3D Printing Material Reuse via ML Optimization
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Solution Overview
Problem
Current 3D/4D printing processes face challenges in efficiently reusing materials due to quality issues with used/spent filaments, leading to costly waste and environmental concerns.
Innovation Solution
A method involving a knowledgebase corpus database and machine learning optimization models to analyze material and geometric requirements of printed objects, generate output models for optimized printing, and promote the reuse of 3D/4D printed pieces by slicing and dicing them into reusable blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If materials are reused from previous printing processes, then resource efficiency improves, but quality issues arise due to degradation of used filaments
Solution Approach 1:
The system segments the printing process into distinct phases (design, material selection, printing, post-processing) and further segments material management into virgin material usage, partial reuse, and complete disposal. This segmentation allows optimized material allocation where critical components use virgin material while non-critical parts can utilize recycled material, resolving the contradiction between waste reduction and quality maintenance.
Solution Approach 2:
The patent applies local quality by allowing different material qualities in different regions of the same printed object. Critical structural components are printed with virgin high-quality filament, while non-critical decorative or structural elements use recycled filament. This spatial differentiation of material quality enables simultaneous achievement of overall quality standards and waste reduction.
Solution Approach 3:
The system changes material parameters (purity, strength, consistency) based on the specific application requirements. By adjusting the proportion of virgin versus recycled material, and modifying printing parameters such as temperature, speed, and layer height, the system optimizes the balance between material reuse and print quality for each specific printing job.
2Productivity
If advanced machine learning models are used for optimization, then printing efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements a universal machine learning platform that handles multiple functions: predicting print quality, optimizing material selection, determining reuse strategies, and calculating cost implications. This single multi-functional system replaces what would otherwise require multiple separate specialized systems, achieving high productivity while managing complexity through consolidation.
Solution Approach 2:
The machine learning model performs self-training and self-optimization by learning from historical printing data, material performance records, and quality outcomes. The system automatically adjusts its parameters and recommendations without requiring manual reconfiguration, enabling high productivity while minimizing the operational complexity burden on users.
3Object-affected harmful factors
If complete reuse of printed materials is implemented, then environmental impact reduces, but manufacturing precision deteriorates due to material degradation
Solution Approach 1:
The patent implements a selective discarding and recovering strategy where materials are recovered (reused) for applications where their degraded properties are acceptable, and discarded for applications requiring high precision. The system evaluates each material batch's remaining quality and matches it to appropriate reuse applications, maximizing environmental benefit while maintaining manufacturing precision where required.
Solution Approach 2:
The system performs preliminary assessment and characterization of recycled materials before they are used in printing. By pre-evaluating material properties, sorting materials by quality grade, and pre-mixing recycled materials with virgin materials in appropriate ratios, the system ensures that material degradation does not compromise manufacturing precision in critical applications.
Data Source
AI summary
A method, computer system, and a computer program product are provided for multi-dimensional printing. A printing request for printing tan object with at least one component is received. Information is obtained about the specifics of the object from a knowledgebase corpus database including at least a material requirement for printing of the object. The number of associated components and material and geometric requirements of each component and the object is analyzed. An output model is generated using at least one machine learning optimization model. The output model is generated based on the information obtained about the object and the analysis of the object's geometry and material requirements. The output model is stored in the knowledgebase corpus database.