20180918 FHWN 2016 64037 BEA Himmelverlaengert Dunkelblau ansicht

Masoud Shaloo

BSc MEng

Masoud Shaloo

BSc MEng


Lecturer / Researcher Department of Industrial Management


Campus 1 Wiener Neustadt
Johannes Gutenberg-Straße 3
2700 Wiener Neustadt

Research Activities

  • ProcessIQ

    The aim of this project is to search, evaluate and test various technologies for the automated (AI-supported) detection of quality defects and anomalies in the process data in two product lines of a manufacturer of special machines.

  • DigiProTrain

    This project aims to develop and offer training courses in the field of Industry 4.0 for companies in so-called learning factories. Learning Factories represent a realistic manufacturing environment suitable for education, training, and research. They are a physical learning environment containing educational instruments and equipment, effectively creating the working conditions of a real industrial site for didactic and training purposes. The Networks Development initiative has the purpose of creating an EIT Manufacturing Marketplace that offers all the Learning Factories’ education programmes and activities. Thereby increasing the outreach and access to the Learning Factories training offer, as well as incrementing the availability of equipment suitable for testing and piloting. Funded by EIT Manufacturing as part of the Learning Factory Network Development Program.

  • IntelliProPS

    The aim of this project is (1) the research and development of new planning and control concepts based on the combination of classic methods of planning and control with methods of artificial intelligence (especially machine learning) for production scenarios with very high volatility and variability with regard to product variants, order quantities, raw material/part quality, with a simultaneous high degree of automation and a high degree of human-machine collaboration, (2) the development of a configurable simulator and demonstrator that allows production companies to use various decentralized planning and control concepts enriched with AI for production scenarios with high variability and To test, evaluate and further develop volatility in terms of product variants, order quantities, raw material/part quality (development and test bench for AI-enhanced production planning and control concepts). This project is funded by the FFG as part of the COIN construction FH for the economy program.