Remote real-time collaboration through synchronous exchange of digitised human-workpiece interactions

In this highly globalised manufacturing ecosystem, product design and verification activities, production and inspection processes, and technical support services are spread across global supply chains and customer networks. Therefore, collaborative infrastructures that enable global teams to collaborate with each other in real-time in performing complex manufacturing-related tasks is highly desirable. This work demonstrates the design and implementation of a remote real-time collaboration platform by using human motion capture technology powered by infrared light based depth imaging sensors and a synchronous data transfer protocol from computer networks. The unique functionality of the proposed platform is the sharing of physical contexts during a collaboration session by not only exchanging human actions but also the effects of those actions on the workpieces and the task environment. Results show that this platform could enable teams to remotely work on a common engineering problem at the same time and also get immediate feedback from each other making it valuable for collaborative design, inspection and verifications tasks in the factories of the future. An additional benefit of the implemented platform is its use of low cost off the shelf equipment thereby making it accessible to SMEs that are connected to larger organisations via complex supply chains

Remote Real-Time Collaboration Platform enabled by the Capture, Digitisation and Transfer of Human-Workpiece Interactions

In this highly globalised manufacturing ecosystem, product design and verification activities, production and inspection processes, and technical support services are spread across global supply chains and customer networks. Therefore, a platform for global teams to collaborate with each other in real-time to perform complex tasks is highly desirable. This work investigates the design and development of a remote real-time collaboration platform by using human motion capture technology powered by infrared light based depth imaging sensors borrowed from the gaming industry. The unique functionality of the proposed platform is the sharing of physical contexts during a collaboration session by not only exchanging human actions but also the effects of those actions on the task environment. This enables teams to remotely work on a common task problem at the same time and also get immediate feedback from each other which is vital for collaborative design, inspection and verifications tasks in the factories of the future

Telerehabilitation using Real Time Communication

There are many diseases affecting the population trends globally.Miniaturization of sensors in combined with medical information technology provides efficient solutions to reduce costs and deliver remote medical services through connected devices. Remote consultation via video‐conferencing has been well established, but the chronic or long‐term musculoskeletal conditions require pro‐active management and therapy thus raising the need to develop more advanced telerehabilitation systems. In this paper, we introduce KinectRTC that can be used for Kinect‐based telerehabilitationwith efficient real‐timetransmission of video, audio and skeletal data. The Web Real‐TimeCommunication (WebRTC) technology has benefitted to the proposed framework which is able to manage video and audio streams based on the state of the network and the available bandwidth to guarantee the real‐time performance of the communication

A knowledge capture framework for remote collaborative virtual environments

Due to globalization, the need for geographically separated teams to collaborate on engineering tasks and learn from each other has increased. As a result, being able to observe and hence understand the actions of others in collaborative sessions is very important because it enables the users to work towards a common task’s goal. In this work, we propose and develop a novel collaborative knowledge framework that can be used to achieve a common task understanding during remote collaborative tasks. The collaborative knowledge framework provides playback and reuse facilities to enable the storage of knowledge elements (i.e. utterances, manipulations of shared representations, documents accessed during a collaborative session) as well as their recall in future engineering tasks. We validate the framework using an automotive use case

A framework for digitisation of manual manufacturing task knowledge using gaming interface technology

Intense market competition and the global skill supply crunch are hurting the manufacturing industry, which is heavily dependent on skilled labour. Companies must look for innovative ways to acquire manufacturing skills from their experts and transfer them to novices and eventually to machines to remain competitive. There is a lack of systematic processes in the manufacturing industry and research for cost-effective capture and transfer of human skills. Therefore, the aim of this research is to develop a framework for digitisation of manual manufacturing task knowledge, a major constituent of which is human skill. The proposed digitisation framework is based on the theory of human-workpiece interactions that is developed in this research. The unique aspect of the framework is the use of consumer-grade gaming interface technology to capture and record manual manufacturing tasks in digital form to enable the extraction, decoding and transfer of manufacturing knowledge constituents that are associated with the task. The framework is implemented, tested and refined using 5 case studies, including 1 toy assembly task, 2 real-life-like assembly tasks, 1 simulated assembly task and 1 real-life composite layup task. It is successfully validated based on the outcomes of the case studies and a benchmarking exercise that was conducted to evaluate its performance. This research contributes to knowledge in five main areas, namely, (1) the theory of human-workpiece interactions to decipher human behaviour in manual manufacturing tasks, (2) a cohesive and holistic framework to digitise manual manufacturing task knowledge, especially tacit knowledge such as human action and reaction skills, (3) the use of low-cost gaming interface technology to capture human actions and the effect of those actions on workpieces during a manufacturing task, (4) a new way to use hidden Markov modelling to produce digital skill models to represent human ability to perform complex tasks and (5) extraction and decoding of manufacturing knowledge constituents from the digital skill models

