The CLASCO project aims to revolutionise additive manufacturing (AM) through a digitalised, climate-neutral approach using laser-based surface functionalisation. Z Prime is implementing a universal, AI-driven digitalised process plan for creating functionalised AM parts with complex geometries, focusing on improving efficiency, quality, and environmental sustainability.

Challenges

The additive manufacturing industry often grapples with inefficiencies due to repetitive trial and error activities, which are costly and wasteful. The complexity of the additive manufacturing process means new methods and technologies are required to ensure end-to-end precision, control and consistent quality and minimise inspection costs and production delays. The challenge is to develop a solution that enhances the efficiency and sustainability of additive manufacturing processes.

The Project

The CLASCO project integrates advanced in-line monitoring solutions to provide real-time data on the manufacturing process, ensuring precise control over surface functionalisation. By incorporating machine learning algorithms, the project optimises laser controls, reducing the need for manual adjustments and minimising production errors. The project digitalises the entire manufacturing process history, enabling efficient data acquisition and control feedback loops.

The adoption of sustainable practices to minimise the environmental impact of the manufacturing process also sees the CLASCO project supporting the transition to climate-neutral industrial operations. These integrated approaches ensure that the production of complex additive manufacturing parts is precise, efficient, and sustainable.

Z Prime’s Contributions

Z Prime focuses on developing advanced AI strategies for improving laser-based processes, such as L-Polishing and L-Surface functionalisation, using a data-driven approach. The company implements a data integration level for different geometry, process, machine, and sensor data, and develops a Decision Support System (DSS) to be tested and validated in real production conditions.

Z Prime’s platform integrates AI and machine learning to optimise process parameters, reducing the need for manual adjustments and minimising production errors. The platform also supports the digitalisation of the manufacturing process, enabling efficient data acquisition and control feedback loops for enhanced process optimisation.

Benefits

• Reduced Trial and Error: Minimises repetitive trial and error activities, significantly cutting-down on waste and production costs.
• Enhanced Precision: Ensures precise control over the production of complex AM parts through real-time monitoring and machine learning.
• Lower Inspection Costs: Reduces inspection costs by providing detailed digital records and real-time data analysis.
• Improved Efficiency: Enhances manufacturing efficiency through optimised process control and reduced production errors.
• Sustainability: Supports climate-neutral practices, reducing the environmental footprint of the manufacturing process.

Conclusion

The CLASCO project sets a new standard for additive manufacturing by integrating digitalisation and climate-neutral practices with advanced laser-based surface functionalisation. By leveraging in-line monitoring and machine learning, CLASCO enhances the precision, efficiency, and sustainability of producing complex AM parts. This innovative approach not only addresses the critical challenges of the industry but also supports the broader transition to sustainable and digitalised industrial practices across Europe. The successful implementation of CLASCO demonstrates its potential to transform additive manufacturing, ensuring higher standards of quality, efficiency, and environmental responsibility.