1. Digitalisation pathways
  2. Autonomous Smart Factories
  3. Realtime optimisation
View Kanban

Associated projects

Show all
Showing 10 out of 18
Toggle all information

CloudiFacturing | Cloudification of Production Engineering for Predictive Digital Manufacturing

01-10-2017

-

31-03-2021

Calltopic: FoF.2017.12

: 7 | : 6
Show more information
Comments The cloud-based optimisation of lamination oven’s configuration will lead to the following significant impacts.
 Periodic Reporting for period 1 - CloudiFacturing (Cloudification of Production Engineering for Predictive Digital Manufacturing) Result description Just to give two examples: 1) the fully automated manufacturing of electric motors based on an optimisation scheme by deriving NC programmes for the machinery or 2) an improved optimisation loop (improved
 Added Value from user perspective Taxon description or the KPI perspective (what KPI is the focus of the optimisation).
 Contribution to the reduction of energy use and CO2 emissions Comments The cloud-based optimisation of lamination oven’s configuration will lead to the following significant impacts.

COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation

01-09-2016

-

31-08-2019

Calltopic: FoF.2016.11

: 117 | : 7
Show more information
Description Data across the (multi-sided) company internal value chain is integrated by the IIMS with optimisation and modelling tools for resource management including innovative, multi-level, real-time
Comments Design a technical operating system for the support connected and interoperable factories with their stakeholders and for the optimisation of manufacturing and logistics processes.
 Real-time event broker I Result description The goal of the IIMS is to enhance re-configurability, scalability and optimisation of resources and processes inside the factory andoptimise manufacturing and logistics processes.
 Real-time event broker II Result description The goal of the IIMS is to enhance re-configurability, scalability and optimisation of resources and processes inside the factory andoptimise manufacturing and logistics processes.
 Autonomous /online/realtime Manufacturing Process Optimisation on machine level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on machine level
 Added Value from user perspective Taxon description or the KPI perspective (what KPI is the focus of the optimisation).
 Operating systems Comments Design a technical operating system for the support connected and interoperable factories with their stakeholders and for the optimisation of manufacturing and logistics processes.

SatisFactory | A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments

01-01-2015

-

31-12-2017

Calltopic: FoF.NMPB.2014.4

: 53 | : 29
Show more information
 Autonomous /online/realtime Manufacturing Process Optimisation on machine level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on machine level
 Platform enabled optimisation Taxon description digital platforms offer added-value basd upon three main mechanisms: Reduction of transaction costs Network effects Use of data integration for new services (mainly optimisation

ROSSINI | RObot enhanced SenSing, INtelligence and actuation to Improve job quality in manufacturing

01-10-2018

-

31-03-2022

Calltopic: DT-FOF-02-2018

: 3 | : 0
Show more information
 Realtime optimisation Taxon title Realtime optimisation
 Supply chain and value network efficiency Taxon description Optimisation challenges must be faced along the entire supply chain or value network, involving OEMs, components suppliers, service providers and SMEs.
    Autonomous Smart Factories
    Comment:

    ROSSINI will provide several advances beyond the state of the art, developing a Safety Aware Control Architecture for robot cognitive perception and optimal task planning and execution. In order to enable actual perception, ROSSINI  will deploy a data processing technique based on artificial intelligence, taking advantage of GPU computing and advanced machine learning algorithms for real-time image recognition, to obtain a semantic scene map that adapts to dynamic working conditions. ROSSINI will adopt AI algorithms also for motion prediction of humans and moving entities, and will embed their stochastic information in the dynamic semantic scene map. This will allow the robot to further refine its planning in order to maximize performance while preserving safety. Moreover, the deployment of artificial intelligence techniques for data processing will not be just aimed to enable robot perception, but will also make robot cognition possible. ROSSINI Safety Aware Control Architecture will make it possible for robots to optimally schedule the tasks reacting to a changing environment. Each action to be executed will be sent to a dynamic planner that will dynamically optimize its execution in terms of trajectory to follow and/or interactive behaviour to reproduce while considering the variable safety conditions in the working area. In this way, ROSSINI will reach the goal to optimise the trade-off between human operator safety and manufacturing productivity and will introduce a novel perception and control architecture blending safety and performance oriented planning/control.

Z-BRE4K | Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories

01-10-2017

-

31-03-2021

Calltopic: FoF.2017.09

: 11 | : 0
Show more information
Comments Z-Bre4k will contribute to the optimisation of the manufacturing processesresulting in significantly less waste and scrap.
 Realtime optimisation Taxon title Realtime optimisation
 Supply chain and value network efficiency Taxon description Optimisation challenges must be faced along the entire supply chain or value network, involving OEMs, components suppliers, service providers and SMEs.
 Waste minimisation Comments Z-Bre4k will contribute to the optimisation of the manufacturing processesresulting in significantly less waste and scrap.
    Autonomous Smart Factories
    Comment:

    Z-Bre4k will provide a complete monitoring solution at component, machine and system level by combining the high capabilities and effectiveness in sensors and actuation, networking and computational power, utilisation of better and smarter technologies (e.g. material and tools). The latest technologies and algorithms will be utilized for adaptive systems while surpassing the fact that they are disjointed, overwhelmed by complexity, vulnerable to external influence and poor Predictive Capabilities.

