Train 3DOS™ AI

3DOS™ AI Machine Learning

The 3DOS™ decentralized manufacturing network utilizes advanced artificial intelligence (AI) capabilities to enhance the 3D printing process, analyze video camera feeds, and optimize printing outcomes.

Users are rewarded with 3DOS™ tokens for actively contributing to the system's training, fostering a collaborative environment. Conversely, individuals attempting to exploit the system for fraudulent gain, without genuinely engaging in 3D printing on the network, risk forfeiting their tokens as a measure against misuse.

Here's a detailed explanation of how the system works:

1. Video Camera Feed Analysis:

a. Monitoring 3D Printers:

• The network integrates cameras with 3D printers across its decentralized network.

• Cameras capture live video feeds of ongoing 3D printing processes.

3DOS Live Video

b. Real-time Image Processing:

• AI algorithms process these video feeds in real-time to monitor the printing progress.

• Object recognition techniques identify various aspects of the printing process, such as layer adhesion, print speed, and filament deposition.

c. Failure Detection:

• AI is trained to recognize common print failures, including layer shifting, warping, under-extrusion, or over-extrusion.

• When a failure is detected, the AI system immediately alerts the user and logs the details for analysis.

d. Success Confirmation:

• Successful prints are also acknowledged by the AI, providing positive feedback to the user.

2. Print Settings and Failure Rate Analysis:

a. Data Collection:

• The AI collects data on print settings, including layer height, print speed, temperature, and material type, from successful and failed prints.

b. Failure Rate Analysis:

• Historical data on failure rates is used to identify patterns and correlations between specific print settings and failure types.

• The AI learns from this data to suggest optimal print settings and minimize the risk of failures.

3. Originality Confirmation:

a. Unique Print Signatures:

• The AI is trained to recognize unique patterns and characteristics in 3D prints, forming a kind of "print signature" for each design.

b. Authentication:

• When a 3D print job is submitted, the AI checks its signature against a database of authorized designs to confirm its originality.

• Unauthorized or potentially compromised prints trigger alerts and preventive measures.

4. Machine Telemetries and Slicing Profiles:

a. Continuous Learning:

• The AI continuously learns from the telemetries of all connected 3D printers, gaining insights into machine performance, wear and tear, and maintenance needs.

b. Slicing Profile Optimization:

• By analyzing slicing profiles used for different prints, the AI suggests improvements to optimize print quality, speed, and material usage.

3DOS™ AI learns through the communities slicing settings, material settings, and successful prints

5. Print Job Routing:

a. Quality Metrics:

• The AI considers historical data, machine capabilities, and current workloads to assess the quality of each 3D printer in the network.

b. Dynamic Routing:

• Print jobs are dynamically routed to the printers that have a track record of producing high-quality prints with minimal failures.

3DOS™ Routes, Tracks, and Proves Provenance of Orders

6. User Guidance and Improvement:

a. User Feedback:

• The AI provides real-time feedback to users, guiding them on how to enhance print quality based on analysis of ongoing and past prints.

b. Adaptive Learning:

• The AI continually adapts and refines its recommendations based on the evolving dataset of prints and user interactions.

In summary, the 3DOS decentralized manufacturing network uses AI to create a feedback loop, continuously improving 3D printing outcomes by analyzing video feeds, optimizing settings, confirming originality, and guiding users toward better printing practices. The decentralized nature of the network allows for a wide range of 3D printing capabilities to be harnessed and shared within the community.