US-12617155-B1 - Adaptive object fabrication using enriched voxel-based digital twins and AI-guided material substitution

Abstract

The present invention relates to adaptive object fabrication using enriched digital twins that incorporate voxel-based metadata tailored to environmental conditions and material performance. A machine-learned artificial intelligence (AI) engine selects substitute materials and predicts failure modes based on environmental context data, such as temperature, vibration, and chemical exposure. These insights are embedded into a voxel-based model as prescriptive metadata, generating an enriched digital twin configured for precise additive manufacturing. Secure transmission, cryptographic authorization, and compliance indexing ensure controlled and traceable fabrication. Unlike prior approaches, the system transforms digital twins into machine-executable fabrication directives that adapt dynamically to deployment-specific requirements. The invention enables context-aware object replication with improved survivability, regulatory compatibility, and manufacturing efficiency.

Inventors

• Abubakr Y. Alkhalifa
• Mohamed Yousif Elkhalifa

Assignees

• SGM Infotech, LLC

Dates

Publication Date

20260505

Application Date

20250812

Claims (20)

1. 1 . An adaptive object fabrication method comprising: receiving environmental context data representing expected deployment conditions associated with a physical location in which a fabricated object is intended to be deployed; receiving a digital twin of a physical object, wherein the digital twin comprises a voxel-based model with geometric data; applying an artificial intelligence (AI) model that is machine-learned to the digital twin and the environmental context data to select one or more substitute materials for reconstructing the physical object, wherein the AI model is trained to predict object performance and failure risk under environmental stress conditions based on historical degradation data; generating an enriched digital twin version of the digital twin by annotating the voxel-based model with metadata corresponding to the selected substitute materials and predicted performance attributes; transmitting the enriched digital twin to an additive manufacturing system configured to fabricate a physical instance of the object using the selected substitute materials in accordance with the voxel-based model, including the metadata; collecting performance data from a deployed instance of the fabricated object; and retraining the artificial intelligence model, for subsequent material substitution decisions, using a federated learning framework that aggregates performance data from multiple fabrication deployments.
2. 2 . The method of claim 1 , wherein the environmental context data includes data related to at least one condition selected from the group consisting of temperature, humidity, salinity, radiation, pressure, vibration, and chemical exposure.
3. 3 . The method of claim 1 , further comprising encrypting the enriched digital twin and assigning a unique identifier (UID) prior to transmission.
4. 4 . The method of claim 1 , wherein the environmental context data is manually entered, retrieved from a planning database, or derived from historical deployment records.
5. 5 . The method of claim 1 , wherein the AI model adapts over time by incorporating post-deployment feedback data to improve future material substitution outcomes.
6. 6 . The method of claim 1 , wherein the digital twin is received from a third-party design repository, edge-scanning system, or cloud-based engineering service.
7. 7 . The method of claim 1 , wherein the fabrication system comprises a multi-material additive manufacturing device capable of interpreting voxel-based material metadata.
8. 8 . The method of claim 1 , wherein the AI model is trained using a combination of synthetic simulation data and empirical stress test datasets.
9. 9 . The method of claim 1 , wherein the additive manufacturing system executes a validation process to confirm fidelity of the reconstructed object with the annotated digital twin.
10. 10 . The method of claim 1 , wherein the digital twin comprises a mesh model or a voxel-based model annotated with material substitution metadata, the substitute material is selected using a machine-learned artificial intelligence model, and either a deterministic rule-based engine based on environmental conditions, or a look-up table indexed by deployment zones and prevalidated material profiles.
11. 11 . An adaptive object fabrication system comprising: an input interface configured to receive a digital twin of a physical object, wherein the digital twin includes a voxel-based model of geometric information; a data input module configured to accept environmental context data representative of physical deployment conditions; a material substitution engine comprising one or more processors executing a machine-learned artificial intelligence (AI) model configured to select substitute materials based on the environmental context data and the digital twin received, the machine-learned AI processor being trained using predictive failure modeling; a twin enhancement module configured to create an enriched digital twin by annotating the digital twin with material substitution metadata and simulated performance predictions, based, in part, on the environmental context data; and a communication module configured to transmit the enriched digital twin to an additive manufacturing system for reconstruction of the physical object using the selected materials; wherein performance data from deployed instances of the plurality of enriched digital twins is collected, and the material substitution engine, including the machine-learned artificial intelligence processor, is retrained for subsequent material substitution decisions, using a federated learning framework that aggregates performance data from multiple deployments of the plurality of enriched digital twins.
12. 12 . The system of claim 11 , wherein the twin enhancement module includes a simulation engine configured to model at least one property selected from the group consisting of heat resistance, vibrational fatigue, and corrosion risk.
13. 13 . The system of claim 11 , wherein the data input module is configured to accept user-defined constraints for substitution material selection.
14. 14 . The system of claim 11 , wherein the material substitution engine accesses a distributed material property database updated in real time.
15. 15 . The system of claim 11 , wherein the annotated metadata includes expected part lifetime under a defined environmental loading profile.
16. 16 . The system of claim 11 , wherein the AI engine is updated based on feedback collected from deployed objects monitored by environmental or structural sensors.
17. 17 . An adaptive object fabrication method comprising: receiving environmental performance data describing anticipated conditions at one or more target deployment locations; creating a plurality of enriched digital twins of a physical object using a machine-learned artificial intelligence (AI) engine trained to incorporate substitute materials based on annotated deployment conditions, including predictive failure modes and environmental durability profiles associated with different deployment conditions, wherein each of the plurality of enriched digital twins comprises a voxel-based model annotated with material substitution metadata and predicted performance characteristics tailored to a corresponding deployment condition; selecting an enriched digital twin from the plurality of enriched digital twins based on the annotated deployment conditions that correspond to the environmental data received; transmitting the enriched digital twin to a fabrication site equipped with additive manufacturing hardware configured to reconstruct the physical object using the substitute materials; collecting performance data from deployed instances of the plurality of enriched digital twins; and retraining the machine-learned artificial intelligence engine, for subsequent material substitution decisions, using a federated learning framework that aggregates performance data from multiple deployments of the plurality of enriched digital twins.
18. 18 . The method of claim 17 , wherein each enriched digital twin of the plurality of enriched digital twins includes voxel annotations specifying fabrication constraints selected from the group consisting of layer thickness, toolpath optimization, and localized reinforcement.
19. 19 . The method of claim 17 , wherein the enriched digital twin includes durability annotations that vary according to environmental zone classification, and wherein the plurality of enriched digital twins are indexed based on performance thresholds and regulatory compliance attributes to facilitate environment-specific fabrication decisions.
20. 20 . The method of claim 17 , wherein transmission of the enriched digital twin is logged in a distributed ledger system for traceability.

