Additive Manufacturing Control via Peer-to-Peer Network Optimization
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Solution Overview
Problem
Current additive manufacturing systems face high rejection rates due to the complexity of setting control parameter values, leading to suboptimal material properties and inefficient production processes, which require frequent manual adjustments and interruptions.
Innovation Solution
A peer-to-peer application is used to optimize control data sets through an optimizing process, reducing the need for central instances and enhancing security, efficiency, and material properties by allowing nodes in a peer-to-peer network to monitor and execute the controlling means, thereby reducing transaction costs and managing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustment of control parameters is used, then production flexibility is maintained, but productivity decreases and rejection rate increases
Solution Approach 1:
The system performs self-optimization of control parameters through automated feedback loops. The controlling means automatically adjusts control parameter values based on monitored component parameters without requiring manual operator intervention, enabling the system to service itself and eliminate productivity losses from manual adjustments
Solution Approach 2:
The system implements continuous feedback mechanisms where component parameters are monitored during production and fed back to the controlling means. This feedback loop enables automatic recalculation and adjustment of control parameter values to maintain optimal production conditions and reduce rejection rates
2Manufacturing precision
If multiple control parameter values are set manually, then production flexibility is maintained, but manufacturing precision decreases
Solution Approach 1:
The system replaces manual mechanical adjustment of control parameters with automated computational processes. The controlling means uses algorithms to calculate optimal control parameter values based on design plans and monitored component parameters, substituting human operator judgment with systematic computational optimization to improve manufacturing precision
Solution Approach 2:
The system dynamically changes control parameter values based on real-time component parameters and production conditions. The controlling means automatically recalculates and adjusts multiple control parameters simultaneously to maintain optimal manufacturing precision throughout the production process
3Reliability
If central platform is used for process management, then control capability is maintained, but transaction costs increase and security decreases
Solution Approach 1:
The system segments the process management functionality into distributed controlling means that operate autonomously at different production locations. Each controlling means manages its own optimization processes independently, eliminating the need for a centralized platform and reducing transaction costs while maintaining control capability through decentralized architecture
Solution Approach 2:
The system uses standardized communication protocols and data formats as intermediaries between distributed controlling means. This intermediary layer enables seamless coordination and information exchange between decentralized units without requiring a central platform, maintaining system coherence while enhancing security and reducing complexity
Data Source
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AI summary
The invention relates to a method for operating at least one additive manufacturing apparatus (102, 202, 402.1, 402.2, 602.1, 602.2), comprising producing at least one first component (222, 322, 422) by the additive manufacturing apparatus (102, 202, 402.1, 402.2, 602.1, 602.2) in accordance with a first control data set during a first production step, providing at least one component parameter data set related to the first component (222, 322, 422) to at least one peer-to-peer application (110, 210, 410, 510) of at least one peer-to-peer network (104, 204,404, 604), controlling at least one optimizing process based on the provided component parameter data set and the first control data set by means of at least one controlling means (116, 216,416, 516) of the peer-to-peer application (110,210,410, 510) executed by at least a part of the nodes (112.1, 112.2, 112.3, 212.1, 212.2, 412.1,412.2,412.3,601.1, 612.1, 626.1) of the peer-to-peer network (104, 204,404, 604) such that the first control data set is adapted, and providing the adapted first control data set from the peer-to-peer application (110, 210, 410, 510) to the additive manufacturing apparatus (102, 202, 402.1, 402.2, 602.1, 602.2).