Digital twin in manufacturing : conceptual framework and case studies

The digital twin (DT) concept has a key role in the future of the smart manufacturing industry. This review paper aims to investigate the development of the digital twin concept, its maturity and its vital role in the fourth industrial revolution. Having identified its potential functionalities for the digitalisation of the manufacturing industry, the digital twin concept, its origin and perspectives from both the academic and industrial sectors are presented. The identified research gaps, trends and technical limitations hampering the implementation of digital twins are also discussed. In particular, this review attempts to address the research question on how the digital twin concept can support the realisation of an integrated, flexible and collaborative manufacturing environment which is one of the goals projected by the fourth industrial revolution. To address this, a conceptual framework supporting an integrated product-process digital twin for application in digitised manufacturing is proposed. The application and benefits of the proposed framework are presented in three case studies

Motion capture technology in industrial applications: A systematic review

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition

Smart Technologies for Precision Assembly

This open access book constitutes the refereed post-conference proceedings of the 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, held virtually in December 2020. The 16 revised full papers and 10 revised short papers presented together with 1 keynote paper were carefully reviewed and selected from numerous submissions. The papers address topics such as assembly design and planning; assembly operations; assembly cells and systems; human centred assembly; and assistance methods in assembly

Multi-scale metrology for automated non-destructive testing systems

This thesis was previously held under moratorium from 5/05/2020 to 5/05/2022The use of lightweight composite structures in the aerospace industry is now commonplace. Unlike conventional materials, these parts can be moulded into complex aerodynamic shapes, which are diffcult to inspect rapidly using conventional Non-Destructive Testing (NDT) techniques. Industrial robots provide a means of automating the inspection process due to their high dexterity and improved path planning methods. This thesis concerns using industrial robots as a method for assessing the quality of components with complex geometries. The focus of the investigations in this thesis is on improving the overall system performance through the use of concepts from the field of metrology, specifically calibration and traceability. The use of computer vision is investigated as a way to increase automation levels by identifying a component's type and approximate position through comparison with CAD models. The challenges identified through this research include developing novel calibration techniques for optimising sensor integration, verifying system performance using laser trackers, and improving automation levels through optical sensing. The developed calibration techniques are evaluated experimentally using standard reference samples. A 70% increase in absolute accuracy was achieved in comparison to manual calibration techniques. Inspections were improved as verified by a 30% improvement in ultrasonic signal response. A new approach to automatically identify and estimate the pose of a component was developed specifically for automated NDT applications. The method uses 2D and 3D camera measurements along with CAD models to extract and match shape information. It was found that optical large volume measurements could provide suffciently high accuracy measurements to allow ultrasonic alignment methods to work, establishing a multi-scale metrology approach to increasing automation levels. A classification framework based on shape outlines extracted from images was shown to provide over 88% accuracy on a limited number of samples.The use of lightweight composite structures in the aerospace industry is now commonplace. Unlike conventional materials, these parts can be moulded into complex aerodynamic shapes, which are diffcult to inspect rapidly using conventional Non-Destructive Testing (NDT) techniques. Industrial robots provide a means of automating the inspection process due to their high dexterity and improved path planning methods. This thesis concerns using industrial robots as a method for assessing the quality of components with complex geometries. The focus of the investigations in this thesis is on improving the overall system performance through the use of concepts from the field of metrology, specifically calibration and traceability. The use of computer vision is investigated as a way to increase automation levels by identifying a component's type and approximate position through comparison with CAD models. The challenges identified through this research include developing novel calibration techniques for optimising sensor integration, verifying system performance using laser trackers, and improving automation levels through optical sensing. The developed calibration techniques are evaluated experimentally using standard reference samples. A 70% increase in absolute accuracy was achieved in comparison to manual calibration techniques. Inspections were improved as verified by a 30% improvement in ultrasonic signal response. A new approach to automatically identify and estimate the pose of a component was developed specifically for automated NDT applications. The method uses 2D and 3D camera measurements along with CAD models to extract and match shape information. It was found that optical large volume measurements could provide suffciently high accuracy measurements to allow ultrasonic alignment methods to work, establishing a multi-scale metrology approach to increasing automation levels. A classification framework based on shape outlines extracted from images was shown to provide over 88% accuracy on a limited number of samples

Challenges for engineering students working with authentic complex problems

Engineers are important participants in solving societal, environmental and technical problems. However, due to an increasing complexity in relation to these problems new interdisciplinary competences are needed in engineering. Instead of students working with monodisciplinary problems, a situation where students work with authentic complex problems in interdisciplinary teams together with a company may scaffold development of new competences. The question is: What are the challenges for students structuring the work on authentic interdisciplinary problems? This study explores a three-day event where 7 students from Aalborg University (AAU) from four different faculties and one student from University College North Denmark (UCN), (6th-10th semester), worked in two groups at a large Danish company, solving authentic complex problems. The event was structured as a Hackathon where the students for three days worked with problem identification, problem analysis and finalizing with a pitch competition presenting their findings. During the event the students had workshops to support the work and they had the opportunity to use employees from the company as facilitators. It was an extracurricular activity during the summer holiday season. The methodology used for data collection was qualitative both in terms of observations and participants’ reflection reports. The students were observed during the whole event. Findings from this part of a larger study indicated, that students experience inability to transfer and transform project competences from their previous disciplinary experiences to an interdisciplinary setting