smart City Platform Development for an Automated Waste Collection System

An Innovative Industry 4.0 Cloud Data Transfer Method for an Automated Waste Collection System

Using CAD and Flow Simulation for Educational Platform Design and Optimization

Material flow design and simulation for a glass panel recycling installation

Using Augmented Reality in Smart Manufacturing