Research Projects


Additive Manufacturing Quality and moniTOring cOntroL System




The AMQ_TOOLS project’s results include validated quality-oriented predictive models that support the selection of optimal combinations of process parameters in advance and the in-process verification of quality results. The models, which are integrated into the CPS, enable improvements in critical manufacturing KPIs such as lead time (e.g. reducing the need for downstream quality inspections and reducing production time) and component quality (e.g. reducing porosity). AMQ_TOOLS addresses Additive Manufacturing (AM) quality control with a decision support framework which integrates a Design for AM (DfAM) and a process simulation software solution (ANSYS Additive Suite), with the Smart ProdACTIVE digital platform that connects the AM machines (EOS M290) with quality assurance requirements and quality control data. Smart ProdACTIVE is a fully integrated system, that links production processes with data sources (machines, sensors, HMIs), and uses traceability information to correlate this data with quality results. Therefore, ProdACTIVE will enable the smart monitoring of Additive Manufacturing processes through the application of quality-oriented predictive models and machine learning algorithms, which can be applied in real time. These models predict the presence of defects, by category, area and grade.

Project Summary

Metal Additive manufacturing is a complex operation that requires the fine-tuning of hundreds of processes parameters to obtain repeatability and a high quality design at dimensional, geometric, and structural levels. To help manufacturing companies address these challenges, AMQ_TOOLS develops an innovative quality-oriented decision support framework, composed of (i) a Model Based Design (MBD) tool with Design for Additive Manufacturing (DfAM) features, and (ii) a Cyber-Physical System (CPS) created from the integration of an Additive Manufacturing (AM) asset with a real-time smart monitoring software application. This framework caters to the needs of process engineers and quality managers to improve a set of quality and economic KPIs.


The AMQ_TOOLS project develops, integrates and demonstrates a quality-oriented decision support framework, that includes an in-process smart monitoring system that predicts the quality KPIs of metal Additive Manufacturing. The potential impact on manufacturing KPIs is manifold: reduced AM process lead time, reduced Cost of Quality, and Energy savings.

ES Role

EnginSoft's role concerns the development and operational phases of the AM QTOOLS project, specifically:

  • the creation of the quality-oriented framework based on the MBD approach with a catalogue of characteristics for aluminium parts printed with a Laser Powder Bed Fusion (LPBF) process, and common defects as information enhanced by the knowledge generated by the CPS.
  • implementation of Decision support with predictive models embedded in Smart ProdACTIVE to track process outputs and, through advanced data analytics, to provide information on the expected quality results of a specific setup.
  • the validation of the data-driven digital twin (Smart ProdACTIVE) to monitor and validate the improved design of parts on the shop floor.



Funding Scheme

Sub-grant agreement of Digital innovation HUBs and CollAborative Platform for cyber-physical systems (HUBCAP)
Call identifier
DT-ICT-01-2019, HUBCAP Open Call #3: HUBCAP-OC3-2021/1556275, sub-grant agreement 872698


Project web site

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12 months


October 2021 – October 2022



Reference in EnginSoft

Giovanni Borzi

Partners Number


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