Experiment 14: Digital Twin to optimize design of cars braking system assembly line (DIGITBREAK)

Motivation of the Experiment

Producer of manufacturing assembly lines faces unprecedented challenges in optimizing design and engineering to meet evolving market needs while ensuring, efficiency and productivity challenges.

  • Providing a first-time right architecture, by balancing processes, sub-processes, buffers sizes;
  • Providing the right mechanical solutions, by predicting critical problems before physical equipment testing to avoid wrong sizing/speed, force, torque/potential interferences, collisions or defect;
  • Guaranteeing performances (rates, availability, quality) complying with customer’s requirements;
  • Enabling the re-configuration of the machines to support different scenarios: new product variants, revamping, technology upgrading, by estimating the impact on the current design.

Experiment aims at solving these challenges by

  • Developing, integrating and demonstrating a novel Simulation Based Digital Twin of a car brake assembly line production system leveraging EnginSoft, Cosberg and MADE competences
  • Demonstrating advantages of such SBDT approach to new potential customers through the integration with DIGITBrain;
  • Enhancing Cosberg ability to design customized solution allowing for improved cost efficiency; time consuming, time to market, quality and reliability;
  • Developing a new MaaS business model, based on SBDT deployment on the Digital Agora
  • Exploit DIH Test Before Invest scenario, facilitating end user market exploitation and replication

Purpose of the Experiment

The purpose of the experiment is to exploit digital twin to accomplish the following manufacturing scenario for the end-user:

 

Scenarios Application fields Key-Users Drivers KPI Target Simulating Approaches
1. Performance Green field
Brown field
Designers
Production
Manager
Architecture
Bill of Material
Technologies
Process parameter
#1 Throughput
(Pieces/minute)
Digital and Physical
2. Downtime Green field
Brown field
Designers
Production
Manager
Technologies
Process parameter
#2 Availability
(Running time/total time)
Digital
3. Quality Green field
Brown field
Designers
Production
Manager
Technologies
Process parameter
#3 Quality Index
(Pieces OK /total pieces)
Theoretical
4. Revamping
Technological
Upgrade
Brown field Designers Architecture
Bill of Material
Technologies
Process parameter
#4 OEE
(Throughput,
Availability,
Quality)
Digital
5. Resource
Optimisation
Brown field Production
Manager
Bill of Material
Process parameter
#5b Material
(Bill of devices)To be monitored:
#4 OEE
Physical

 

The project uses a Simulation Based Digital Twin (SBDT), based on Discrete Event Simulation models (DES) relying on engineering. knowledge and data to represent the behaviour of the plant. The underlying simulation model is used to obtain high-fidelity predictions, including production forecasts of new scenarios for which no past data are available. DES model requires limited computational resources: a full day of production of an assembly line with a complex production mix is simulated within seconds on a laptop. Exploiting the Test Before Invest the project will deploy different approaches:

  • Digital and Physical: digital simulation of events and physical application by real testing on the pilot machine (scenario 1 – Productivity, scenario 5 – Revamping, scenario 4 – Resources Optimization);
  • Only Digital: digital simulation of events, without any chance to test physically the solution (scenario 2 - Downtime);
  • Theoretical evaluation: study the effects of simulating new events and haw could affect target KPIs (Scenario 3 - Quality).

Technical Impact

The experiment follow Test Before Invest (TBI) methodology7 deploying use case installed at MADE Competence Center - Digital Innovation Hub. This scenario allow for simulation in real operational environment (TRL 7) minimizing technical and business risk of technology transfer to market both for end user and Individual Software Vendor.
The experiment builds on the following blocks: (i) pilot assembly line at MADE - TBI (ii) line monitoring system and data flow thereof, (iii) information synthesis layer, (iv) discrete event simulation models of the assembly line, (v) DIGITbrain framework, including the Digital Agora.

Expected Economic Impact

Independent Software Vendor will integrate Models and Algorithms (section 3 and 4) into DIGITBrain, and publish them in the Digital Agora where new clients can access them. Thanks to the use case developed within Digital Agora, EnginSoft will demonstrate the advantages of a Simulation Based Technology to potential customers, and enable innovative business models using the Agora. As such, Enginsoft will take advantage of the marketing, support, and services provided by DIGITBrain operators. Enginsoft will benefit from Digital Agora as an additional marketing tool to penetrate the manufacturing market sector at EU scale.

End user. The project will integrate the digital library, composed of digital models of the MADE pilot line, into the Digital Agora private repository. Cosberg will use models accessing the DIGITBrain. Building on the experiment results, Cosberg might replicate the developed methodology to other assembly systems in similar and different sectors (e.g. white goods, furniture industries after the end of the project. The DIGITBrain platform will open the possibility to implement a MaaS business model leading to potential codesign scenario with clients (e.g. global producer of automotive brakes). It will result in a faster and optimized feedback to customer request, increased performance, productivity and cost efficiency in the engineering phase. The Digital Agora will consolidate Cosberg leadership with existing clients.

Project Partners

 

Croom Precision Medical

serves as the experiment end user.

Montimage

serves as the technology partner.