Cyber-physical systems in the re-use, refurbishment and recycling of used electrical and electronic equipment

The aim of the research outlined in this paper is to demonstrate the implementation of a Cyber-Physical System (CPS) within the End of Life (EoL) processing of Electrical and Electronic Equipment (EEE). The described system was created by reviewing related areas of research, capturing stakeholder’s requirements, designing system components and then implementing within an actual EoL EEE processer. The research presented in this paper details user requirements, relevant to any EoL EEE processer, and provides information of the challenges and benefits of utilising CPSs systems within this domain. The system implemented allowed an EoL processer to attach passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tags to cores (i.e. mobile phones and other IT assets) upon entry to the facility allowing monitoring and control of the core’s refurbishment. The CPS deployed supported the processing and monitoring requirements of PAS 141:2011, a standard for the correct refurbishment of both used and waste EEE for reuse. The implemented system controls how an operator can process a core, informing them which process or processes should be followed based upon the quality of the core, the recorded results of previous testing and any repair efforts. The system provides Human-Computer Interfaces (HCIs) to aid the user in recording core and process information which is then used to make decisions on the additional processes required. This research has contributed to the knowledge of the advantages and challenges of CPS development, specifically within the EoL domain, and documents future research goals to aid EoL processing through more advanced decision support on a core’s processes

Developing augmented reality capabilities for industry 4.0 small enterprises: Lessons learnt from a content authoring case study

Augmented reality (AR) has been proposed as a disruptive and enabling technology within the Industry 4.0 manufacturing paradigm. The complexity of the AR content creation process results in an inability for Small Enterprise (SE)to create bespoke,flexibleARtraining support “in-house” and is a potential barrier to industrial adoption of AR. Presently, AR content creation requires a range of specialist knowledge (e.g. 3D modelling, interface design, programming and spatial tracking) and may involve infrastructure changes (e.g. fiducial markers, cameras) and disruption to workflow. The research reported in this paper concerns the development and deployment of an Augmented Repair Training Application (ARTA); a templatebased interface to support end user (shop floor) AR content creation. The proposed methodology and implementation are discussed and evaluated in a real-world industrial case study in collaboration with a Small Enterprise (SE) in the Used and Waste Electronic and Electrical Equipment sector (UEEE/WEEE). The need for end user friendly templates is presented in the conclusion alongside further related work

Modelling manufacturing processes using Markov chains

Optimizing manufacturing processes with inaccurate models of the process will lead to unre-liable results. This can be true when there is a strong human influence on the manufacturing process and many variable aspects. This study investigates modelling a manufacturing process influenced by human inter-action with very variable products being processed. To develop a more accurate process model for such pro-cesses radio frequency identification (RFID) tags can be used to track products through the process. The tags record information for each product and this data can be used to produce more accurate models of the manu-facturing process. The data produced has been used to create a Markov chain model. This model is used to predict future product paths for use in discrete event simulation. In this case an IT refurbishment company is used as a case study. RFID tags have been utilized to track the IT products moving through the refurbishment process and this information has been used to produce a Markov chain model

Comparison of in-sight and handheld navigation devices toward supporting industry 4.0 supply chains: First and last mile deliveries at the human level

Last (and First) mile deliveries are an increasingly important and costly component of supply chains especially those that require transport within city centres. With reduction in anticipated manufacturing and delivery timescales, logistics personnel are expected to identify the correct location (accurately) and supply the goods in appropriate condition (safe delivery). Moving towards more environmentally sustainable supply chains, the last/first mile of deliveries may be completed by a cyclist courier which could result in significant reductions in congestion and emissions in cities. In addition, the last metres of an increasing number of deliveries are completed on foot i.e. as a pedestrian. Although research into new technologies to support enhanced navigation capabilities is ongoing, the focus to date has been on technical implementations with limited studies addressing how information is perceived and actioned by a human courier. In the research reported in this paper a comparison study has been conducted with 24 participants evaluating two examples of state-of-the-art navigation aids to support accurate (right time and place) and safe (right condition) navigation. Participants completed 4 navigation tasks, 2 whilst cycling and 2 whilst walking. The navigation devices under investigation were a handheld display presenting a map and instructions and an in-sight monocular display presenting text and arrow instructions. Navigation was conducted in a real-world environment in which eye movements and device interaction were recorded using Tobii-Pro 2 eye tracking glasses. The results indicate that the handheld device provided better support for accurate navigation (right time and place), with longer but less frequent gaze interactions and higher perceived usability. The in-sight display supported improved situation awareness with a greater number of hazards acknowledged. The benefits and drawbacks of each device and use of visual navigation support tools are discussed

A data-driven simulation to support remanufacturing operations

Simulations are a vital component in developing smart manufacturing systems, predicting the behaviour of the manufacturing shop floor operations to support production planning, scheduling and maintenance decisions within manufacturing environments. However, simulations are often limited in their ability to support real-time business decisions in complex fast changing environments due to the cost and time required to build, update and maintain simulation models. Remanufacturing operations in particular could benefit from the use of simulations as a tool to support the assessment of different strategies to real-time scenarios due to the uncertain nature of product returns. This research develops a data-driven simulation approach to predict material flow behaviour within remanufacturing operations, by utilising data from digital manufacturing systems (i.e. databases, traceability systems, process plans) to update and automatically modify the simulation constructs to reflect the real world or planned system. A data-driven simulation is proposed comprising of three elements: (i) an adaptive remanufacturing simulation algorithm to model the complex material flow found within a remanufacturing process in a generic and reusable way, (ii) a remanufacturing information model to structure and highlight the simulation data requirements and (iii) an information service layer to collect and analyse sensor data for use within the simulation. The simulation is implemented to demonstrate how it can automatically reconfigure and adapt to changes within the data inputs (process and factory models) using a case study of operations from a Waste Electrical and Electronic Equipment (WEEE) remanufacturer, utilising data collected from a Radio Frequency Identification (RFID) traceability system installed within the remanufacturing facility

An industrial evaluation of an Industry 4.0 reference architecture demonstrating the need for the inclusion of security and human components

The research presented in this paper looks at evaluating RAMI4.0, a Research Architecture (RA) designed for Industry 4.0, through the representation of an existing Cyber-Physical System’s (CPSs) key functionality. The use case represented is that of a UK firm refurbishing End of Life (EoL) IT devices for business clients. EoL refurbishment is a domain with many complexities due to an inherent business model which results in varying quantities, types and conditions of received devices. These uncertainties can generally not be addressed until the devices have arrived in the facility and are inspected. RAs are an important tool used in system development to represent functionality, this representation should be high level and allow the easy communication of key concepts for not only client-to-developers and developer-to-developer but also either to an audience. An appropriate RA will help industrialists to understand what Industry 4.0 means to them (i.e. increased flexibility and control) and the functionality of any system potentially being invested in. The results of this research included two proposals for the extension of RAMI4.0 regarding the representation of security and humans within the systems. While Industry 4.0 focusses on CPSs this work also makes a further recommendation that the focus of modelling should be shifted to Cyber-Physical Human Systems (CPHSs) to ensure correct consideration of the humans within the system