smart ProdACTIVE

ICT platform to real-time QUALITY PREDICTION and OPTIMIZATION supported by TRAINED COGNITIVE MODE

The smart ProdACTIVE tool predicts the quality, energy and cost of the injection process in real-time, covering the 100% of products, and suggests the appropriate re-actions to adjust the process set-up and/or mechanism. It works in combination with the real time monitoring system (or Intelligent Sensor Network) to elaborate instantaneously the production data set with respect to quality/energy/cost prognosis. The client-server mechanism works in combination with the real time monitoring system (or Intelligent Sensor Network) to elaborate instantaneously the production data set with respect to quality/energy/cost prognosis. The client-server Connections, based on OPC_UA protocol that is accepted as Interface for Industry 4.0, are collecting all process data coming from all existing devices and active sensors in a centralized database.

A fundamental innovative characteristic of smart ProdACTIVE tool is the Cognitive predictive quality model integrating multi-resolution and multi-variate process data, monitored and gathered by an articulated network of sensors by means of the collection of distributed control system, advanced models linking process variables to specific defect generation mechanisms, new optimization tools and remote management of production by self-adaptive equipment. The final tool is a smart web application to visualize, share and communicate the significant data and to support the decision making with proper reactions in real-time (retrofit) based on the captured signals from the process and intelligent elaboration of data by the quality model.

smart ProdACTIVE is a product of EnginSoft SpA

smart ProdACTIVE

ICT platform to real-time QUALITY PREDICTION and OPTIMIZATION supported by TRAINED COGNITIVE MODE

MONITORING MODULE

Database

  1. Real-time acquisition DB
  2. Hystorical data storage

Sensors connectivity; Smart Data visualization Traceability

  1. Advanced sensors connection (Machine, Die, Thermocamera, ThermoUnit, etc., customized)
  2. Web Dataelaboration&visualization: charts, diagram, 3Dviewer… thresholds, alarms, apps

CONTROL MODULE

Cognitive data model

Re-Active optimization

  1. Cognitive quality model (product model and training methods); re-active optimization
  2. Web Data elaboration & visualization: charts, diagram, 3Dviewer… thresholds, alarms, apps

Energy and Cost model

  1. Energy consumption visualization
  2. Real-time cost visualization during production

Predictive maintenance

Machine and tooling monitoring and data-driven prediction of possible failure

Web-service

  1. Customized connection to MES/ERP
  2. Remote web connectivity and Apps
smart ProdACTIVE

Main product modules

Main Benefits

  Faster Delivery Times

  Real-time Monitoring Active

  Scrap Rate: the involved HPDC foundry is expecting for a 40% reduction in scrap rate

  Production: flexibility, stability and efficiency is generating 10% of no-quality-cost

  Quality Control: in good exploitation scenario the cost of quality control can decrease of 40%

  Energy: energy consumption will be reduced by 5-10%, due to scrap reduction and more production efficiency with reference to the single cast part

 

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Innovative control and real-time quality prediction for the casting production of aluminum alloy structural components

This work was developed within the “MUSIC” Project (MUlti-layers control & cognitive System to drive metal and plastic production line for Injected Components), supported by European Union [FP7-2012-NMPICT-FoF] under grant agreement number n°314145. The authors would like to thank the MUSIC consortium (www.music.eucoord.com).

  Read the technical paper

Real-time HPDC quality prediction and optimization supported by trained cognitive model

This work was developed inside “MUSIC” Project (MUlti-layers control & cognitive System to drive metal and plastic production line for Injected Components), supported by European Union - [FP7-2012-NMP-ICT-FoF] under grant agreement number n°3141. The authors would like to thank the MUSIC consortium (www.music.eucoord.com), and particularly RDS Moulding Technology SpA.

  Read the technical paper

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