Project ideas - Digitized production
Join our project pitches on digitized production
09h00: AI-assisted CAD-to-CAM for Machining Processes (AutoCAM_SBO
This SBO project aims to develop an automated CAM-NC workplan generation system for new parts using machine learning. Skilled operators' knowledge and best practices for mainstream manufacturing processes will be used to create a model that can generate optimal workplans for new designs while accounting for machine constraints. The resulting "automated CAD-to-CAM system" is expected to increase the efficiency of the CAD-CAM workflow and reduce costs.
09h30: Scalable and Intelligent Corner Case/Scenario Detection in Complex Production Processes (CoDePro_SBO)
This project aims to improve human-robot collaboration and machine-to-machine interaction in manufacturing through automated validation of production processes. Agent-based modelling can be used to detect corner cases that may be dangerous for operators or costly due to faulty products or anomalies in the system. The project will leverage the reasoning capabilities of agents to increase reliability and reduce operating costs.
10h00: Automated generation of factory simulation models (AUTOFACTO_IRVA)
This project aims to automate the creation and analysis of factory simulation models, which is a difficult and time-intensive manual task. The model structure will be generated by discrete optimization and the model parameters will be estimated based on historical data, while sensitivity analysis will highlight the important simulation outputs to the user. The goal is to develop a generic methodology that can be applied to industrial company cases.
10h30: IIoT architecture for multi-site data collections and personalized dashboards (Dashboard_IRVA)
The Flemish manufacturing industry is looking for new approaches to quality monitoring due to the time-consuming and costly nature of traditional methods such as SQC and SPC. Recent developments in industrial IoT, sensing solutions, interconnected machinery, and smart tools provide an opportunity for faster feedback and even predictive quality control. The project aims to investigate and validate an IoT-enabled system architecture for inline quality monitoring and dashboarding on Flanders Make research infrastructure and industrial production systems.
11h00: Data-Driven Fast Optimization of Small-Batch Production (OptiPro_SBO)
This project aims to use machine learning and AI to optimize parameter settings in production processes that involve thermal effects on materials, such as welding, laser cladding, robomoulding, and hot-embossing. The traditional approach to optimizing process parameters, using design of experiments, can be tedious and time-consuming. The goal is to leverage expert knowledge and experience to develop more efficient and effective methods of tuning process parameters.
11h30: Industrial valorisation of non-native robot programming methods (VALPROG_IRVA)
The Flemish manufacturing industry is facing challenges including reshoring, an aging workforce, faster product evolution, increased productivity, and sustainability. Advanced robot programming methods developed by Flanders Make can help address these challenges. The VALPROG project aims to bring reusable results to a higher technology readiness level by combining research and application projects with the involvement of various companies in the industry.