A dynamic and flexible manufacturing environment presents many challenges in the movement of autonomous mobile robots (AMRs), leading to delays due to the complexity of operations while negotiating even a simple route. Therefore, an understanding of rules related to AMR movement is important both from a utility perspective as well as a safety perspective. Our survey from literature and industry has revealed a gap in methodology to test rules related to AMR movement in a factory environment. Testing purely through simulations would not able to capture the nuances of shop floor interactions whereas physical testing alone would be incredibly time-consuming and potentially hazardous. This work presents a new methodology that can make use of observations of AMR behaviour on selected cases on the shop floor and build up the fidelity of those simulations based on observations. This paper presents the development of a Highway Code for AMRs, development of simulation models for an ideal AMR (based on the rules from the Highway Code), and physical testing of real AMR in an industrial environment. Finally, a behavioural comparison of an ideal AMR and a real AMR in five scenarios (taken from the shop floor of an industrial partner) is presented. This work could enable informed decisions regarding the implementation of AMRs through identification of any adverse behaviours which could then be mitigated either through improvements on the AMR or through establishing shop floor protocols that reduce the potential impact of these behaviours.Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Int J Adv Manuf Technol (2019) 104:4617-4628
Aerospace production systems face increasing requirements for flexibility and reconfiguration, along with considerations of cost, utilisation, and efficiency. This drives a need for systems with a small number of automation platforms (e.g. industrial robots) that can make use of a larger number of end effectors that are potentially flexible or multifunctional. This leads to the challenge of ensuring that the configuration and location of each end effector is tracked by the system at all times, even in the face of manual adjustments, to ensure that the correct processes are applied to the product at the right time. We present a solution based on a Data Distribution Service that provides the system with full awareness of the context of its automation platforms and end effectors. The solution is grounded with an example use case from WingLIFT, a research programme led by a large aerospace manufacturer. The WingLIFT project in which this solution was developed builds on the adaptive systems approach from the Evolvable Assembly Systems project, with focus on extending and increasing the aerospace industrial applicability of plug and produce techniques. The design of this software solution is described from multiple perspectives, and accompanied by details of a physical demonstration cell that is in the process of being commissioned.
Assessing the quality of inspection for tapered aircraft fastener holes using an engineer's blue contact test.
Currently, systems installed on large-scale aerospace structures are manually equipped by trained operators. To improve current methods, an automated system that ensures quality control and process adherence could be used. This work presents a mobile robot capable of autonomously inspecting aircraft systems and providing feedback to workers. The mobile robot can follow operators and localise the position of the inspection using a thermal camera and 2D lidars. While moving, a depth camera collects 3D data about the system being installed. The in-process monitoring algorithm uses this information to check if the system has been correctly installed. Finally, based on these measurements, indications are shown on a screen to provide feedback to the workers. The performance of this solution has been validated in a laboratory environment, replicating a trailing edge equipping task. During testing, the tracking and localisation systems have proven to be reliable. The in-process monitoring system was also found to provide accurate feedback to the operators. Overall, the results show that the solution is promising for industrial applications.
The main challenge aerospace industries are facing in recent times has been triggered by the remarkable increase in commercial aircraft demand. To address this challenge, aircraft manufacturers need to explore ways to increase capacity and workflow through process optimisation and automation. This study focusses on the optimisation of component flow and inventory during the assembly of the A320 Family wings' at Airbus (Broughton, UK) plant through Discrete Event Simulation (DES). This research measured the likely impact of future changes in the wing assembly process, using simulation by: mapping of component flow from delivery to the point of use, simulation of current logistics scenario (AS-IS), simulation of future logistics scenarios (TO-BE) that include proposed changes for optimising flow and managing capacity surge, and testing and validation of mapping and simulation. The developed DES model demonstrated the impact of changes planned to be implemented by showing a considerable increase in production capacity growth, by achieving a target of 50% increase of aircraft rate/month within one year. It also highlighted the main problems causing blockages and other non-value activities in the process.
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