Public procurement notice: Contract Award Notice
Published: 2020-08-14
Buyer: University of Nottingham
Country: UK
Contract type: Supplies
Procedure: Open procedure
CPV classification: 32424000 | 42990000 | 48800000 | 51500000
Estimated value: 2270000 GBP
Award criteria: Lowest price

Technical requirements: The Centre for Aerospace Manufacturing (CAM) at the University of Nottingham (UoN) works with partners from across the UK and European aerospace industry to deliver industrially focused research into aerospace assembly systems. Advanced manufacturing is a key research priority area for Nottingham and the University has invested significantly in this research. The University made a strategic investment in automated assembly infrastructure through the Future Automated Aerospace Assembly Demonstrator (FA3D) project, which was commissioned in 2016. Based on the success of this project, funding has been secured from the UK Government’s Industrial Strategy Challenge Fund (ISCF) through the Aerospace Technology Institute (ATI) for development of the second phase of this demonstrator.
The minimum requirements of 3D modelling and simulation to enable virtual commissioning are as follows:
(a) all automation, tooling and associated control equipment must be modelled in 3D in the desired CAD package or supplied in the required format as set by UoN.
— as a minimum, the supplied solution must be able to accept CAD models in CATIA V5 R2017 as an input and be able to output to a compatible format. This requirement does not constrain the format used internally for the digital twin, and suppliers are free to recommend their preferred modelling and simulation package for that purpose.
(b) the CAD model must be supplied with embedded forward and inverse kinematics to enable simulation and verification of the FA3D2 and its associated processes.
(c) all acceleration and speed parameters must be set in the kinematics assemblies within the model to enable real time simulation and motion visualisation.
(d) process positions must be set within the kinematics for process specific positions such as home or closed for tooling clamps, etc.
(e) smart devices must be generated to simulate actual device behaviour and enable PLC off-line programming.
(f) a post processor must be supplied to enable realistic simulation and off-line programming of the FA3D2 and its automated processes.
(g) a full process and method, along with suitable training data where possible, must be supplied to enable UoN users to generate off-line programmes for the automated processes including PLC code generation and verification.
(h) the supplier must demonstrate their 3D simulation and virtual commission capabilities and any previous implemented solutions within their tender submission.
(i) the installed system must be in accordance with the framework and requirements set out in Work Package 2: Smart Manufacturing and in collaboration with the successful tenderers of WP2.2 IoT Network Infrastructure, WP2.3 Digital Twin, and WP2.4 Data Analytics and Machine Learning.
The minimum requirements of the IoT network infrastructure to enable real-time data acquisition, management and visualisation via interactive dashboards/HMIs are as follows. See Figure 8 for the data acquisition IoT architecture example. a. The system must enable the user to capture data gathered from all intelligent sensors and metrology devices specified in Work Package 5: Precision Assembly, or data from a selection of these sensors. o The data from the sensors must be categorised within the data store to perform analysis in order to gain insights into the automated processes. o The supplier must give UoN access to this data both programmatically through an API (Application Programming Interface) and through an interactive dashboard interface. b. The installed system must be capable of presenting real-time information (of pre-selected process data captured by sensors) to the user via interactive dashboards on an HMI device (number of interfaces to be agreed). o These dashboards must be easily re-configurable and enable the user to view, select, edit, load and export process parameters. c. The supplier must provide UoN with the ability to build its own interactive dashboards to visualise data from sensors and automated equipment. Access to baseline code is required wherever possible. d. The installed system must be able to capture, store and re-call all historic process sensor data from the data store and know when (time and date) processes were carried out. e. The supplier must connect to all required process sensors, PLCs and/or devices to enable data acquisition. f. The installed system must be in accordance with the framework and requirements set out in Work Package 2: Smart Manufacturing and in collaboration with the successful tenderers of WP2.1 Modelling and Simulation, WP2.3 Digital Twin, and WP2.4 Data Analytics and Machine Learning.
The minimum requirements for digital twin generation are follows. See Figure 7 for the basic FA3D2 digital twin architecture and Figure 9 for the example FA3D2 digital twin minimum data flow. a. The supplier must provide an architecture diagram with their tender proposal, which illustrates their digital twin concept, hardware, software, and data flow. b. The supplier must provide a connected system, which enables bi-directional communication and visualisation of sensor and automation data. Systems within the cell must be able to access related messages from sensors or devices. These tags/messages will be used to trigger other events within the cell or instruct data to be extracted, transformed and loaded (ETL) at specified real-time interval/s. c. The supplier must provide UoN with the required architecture to enable a digital control room interface/dashboard which enables users to view/perform the following: a. Real-time visibility of KPIs and issues for proactive problem resolution - supplier to suggest their achievable frequency. b. Drill down into underlying line asset data for root-cause analysis and continuous improvement - supplier to demonstrate workflow. c. Automatic notification (text, email or equivalent) when a machine signal or KPI deviates from normal behaviour - supplier to demonstrate proposed concept for alerts referring to server, tag, device, or machine issue d. Trend any PLC tag in minutes - time stamp, name and value in an open format etc. e. Enable remote troubleshooting of PLC issues. f. Connect to existing factory assets via the installed micro server and/or gateway. g. Send and revision control robot programs to the required controllers for execution. h. Visualise the physical automated process running within the virtual environment - real time (supplier to specify frequency), this must be via a mobile tablet or other device proposed by the supplier such as augmented headset device. d. The supplier must provide an example of how a digital work instruction can be generated, published and visualised by the operator. e. UoN will require the capability to capture data within the digital work instruction. The supplier should demonstrate how process parameters can be captured within the instruction and ETL for future analysis. f. The installed system must be in accordance with the framework and requirements set out in Work Package 2: Smart Manufacturing and in collaboration with the successful tenderers of WP2.1 Modelling and Simulation, WP2.2 IoT Network Infrastructure, and WP2.4 Data Analytics and Machine Learning.
The minimum requirements for data analytics and machine learning are as follows: a. All process data captured must be extractable in an open format such as comma separated variable file format (.csv) or XML, XLS, XLSX, PDF, TXT that can be opened within freely available text editing software. b. Data collected and exported will be analysed by running machine learning algorithms. The installed system must have the function to import the data back into the installed system and automatically update the process variables. c. The installed system must be capable of capturing and categorising in-process measurements and program data. This must enable UoN to complete real-time modifications of programs. d. The installed system must be in accordance with the framework and requirements set out in Work Package 2: Smart Manufacturing and in collaboration with the successful tenderers of WP2.1 Modelling and Simulation, WP2.2 IoT Network Infrastructure, and WP2.3 Digital Twin


Winner: N.T.I Measure
Award value: 2270000
Number of bids: 2