      Realtime optimisation
      Comment:

      A real-time adaptive simulator with high fidelity will be a demonstrator (remote or local) of the machine’s state, in which a fast-forward simulation mechanism (prognostic models) predicts the potential events of breakdown of components and machines. What-if capabilities will allow the maintenance planners to find the most effective and cost-efficient schedules for component replacement and maintenance plans.

        Autonomous /online/realtime Manufacturing Process Optimisation on factory level
        Comment:

        Strategies to improve maintainability and increase operating life of production will be applied to update the existing and to develop a set of new strategies based on real data in order to improve maintainability and operating life of production systems. This approach will use a method to translate optimization objectives defined at production and factory levels, into optimized maintenance policies at asset/production process levels.

        Autonomous /online/realtime Manufacturing Process Optimisation on machine level
        Comment:

        At the asset and machine level, the Z-BR3AK solution will perform a condition monitoring and generate health status reports. Attention will be given to the faults detection through FMEA analysis (FMECA) to allow remedial actions and synchronise the manufacturing process. The proposed engine will perform monitoring, inspection and control at component, machine, system and product level to issue warnings, alerts (e.g. about deviations from production and quality requirements), reports on (potential) failures or failure prone situations and pass related information to a higher-level Z-Bre4k DSS (for decision support at manufacturing and enterprise level).

X-ACT | Expert cooperative robots for highly skilled operations for the factory of the future

01-10-2012

-

30-09-2015

Calltopic: FoF.ICT.2011.7.1

: 11 | : 3
Show more information
 Autonomous /online/realtime Manufacturing Process Optimisation on machine level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on machine level

MUSIC | MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components

01-09-2012

-

31-08-2016

Calltopic: FoF.ICT.2011.7.1

: 13 | : 6
Show more information
 Smart Control and Cognitive System applied to the HPDC Foundry 4.0A robust and competitive methodology developed under EU-FP7 MUSIC Project Result description This real time control system capability is then presented and applied to industrial case-histories, showing how to train a cognitive-based ICT platform for the industrial optimisation of High Pressure
 Autonomous /online/realtime Manufacturing Process Optimisation on machine level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on machine level

TWIN-CONTROL | Twin-model based virtual manufacturing for machine tool-process simulation and control

10-01-2015

-

30-09-2018

Calltopic: FoF.NMPB.2015.14

: 4 | : 4
Show more information
 Machine Tool Digital Twin with Life Cycle features Result comments Manufacturing Process Optimisation on machine level
 Local Monitoring and Control System Result comments Manufacturing Process Optimisation on machine level
 Autonomous /online/realtime Manufacturing Process Optimisation on factory level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on factory level
 Platform enabled optimisation Taxon description digital platforms offer added-value basd upon three main mechanisms: Reduction of transaction costs Network effects Use of data integration for new services (mainly optimisation

SYMBIO-TIC | Symbiotic Human-Robot Collaborative Assembly: Technologies, Innovations and Competitiveness

01-04-2015

-

01-04-2019

Calltopic: FoF.NMPB.2014.6

: 21 | : 9
Show more information
 Real-time multimodal command processing and interaction Result description The Realtime HRI is containered in FB –structures (Realtime HRI-Core FB-Set).
 Demonstrator 2: Human-Robot Collaboration on Aircraft Rib Assembly Result comments Optimisation on machine level Collaborative Product-Service Factories Increasing human achievements in manufacturing systems
 Autonomous /online/realtime Manufacturing Process Optimisation on machine level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on machine level
 Added Value from user perspective Taxon description or the KPI perspective (what KPI is the focus of the optimisation).

Associated results & demonstrators

Show all
Showing 10 out of 15
Toggle all information

Fortissimo Experiment 707 | Cloud-based-HPC optimisation of manufacturing processes

Project: Fortissimo 2

Type:

Updated at: 23-06-2018

Show more information
Title Cloud-based-HPC optimisation of manufacturing processes
  Autonomous /online/realtime Manufacturing Process Optimisation on machine level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on machine level

APS | Advanced Predictive System for Plastic Injection Moulding Industry

Project: PREVIEW

Type: /

Updated at: 18-07-2018

Show more information
Description The APS system applies novel and advanced artificial intelligence and machine learning techniques which create the robust and accurate prediction and optimisation of the injection moulding processes compared
  Autonomous /online/realtime Manufacturing Process Optimisation on machine level Taxon title Autonomous /online/realtime Manufacturing Process Optimisation on machine level
© 2020 Chris Decubber BV