Additive Manufacturing Failure Diagnosis Using Subsystem Thresholds
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Solution Overview
Problem
Current additive manufacturing systems face challenges in diagnosing failed builds and identifying performance issues, requiring extensive human labor and time to manually identify root causes of failures.
Innovation Solution
A system and method that utilize machine learning or statistical models to determine parameters associated with additive manufacturing device subsystems, compare these parameters with threshold values, and diagnose failure modes, providing a visually interactive interface for rapid diagnosis of build or module-level issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual diagnosis is used to identify root causes of additive manufacturing failures, then diagnostic accuracy can be achieved, but time consumption and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical diagnosis with an automated diagnostic system that uses sensors, processors, and algorithms to automatically identify failure modes and root causes in additive manufacturing processes, eliminating the need for manual inspection while maintaining diagnostic accuracy
Solution Approach 2:
The diagnostic system enables the additive manufacturing device to self-diagnose failures by automatically collecting sensor data, analyzing process parameters, and identifying root causes without external human intervention, thus reducing both time consumption and labor requirements
2Reliability
If manual expert diagnosis is employed to identify performance issues, then accurate root cause identification is possible, but labor requirements and time investment increase
Solution Approach 1:
The system replaces expert manual diagnosis with automated diagnostic algorithms that process sensor data and process parameters to reliably identify root causes, eliminating the need for expert human labor while maintaining diagnostic reliability
Solution Approach 2:
The patent introduces an intermediary diagnostic system that acts as a bridge between raw sensor data and root cause identification, using processors and algorithms to translate complex manufacturing data into actionable diagnostic information without requiring manual expert analysis
3Productivity
If automated diagnostic systems are implemented, then time and labor efficiency improve, but system complexity increases
Solution Approach 1:
The diagnostic system is designed to be universal and multi-functional, capable of diagnosing multiple types of failures across different additive manufacturing processes using the same platform, which reduces overall system complexity while maintaining high diagnostic productivity
Solution Approach 2:
The patent uses intermediary components such as sensors and processors that simplify the complexity of automated diagnosis by breaking down the diagnostic process into manageable stages: data collection, data processing, and failure mode identification, making the system easier to implement and maintain
Data Source
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AI summary
A system for diagnosing an additive manufacturing device (100) is provided. The system includes one or more processors (302), one or more non-transitory memory modules (304) communicatively coupled to the one or more processors (302) and storing machine-readable instructions. The machine-readable instructions, when executed, cause the one or more processors (302) to: determine parameters associated with at least one subsystem of the additive manufacturing device (100), the parameters being related to a build generated by the additive manufacturing device (100); compare the parameters with threshold values (730, 750); and determine a failure mode (822), among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device (100) based on the comparison of the parameters with the threshold values (730, 750).