Introduction to Digital Manufacturing

The Digital Transformation and Manufacturing Engineering are innovative elements of the Industry 4.0 strategy and application of Enabling Technologies with particular reference to “Simulation and process optimization of interconnected machines” and “Big data analytics to optimize products and production processes”.

The Cognitive manufacturing is an emerging frontier of engineering science that integrates domain knowledge from industrial and systems engineering, manufacturing process science, computer learning, information technology, adaptive control theory, biologically-inspired system design and environmentally cognizant design and sustainability.

The process level address the development of distributed intelligence agents at the discrete process level considering techniques for “perception” through sensor fusion, including feature-level fusion, and explore learning algorithms for assessing process condition, process health and prognostication.

The computer-enabled decision making supports the Operator to take the corrective actions in quasi real-time.




  • Control the all the stages of complex production processes (e.g. Casting)
  • Stability of the process
  • Application to the existing traditional processes (e.g. casting)
  • Improve the production efficiency (OEE),
  • accelerate the process fine-tuning (optimization)
  • real-time adjustment of the process parameters
  • oriented to the zero defect quality (real time prediction)
  • Improve process knowledge present in the data
  • Re-use the knowledge to flexible production
  • Reduce energy consumption
  • Short time-to-market
  • Cost reduction

A completely new ICT platform based on innovative Control and Quality model prediction in production line

The “smart Prod ACTIVE” 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 Prod ACTIVE” 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 real-time visualization of elaborated data, including safety messages and statistic production diagrams, will be appropriately customized for multiple users’ interfaces as machine operator, production manager and plant director. The standardization Quality classification and investigation methods, as well as the traceability, are fundamental to train the Cognitive model guiding the minimization of relevant indexes affecting the scrap rate. 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.

The platform has been tested in high pressure die casting (HPDC cell) and plastic injection moulding (PIM) production line. It’s flexible and extendible to further multi-stages production lines integrating the control of different devices. Example: Foundry 4.0 - Smart casting process control and real time quality prediction


Innovation by smart ProdACTIVE

Full remote control of multi-stages production process

Deeper knowledge of production process to support the Reactive actions:

  • max production efficiency; no failure, no pause, reduced cycle time...
  • stability of production; max quality reproducibility
  • best die life;
  • intelligent reaction/optimization

Process data management: Traceability and statistical elaboration of quality and cost

Flexibility in production: re-use and re-start production; multi plants company

Real-time quality prediction; Scrap reduction; quality control at 100%

Reduction of time and cost of Quality control (the quality control is applied only for the predicted products)

Process optimization and reduction of energy consumption

Reduction of time to market and minimization of trial&error approach

Why smart ProdACTIVE

ACTIVE real-time application of Quality model prediction

Re-ACTIVE process Cognitive optimization and retrofit

Fully integrated system in the multi-stages PRODUCTION line

Centralized PRODUCTION control via Intelligent Sensors Network (ISN)

SMART and flexible database to acquire and manage the process data

SMART web-based application for remote control of the process stability, efficiency, energy consumption and cost

SMART web-service interface with MES or ERP

The system is capable of reacting in real-time to variable process conditions, describing and predicting the behavior of the process in terms of output quality for any combination of control parameters and sensor measurements.

The system eventually proposes a set of suitable solutions for optimizing the manufacturing process, that is, tuning the control parameters in order to achieve better quality, better energy efficiency and to reduce the total costs.

The system shall also visualize real-time and historical data such as, but not limited to: statistics related to the process (production efficiency, number and types of defects, etc.), safety messages, sensor measurements or data related to item inspection.

Benefits and metrics


To improve production quality
To minimize energy consumption
To reduce total costs

Higher Quality, Faster Delivery Times
Efficiency, Robustness
Minimum Energy Consumption
Real-time Monitoring
Active Control of Quality
Lower Total Costs



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




For more information please contact:

Giovanni Borzi

EnginSoft - Competence Centre Padova
Via Giambellino 7, 35129 PADOVA
+39 049 7705 311