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Manufacturing

Production Digital Twin

New product development was gated by physical machine availability. the only way to test a configuration was to make it, which meant R&D competed directly with production for capacity.

Timeline
Phased 6 month build: data structuring and model calibration in months 1, 3, simulation interface and validation against known historical outcomes in months 4, 5, live deployment and R&D team onboarding in month 6. Ongoing model refinement as new trial data is generated.
Scope
Historical sensor data audit and structuring (3 year archive), physics informed model architecture and calibration to specific machinery configuration, R&D simulation interface build, real time sensor integration, glass fibre hybrid model calibration using available trial data, knowledge transfer and training for R&D and production engineering teams.
Model
R&D engineers operate the simulation interface independently. Model recalibration triggered quarterly using new physical trial data, the twin improves as the product portfolio expands. Production engineering team retains final sign off on all configurations before physical validation trials are commissioned.

The outcome

Product configuration trials reduced from 8 to 12 physical runs to 2 to 3; development cycle time cut by 60%.

SENSOR DATALine speedNeedle penetrationTemperature profilesCalender pressureWeight and tensiletestsDIGITAL TWINPhysics-informed ML modelCalibrated on historicalproduction runsParameter simulatorTarget spec to predicted outputmapOut-of-spec risk scoringConfidence ranges with adjustmentsOUTPUTSimulationinterfacePredicted output profileCompliance scoreParameter adjustmentsFewer physical trials

Findings

What we built it around.

01

Sensor data integration layer, three years of historical production data (line speed, needle parameters, temperature profiles, weight, tensile results) structured and ingested

02

Physics informed ML model calibrated against production history for the specific needlepunch machinery configuration

03

Parameter configuration simulator, R&D interface for specifying product targets and receiving predicted output profiles

04

Out of spec probability scoring with recommended parameter adjustment outputs

05

Real time synchronisation with active production sensor feeds, twin updates as production conditions change

06

Glass fibre reinforced hybrid construction model layer (separately calibrated using reinforced product trial data)

Results

What changed.

01

Physical trial runs per new product configuration reduced from an average of 8, 12 runs to 2, 3 validation runs, a 75% reduction in physical development iterations

02

New gramage variant development cycle time cut by 60%, from an average of 14 weeks to 5, 6 weeks from concept to production ready specification

03

Primary line capacity recovered from R&D trial use, equivalent to approximately 8, 10 additional production days per quarter

04

Glass fibre reinforced hybrid product development accelerated significantly: first viable Orbond Plus equivalent configuration achieved in 3 physical validation runs vs

11 runs for the previous standard product development cycle

05

Institutional process knowledge formalised into a queryable model, reducing dependency on individual senior engineers and creating a transferable knowledge base for operator development

Takeaway

Product configuration trials reduced from 8 to 12 physical runs to 2 to 3; development cycle time cut by 60%.

Manufacturing

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