Collaborative Projects

The objective of this project is to develop new materials for 3D printing technologies using plastic waste from industrial processes. The initiative is framed within the principles of the circular and sustainable economy and addresses the need to expand the range of materials available for 3D printing, overcoming current limitations in quality and technical properties found in many commercial options.

This industrial research project is focused on applying Artificial Intelligence and Big Data techniques to optimize production processes. Its main goal is to reduce energy consumption in plants by addressing different areas: shortening cycle times, reducing scrap, reusing energy resources, and improving the efficiency of equipment and processes. All these actions are aligned with the principles of the circular economy, promoting a more responsible and sustainable use of resources.

The automotive sector is highly competitive and requires continuous improvement in both production processes and the overall value chain. In this context, three strategic axes in manufacturing are product quality (NOK), process efficiency (OEE), and downtime prevention through predictive maintenance. Any failure in these areas can have significant consequences not only for Linde Wiemann but also for its clients and suppliers.

The main objective of this project is the development of an innovative monitoring system for machining processes in ferrous and non-ferrous materials, enabling the reuse of waste generated in the form of shavings and transforming them into new materials with industrial applications. The initiative is structured in two phases, covering both the analysis of the shavings and their reprocessing to validate their technical and functional feasibility.

This project aims to explore flexible additive manufacturing technologies by incorporating advanced robotics, intelligent systems, computer vision, artificial intelligence, and digital twin applications into the use of sustainable materials. The integration of these technologies will optimize 3D printing processes toward greater efficiency, sustainability, and zero-defect manufacturing, significantly reducing the environmental impact of these operations.

The COVID-19 pandemic created widespread fear of using enclosed spaces with high human traffic, directly impacting transportation systems and shared vehicles. While public transportation such as metro and buses experienced only a moderate decline in users, private companies with shared vehicles, hourly rentals, or internal corporate fleets saw drastic drops in usage, jeopardizing the viability of some operators.

The main goal of this project is to carry out industrial research into the application of Artificial Intelligence (AI) algorithms to mold operations in plastic injection and blow molding processes. The solution seeks to increase competitiveness by predicting events, enabling predictive maintenance, optimizing setup and adjustments, and improving production parameters. This will allow companies in the plastics value chain to digitalize mold-related production processes, efficiently connecting their operations.

In the automotive sector, in-line quality control systems capable of inspecting and verifying total production are essential. Failures at this stage can halt manufacturing and cause significant financial losses. Currently, final quality control—performed at the finishing line before vehicle delivery—faces two major challenges: the high level of manual intervention in rework operations (affecting 45–50% of vehicles) and the impossibility of using traditional robots due to the need to work without fencing, in environments with high human mobility.