Project Shared Workspace implemented and operational
To fulfil two fundamental internal project communication requirements: i) efficient exchange between partners of information about DIGIMAN project ii) decentralised and secured archiving of the documents generated, one independent and secured web-based communication tool: Project Shared Workplace – PSW has been implemented with a restricted access for project partners only. .... Read More
Definition of Auto Best Practice & Baseline KPI Specification - Confidential
DIGIMAN deliverable report D2.1 presents the metrics and KPI targets set for the fully automated stack assembly concept derived from Auto best practise based on the Toyota Production System (TPS). KPIs have been set to measure specific and tangible performance metrics such as cycle time, assembly related costs, yields and uptime, against which delivered product performance shall not be eroded. The so defined baseline KPIs for blueprint design performance metrics and KPI will be used in subsequent project activities, including Technical cost modelling, Proof of process and production relevant facility implementation and Stack manufacturing validation testing. A summary of the project key objectives are:
- Demonstrate that, via the uplifted automation, the blueprint design as configured as fully integrated assembly and test line, would scale to deliver production capacity >50,000 stacks/year by 2020
- Demonstrate for a single line a total stack power output of >5MW
- Demonstrate a step improvement of cell assembly cycle times from today’s semi-automated to automated processing at <5 seconds per cell
- Advance stack manufacturing technology level to MRL6
- Develop in-process quality controls at component and sub-component level to reduce scrap rate to target <3%
- Model costs showing target trajectories consistent with automotive targets for 2020 at 50k stacks pa
- Ensure that the stack performance is not detrimentally affected by the improvements for manufacturing and assembly delivering 0.7 A/cm2 @ 0.7V world leading for air cooled fuel cell technology
Stack Cmommissioning Handover Baseline Requirements - Confidential
DIGIMAN Deliverable Report D2.2 presents a summary of the Beginning of Life (BoL) handover requirements for Intelligent Energy’s automotive fuel cell stacks. As targets for quality and throughput need to align with contemporary, at rate, single-piece-flow automotive production; scenarios for direct ship-to-line drop-in / drive-off handover need to be modelled and pre-conditioned functionality benchmarks specified. “Best in class” practises (e.g. The Toyota Production System) are applied by Toyota. Performance, life expectancy, environmental compatibility and robustness of life targets will factor within an ensuing BoL test procedure to be developed under DIGIMAN Work Package Task 2.3. This, together with an exemplar for stack assembly, as derived from WP4 outcomes for the Blue-print automated cell assembly design, will enable the modelling of the stack process flow and development of digital cause and effects capability via Discrete Events Simulation, thus, underpinning MRL6 attainment. Models for operational expense (OPEX) based Inputs (Technical Cost Modelling – Stack Assembly) will be enabled.
WP2 interim report
DigiMan’s Work Package 2 involves the setting of requirements for i) the attainment of AC64 fully automated fuel cell stack assembly, and ii) demonstration of that attainment to MRL6 - capability to produce a prototype system or subsystem in a production relevant (i.e. automotive) environment. From this, metrics & target KPIs will be derived. End-of-line (EOL) stack test method procedures and processes will be derived to meet customer performance baselines. This Interim Report provides a mid-term review and status update for the work package. Read more ...
Definition of GDL component handling formats & interfaces specification - Confidential
GDL’s are a key performance influencing component of PEMFC stacks. The raw material is produced as roll stock via contiguous, but imperfect processes; meaning that continuous roll stock cannot be assumed all good (to use) and requires (digital) scanning / mapping and converting processes. DigiMan looks to address industry wide gaps in the supply chain capabilities for direct-to-line supply of roll-stock raw material for lineside conversion to known good, ready to use GDL components. Digital QC and manufacturing techniques will negotiate boundaries and smooth their transitions with seamless interfaces to enable digital transactions between roll-stock manufacturer and fuel cell assembler. Via big-data mining & analysis of data trends and cause & effects relationships, which, might not be otherwise visible, an unprecedented deep level of understanding of GDL structures and their impact on fuel cell performance is expected.