Description

TECHNICAL FIELD OF THE INVENTION This invention relates to adaptive digital manufacturing systems, and particularly to methods and systems for generating, enriching, and fabricating physical objects using voxel-based digital twins that are dynamically tailored to environmental deployment conditions. More specifically, the invention concerns the integration of machine-learned artificial intelligence (AI), predictive failure modeling, and voxel-resolved metadata embedding to produce enriched digital twins that guide additive manufacturing processes. These enriched models incorporate material substitution data, environmental stress factors, and fabrication-specific instructions, enabling precise, context-aware replication of objects across distributed manufacturing environments. BACKGROUND OF THE INVENTION Before our invention, existing approaches to digital modeling, material selection, and object fabrication were characterized by significant limitations in adaptability, intelligence, and environmental awareness. While computer-aided design (CAD) and digital twin technologies have long supported the creation of virtual replicas, these models were typically static in nature, focused on geometry and appearance rather than contextual functionality. They lacked the capacity to embed meaningful environmental intelligence, which rendered them ill-suited for deployment in varied and demanding real-world conditions. Previous fabrication workflows often relied on fixed material libraries or manually curated substitution rules that did not account for the specific deployment environment. This resulted in performance mismatches, shortened product lifespans, and increased failure rates in applications such as aerospace, medical devices, and outdoor infrastructure. Materials chosen without reference to environmental stressors-like corrosion from salt fog, fatigue from vibration cycles, or degradation from UV radiation—frequently underperformed, posing risks to safety and reliability. Moreover, artificial intelligence, when applied at all, operated in isolation from the manufacturing process. These AI systems might recommend substitute materials, but they did not produce fabrication-ready models capable of controlling real-world production systems. The absence of voxel-level integration between AI predictions and physical manufacturing constrained the system's ability to make nuanced, performance-oriented adaptations during fabrication. Another persistent challenge was the inability to embed localized manufacturing directives—such as variable print speeds, toolpath optimizations, or reinforcement requirements—directly into the digital representation. This created disconnects between design and production, resulting in inefficient, generic fabrication processes and failure to meet precise operational demands. Security and traceability were also largely missing from prior approaches. Digital models were often shared across unsecured channels, vulnerable to unauthorized access, tampering, or counterfeiting. With no embedded audit trail or cryptographic verification, it was difficult to ensure the authenticity of the design or compliance with regulatory and warranty standards. This left manufacturers and users exposed to legal, safety, and reputational risks. Finally, most systems were static in their decision-making and could not evolve based on operational feedback. Without the ability to incorporate post-deployment performance data, these systems could not refine future predictions, resulting in a stagnant material selection process and an inability to learn from real-world outcomes. While systems such as Siemens NX, Autodesk Generative Design, GE Predix, and HP MultiJet Fusion offer generative modeling or data analytics, they do not incorporate voxel-level material annotations combined with predictive AI modeling and environmental adaptability. These prior systems lack localized reinforcement logic at the fabrication stage, omit closed-loop AI retraining, and do not leverage deployment feedback to refine future designs. The present invention addresses these and other shortcomings by providing a robust, intelligent, and secure system for context-aware, performance-driven digital twin enrichment and adaptive fabrication. For these reasons and shortcomings, as well as other reasons and shortcomings, there is a long-felt need that gives rise to the present invention. SUMMARY OF THE INVENTION The shortcomings of the prior art are overcome, and additional advantages are provided through the provision of an adaptive object fabrication method that utilizes enriched digital twin technology to generate performance-aware, fabrication-ready data structures. In an exemplary embodiment, the method begins by receiving environmental context data representative of real—world deployment conditions—such as temperature, vibration, humidity, or chemical exposure—expected at the physical location where the object will be used. A digital twin of