Additive Manufacturing Process Planning for Temperature Variation Control
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Solution Overview
Problem
In additive manufacturing, existing process plans based solely on geometrical parameters often lead to overheating issues, resulting in cracking, deformation, and uneven crystal structures, and are inflexible, making it difficult to control temperature variations and improve yield rates.
Innovation Solution
A method and optimizer that build a predicting model to forecast temperature variations, allowing for real-time adjustments to the process plan by adjusting parameters like scanning path and energy input to meet preset conditions, ensuring optimal temperature control during the manufacturing of workpieces in layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a process plan is made based only on geometrical parameters, then the manufacturing process is simple to execute, but temperature control is poor leading to overheating and quality issues
Solution Approach 1:
The system performs preliminary temperature prediction and process plan optimization before actual manufacturing begins. By calculating expected temperature variations in advance and adjusting the process plan proactively, the system prevents overheating issues before they occur, thereby improving temperature control accuracy while maintaining manufacturing simplicity
Solution Approach 2:
The system establishes a feedback loop where temperature predictions from the prediction model are continuously compared with preset conditions, and the process plan is dynamically adjusted based on these predictions. This closed-loop control enables precise temperature management without complicating the manufacturing execution
2Reliability
If a process plan is made without real-time adjustment capability, then the manufacturing process is stable and straightforward, but the yield rate cannot be improved due to inability to respond to temperature variations
Solution Approach 1:
The system transforms the static process plan into a dynamic one by enabling real-time adjustments based on temperature prediction results. The optimization module can modify manufacturing parameters during the process while maintaining overall process stability, thus improving yield rate without sacrificing reliability
Solution Approach 2:
The system optimizes manufacturing parameters such as laser power, scanning speed, and hatching patterns based on predicted temperature variations. By dynamically changing these parameters in response to temperature predictions, the system improves yield rate while maintaining process stability through controlled parameter adjustments
3Productivity
If no temperature prediction is performed, then the manufacturing process proceeds quickly without optimization steps, but overheating occurs causing cracking and deformation
Solution Approach 1:
The system performs rapid temperature prediction calculations before manufacturing each layer or region, allowing it to identify potential overheating zones in advance and adjust parameters proactively. This preliminary action prevents overheating damage while minimizing impact on manufacturing speed through efficient prediction algorithms
Data Source
AI summary
A process plan optimization method for manufacturing a workpiece by adding a material in a plurality of layers is provided. The method includes: building a predicting model, the predicting model configured to predict a temperature variation of at least a portion of the workpiece; predicting an expected temperature variation of the portion of the workpiece to be manufactured during a given time period based on the predicting model and the process plan; and adjusting the process plan in response to the expected temperature variation of the portion failing to meet a preset condition, to make the expected temperature variation of the portion meet the preset condition.