3D Printer Material Tuning With Hidden Build Parameter Mapping
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Solution Overview
Problem
Existing 3D printers require complex and time-consuming processes for tuning build parameters when changing materials, often necessitating software modifications and expert intervention, which are not accessible to end users.
Innovation Solution
A 3D printer system that simplifies the tuning process by allowing users to input a material category and a subset of build parameters, using internal intelligence to determine all other necessary parameters, while keeping proprietary settings inaccessible to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex tuning processes are used to achieve accurate build parameters for new materials, then manufacturing precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The 3D printer system automatically determines private build parameters by itself using internal intelligence (processor executing instructions with empirically derived data) when new materials are introduced, without requiring external expert intervention or complex user tuning processes. The system serves itself by autonomously optimizing build parameters based on material category and public parameters provided by the user.
Solution Approach 2:
The patent introduces an intermediary layer of empirically derived data, relationships, and equations stored in the data storage device that mediates between the user-provided public build parameters and the required private build parameters. This intermediary knowledge base enables automatic determination of accurate build parameters without direct user involvement in the complex tuning process.
2Manufacturing precision
If expert intervention and software modifications are required for material tuning, then manufacturing precision is improved, but ease of operation and accessibility to end users deteriorate
Solution Approach 1:
The system enables end users to independently tune build parameters for new materials without requiring expert intervention. Users simply provide material category and public build parameters, and the system automatically determines all necessary private build parameters, making the process accessible to non-experts while maintaining high precision.
Solution Approach 2:
The patent extracts the complex expert knowledge and tuning procedures from the user side and embeds them within the system as empirically derived data and algorithms. This separation allows users to operate the system without needing specialized knowledge, while the system internally performs the expert-level parameter determination.
3Ease of operation
If all build parameters are made accessible and adjustable by users, then ease of operation is improved, but loss of proprietary information and manufacturer trade secrets increases
Solution Approach 1:
The patent segments build parameters into two distinct categories: public build parameters that users can access and adjust, and private build parameters that remain inaccessible to users. This segmentation allows users to participate in the tuning process with limited parameters while protecting proprietary information, resolving the contradiction between user accessibility and trade secret protection.
Solution Approach 2:
The system uses an intermediary computational process that takes public build parameters as input and automatically determines private build parameters without exposing them to users. This intermediary layer enables user participation in the tuning process while acting as a protective barrier that prevents access to proprietary parameters, maintaining both ease of operation and information security.
Data Source
AI summary
A 3D printer includes a holding area holding material to be used to produce a part and at least one component for producing the part through layer-wise additive manufacturing. A data storage device in the 3D printer stores instructions for generating build parameter values, the instructions including empirically derived data, relationships, and/or equations. A processor in the 3D printer receives values for a public build parameter set and a category for the material wherein the category represents multiple different materials. The processor executes instructions to determine values for a private build parameter set that vary based on the properties of the material. The values for the private build parameter set are determined from the received values for the public build parameter set and the received category. The processor then uses the received values for the public build parameter set and the values for the private build parameter set to build the part.