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
DIGIMAN Deliverable Report D2.1 presents a definition of Automotive Best Practice via the Toyota Production System. It then goes on to define the specification of baseline Critical Performance Indicators for the DIGIMAN Project. The aim of task 2.1 is to document these metrics and KPI targets for the emergent fully automated stack assembly concept as demonstrable and measurable (at a down-scaled rate) via the Proof of Process Demonstrator to be delivered under the project. Read more ...
Stack Cmommissioning Handover Baseline Requirements
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. Read more ...
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 ...
End of Line Test Methods - Confidential
This deliverable report under Work Package 2 presents a specification of stack EoL (End-of-Line) test methods and standards within a mass production, automotive best practice scenario. Leak rates, conditioning phase and EoL performance tests are described and linked with acceptance criteria fixed by automotive EoL expectations. Discrete conditioning phases have been evaluated in order to optimise the duration of the test and as a consequence the cost of this stack acceptance activity both in terms of infrastructure and operation.
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.
GDL roll good optimisation and digitalisation - Confidential
In the context of automated assembly of fuel cell stacks, digitally characterized components allow a quick and reliable workflow. Upon assembly of the stack, the provided set of Quality Control data may be used to gain an advanced understanding of cause and effect correlations between ex-situ components data and in-situ fuel cell performance. This supports the development of high performance fuel cell systems and ultimately allows the fuel cell market to grow. This report deals with the digitalization and optimization of Gas Diffusion Layer (GDL) roll good in order to prepare the component for an automated assembly of fuel cell stacks. Guided by requirements worked out among the project partners in WP3, major results achieved are (i) inline surface and quality inspection on various process steps such as the camera-based defect inspection, (ii) converting of GDL roll good to the requirements of continuous singulation and (iii) preparation of a digital defect protocol for export of data to the downstream process steps. The progress made in this task displays a big leap forward into the direction of industry 4.0 production. It forms the central framework, into which future developments (Quality Analysis methods, production optimization) can be included and contribute to a fully digitalized materials characterization and automated assembly of stacks.
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.
Proof of Process development facility implementedThe DigiMan project focuses on the development of an automated fuel cell assembly system. Cell assembly is uplifted from the incumbent semi-automated system to full automation and output to Intelligent Energy’s (IE) pre-existing automated stack assembly module for its AC64 stack technology platform.
This deliverable report under Work Package 4 presents the implemented Proof of Process development facility, including the PoP Demonstrator implemented design, the system and control architecture, Digital engineering activities, Factory Acceptance Tests and details further planned work to be reported within D4.4 “PoP Demonstrator Cycling Trials Report” and D4.5 “Production Relevant Environment Facility”. Read more ...
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.
GDL database of empirical properties - Confidential
GDL properties strongly influence the performance of fuel cells. The correlation between these properties and FC performance has been widely studied, but numerous phenomena remain unexplained. For instance, it is still unclear whether defect inclusions or heterogeneities (homogenous anomalies) affect the Electrochemical performance. For enhancement of process control methods, a deeper understanding of the correlation of defects / properties / performances is needed. In evaluation of defect inclusions DIGIMAN’s WP5 focuses on Freudenberg materials and their performance as benchmarked against IE’s AC64 stack design. Samples of regular material without anomalies and samples with deliberately created defects were studied. The properties of these various sample types and defect categories have been screened and their influence (on AC64 performance) analyzed. For the anode GDL, properties such as MPL thickness, substrate thickness, electrical resistivity, gas permeability and thermal behavior have been evaluated and their influence on performance studied. This report provides parameter definitions and cause & effect relationships as useable via a GDL database which then supports digital QC and digital cause & effects modelling; thus, addressing the FCH-2-JU Multi-Annual Work Plan 2016 requirement to demonstrate Automotive Best Practice. .
Correlation digital versus empirical GDL properties report - Confidential
The roll stock from which AC64’s GDL parts are converted consists of a fiber substrate equipped with a microporous functional coating (MPL). The substrate is of anone-woven nature, with a semi-randomised long-fiber structure. The coating is a sensitive layer, easily affected by unintended physical contact. As in any production process, it is neither possible to prevent nor practical to remove (from a contiguous, unpunctured roll stock) any ensuing anomalies, some of which may be classified as defects, hence defect inclusions. In evaluation of defect inclusions DIGIMAN’s WP5 focuses on FPM GDL materials and their performance as benchmarked against IE’s AC64 stack design. With the goal to track anomalies in the GDLs, and following a deep parameter study, alternative scanning techniques have been studied. Since thermal properties such as thermal diffusivity were found to vary in an unexpected manner, pulsed thermal thermography combined with thermal diffusivity analysis have been studied, and the resulting scan have been compared. This report provides a comparison between the data from measurements made on small samples via a laser flash analysis, and the data obtained from the Pulse thermography trials.
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
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).
Annual Data Report (Year 3) - Confidential
This report presents the data obtained within DIGIMAN during year 2018, in relationship with the Data Collection Exercise as requested by FCH-JU (TRUST and EU-Survey Platforms).