Multidimensional Print Job Splitting by Vulnerability Metrics
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Solution Overview
Problem
Multidimensional printing technologies face high failure rates due to vulnerabilities in virtual printing models, leading to wasted filament and time, as well as increased costs and wear on printers.
Innovation Solution
A computer-implemented method for splitting a multidimensional print job based on vulnerability metrics, using a machine learning model to predict printing failure risk and prioritize print layers with high risk scores for early printing, thereby minimizing waste and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If print layers are processed in traditional order, then printing process is simple, but printing failure rate is high causing waste of filament and time
Solution Approach 1:
The system performs preliminary analysis of the virtual printing model to identify vulnerable locations and calculate printing risk scores before actual printing begins. This advance preparation allows high-risk areas to be prioritized in the print queue, preventing failures before they occur and reducing waste of filament and time.
Solution Approach 2:
The print job is segmented into different priority queues based on printing risk scores. High-risk print layers are separated into a high-priority queue while low-risk layers go to a standard queue. This segmentation allows the system to dynamically adjust printing order based on failure probability without requiring complete redesign of the printing process.
2Loss of time
If high-risk print layers are printed first, then printing failure impact is reduced, but print job management becomes more complex
Solution Approach 1:
The system implements feedback mechanisms where printing results and failure data are continuously collected and used to update printing risk scores for future print jobs. This feedback loop allows the system to learn from past failures and improve its prioritization strategy over time, reducing time wasted on failed prints while maintaining manageable complexity through automated adjustments.
Solution Approach 2:
The system dynamically changes the printing order parameter based on calculated risk scores. By adjusting the sequence in which print layers are processed according to their vulnerability metrics, the system minimizes time loss from failures without requiring complex manual intervention, as the parameter changes are automatically determined by the risk assessment algorithm.
3Reliability
If printing risk assessment is performed, then printing reliability improves, but computational resources and processing time increase
Solution Approach 1:
The system performs risk assessment selectively, focusing computational resources on identifying and analyzing vulnerable locations rather than uniformly processing all print layers. By concentrating analysis on critical areas that are most likely to fail, the system achieves improved printing reliability while minimizing unnecessary computational energy consumption on low-risk regions.
Data Source
AI summary
A computer-implemented method, a computer system and a computer program product split a virtual printing model according to a probability of multidimensional printing failure. The method includes obtaining a virtual printing model of a multidimensional object, where the virtual printing model comprises print layers. The method also includes determining that a printing risk score for a location on the multidimensional object is above a printing risk threshold. The method further includes mapping a set of print layers of the virtual printing model to the location on the multidimensional object. Lastly, the method includes sending the print layers of the virtual printing model to a multidimensional printer, wherein a mapped set of print layers is sent first.