Structural defects Classification - Confidential
DigiMan will involve characterization and digital codification of physical attributes of key materials (e.g. gas diffusion Layers or GDLs) to establish yield impacting digital cause and effects relationships within the value chain. To support these innovative digital QC methodologies a common language and taxonomy, for up / down stream communications has been derived. Incorporating pre-existing quality control defect catalogues, new classifications, which differentiate between visible and non-visible anomalies are described. An exemplar for their use in digital cause & effects modelling is provided.
Process Specification for use by the DIGIMAN Consortium - Confidential
The DigiMan project will focus on the development of an automated fuel cell assembly system, the output of which should be capable of interfacing with Intelligent Energy’s (IE) existing automated stack assembly module for its AC64 stack technology platform.
This deliverable report presents the DigiMan project’s specification for the hard deliverable “PoP Demonstrator” unit and also soft deliverable “fully costed blue-print-design” for a scalable solution to IE business production needs.
A key theme is to transpose automotive best practice into the manufacture of fuel cell assemblies. As a demonstrator unit, the hard deliverable shall incorporate advanced data collection and part tracking capability which will be utilised to analyse possible production cause and effects characteristics.
Digital manufacturing and Proof-of-Process for automotive fuel cells Proof of Process design specification - Confidential
The DigiMan project focuses on the development of an automated fuel cell assembly system, the output of which should be capable of interfacing with Intelligent Energy’s (IE) existing automated stack assembly module for its AC64 stack technology platform. This deliverable report presents the process design specification for the hard deliverable “PoP Demonstrator” unit and informs on the soft deliverable “fully costed Blueprint-design” for a scalable solution to IE business production needs.
Selection of Empirical Properties Methods Report - Confidential
The objectives of the WP5 are to define the critical parameters for GDLs used in the AC64 technology in order to define in line characterization methods. To do so, boundary properties as well as defects have to be defined. This deliverable summarizes the preliminary work that has been made to select adequate methods to evaluate the empirical properties of interest for DIGIMAN.
GDL empirical and digital properties - Confidential
This is an interim report summarising the findings to date and the focus of the next phase of materials analysis. Further reports will be generated to bring together the full breadth and conclusions from the WP5 activities under DIGIMAN.
Data links and data harvesting for volume manufacture - Process flow - Confidential
The DIGIMAN project will focus on the development of an automated fuel cell assembly system, the output of which should be capable of interfacing with Intelligent Energy’s (IE) existing automated stack assembly module for its AC64 stack technology platform. Additionally, under project Work Package 6, IE aims through the digital modelling of extrapolated “big data”, to determine the cause and effect relationships of the stack materials, in particular GDL, and the to-be-developed fully automated cell assembly processes, with the end of line stack performance test results. Harvesting techniques, will bring cause and effects data together into physical models to enable Digital QC and manufacturing standards to be derived.
This initial deliverable report presents a summary of the current data capture and handling processes used during Air Cooled fuel cell and stack production and test at IE, and provides an outline proposal for an improved process with expanded scope in line with Industry 4.0 best practice to create a Central Database for subsequent cause and effects analysis.
The DigiMan project website is designed to fulfil project communication and dissemination needs for the benefit of the whole scientific community and the public through relevant information including:
- project overall objectives, partner & work packages information
- project activities: news, meetings
- project progress: technical publications, conference presentations, public domain reports
- project resources: links, related events …
- project contact information
All the partners will collectively participate in the dissemination objective of the website by providing up-to-date information. .... Read More
Dissemination and knowledge management protocol - Confidential
This report presents the dissemination protocol for the DigiMan project, the procedure for “Open Access” to peer reviewed research articles, internal rules, information on support from the EU and FCH-JU members and the strategy for Knowledge Management within the project. Read More
Annual Data Report (Year 2) - Confidential
This report presents the data obtained within DIGIMAN during year 2017, in relationship with the Data Collection Exercise as requested by FCH-JU (TRUST and Eu-Survey Platforms).