Additive Manufacturing Process Planning With Robot Trace-Pen Training
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Solution Overview
Problem
Conventional additive manufacturing processes are complex and inefficient, requiring extensive re-planning and re-execution of the entire process chain when changes occur, especially in CAD/CAM systems, making it difficult to adapt to changes in components or preliminary products, and hindering the production of small quantities.
Innovation Solution
A method using a machine training tool, such as a 'trace pen', to translate analog movements of processing machines into digital data, allowing for modular and intuitive redesign of the manufacturing process, enabling individual optimization of each manufacturing station and facilitating transfer between stations with fixed transfer points, thereby simplifying the additive manufacturing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the entire process chain is re-planned and re-executed when changes occur, then manufacturing precision is maintained, but loss of time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining transfer points and reference edges in the CAD model before manufacturing. These pre-established reference elements allow for quick adjustments and re-planning when changes occur, eliminating the need to re-execute the entire process chain while maintaining precision.
Solution Approach 2:
The patent uses copying by creating digital twins and virtual models of the manufacturing process. These virtual representations allow for rapid simulation and adjustment of process changes without affecting the actual manufacturing timeline, enabling quick re-planning when component modifications are needed.
2Adaptability or versatility
If the manufacturing process is segmented into multiple stations, then adaptability improves for individual optimization, but device complexity increases
Solution Approach 1:
The patent applies universality by designing transfer points and reference edges that serve multiple functions across different manufacturing stations. These universal reference elements enable consistent positioning and alignment throughout the segmented process chain, allowing individual station optimization while maintaining system coherence and reducing overall complexity.
3Manufacturing precision
If extensive process chains are used for additive manufacturing, then manufacturing precision is maintained, but productivity decreases for small quantities
Solution Approach 1:
The patent applies segmentation by dividing the additive manufacturing process into distinct modular stations with clearly defined transfer points. This segmentation allows for streamlined workflows at each station, maintaining precision through standardized reference edges while improving overall productivity for small batch production by eliminating unnecessary process steps.
Data Source
Figure 1~2
AI summary
The invention relates to a method and a device for determining an additive manufacturing process for the additive production of a component, in which at least one manufacturing station is traversed in the additive manufacturing process, where at least one pre-product of the component is processed using at least one processing machine. A machine learning tool is used to train the processing machine to process the pre-product in order to determine the additive manufacturing process. The machine learning tool uses a translation aid to translate an analog movement of the processing machine for processing the pre-product into digital motion data of the processing machine. The processing machine is preferably a processing robot. The machine learning tool (robot learning tool) is preferably a trace pen.Furthermore, an additive manufacturing process is used to additively manufacture a component with the following process steps: a) Determining a manufacturing process and b) Additive manufacturing of the component.