smart ProdACTIVE

smart ProdACTIVE

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

smart ProdACTIVE

smart ProdACTIVE is a product of EnginSoft SpA.

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 - 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
History and Traceability
  1. Advanced sensors connection
  2. Web Data elaboration: charts, diagram
  3. Web Visualization: charts, diagram, 3Dviewer, etc.
  4. Thresholds, alarms, apps

COGNITIVE MODULE

Cognitive metamodeling
Re-Active optimization
  1. Cognitive quality model (product model and training methods)
  2. Re-active optimization
  3. Predicted data elaboration & visualization: charts, diagram, 3Dviewer… thresholds, alarms, apps
Energy and Cost model
  1. Energy consumption visualization
  2. Real-time cost elaboration during production
Web-service
  1. Customized connection to MES/ERP
  2. Cloud connectivity and Apps

Main benefits

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  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

Documentation

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smart ProdACTIVE

technical paper

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).

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smart ProdACTIVE

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.

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smart ProdACTIVE

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Insights

CASE STUDY

Structural Optimization of the Drift Chamber at FermiLAB

A collaboration between EnginSoft and the Italian Institute of Nuclear Physics (I.N.F.N.)

The ultimate goal of the study was to optimize the Drift Chamber’s performance in terms of stiffness, strength and weight o be mounted on the Mu2e particle detector at FermiLAB in Chicago

construction modefrontier ansys optimization energy

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CASE STUDY

Using multidisciplinary optimization for engine suspension stiffness

Optimizing idling and ride comfort

In this technical article, Fiat Chrysler Automobiles explain how they created a multibody optimization project to identify the optimal values for the powertrain suspension stiffness for a three-cylinder engine in order to minimize the vibrations at idle condition and ensuring greater ride comfort to the passengers.

automotive optimization modefrontier

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Our competences in smart ProdACTIVE

CASE STUDY

Optimizing a cam mechanism using Adam and MATLAB

The design of a cam for high-speed production machines with various operating criteria imposes various conflicting objectives

This technical case study explains the application of a two-step methodology using the MATLAB and Adam algorithms in the modeFRONTIER software platform.

optimization modefrontier automotive

CASE STUDY

Optimization of an automotive manufacturing system design taking into account regional requirements

Applying CAE to facilitate business CapEx decision making in the automotive manufacturing sector

In this case study, EnginSoft engineers explain how they used modeFRONTIER to assist Comau, a Fiat Chrysler subsidiary, to optimize their approach to the preliminary design of production systems for automotive manufacturing system RFQs.

automotive optimization rail-transport modefrontier SIMUL8 iphysics industry4

CASE STUDY

Shape Rolling of a European Standard Beam IPE Profile

A pilot study carried out by engineers from EnginSoft and Beltrame

The study allowed the engineers to estimate the shape profile geometry after each roll pass, the rolls wear and the total load and point at which the beam will bend in off-center setups.

construction metal-process-simulation forge mechanics