Method for additive manufacturing machine and process qualification and verifiction
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Solution Overview
Problem
Beam based additive manufacturing machines face defects and errors in energy delivery systems, optics, gas flow, and build plate setup, leading to product defects.
Innovation Solution
A method and system for analyzing additive manufacturing machines using Full Layer Exposure (FLE) to capture data, identify defects in objects, and adjust process parameters or issue alerts for hardware or process defects, utilizing machine learning for defect identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional additive manufacturing processes are used without Full Layer Exposure testing, then resource requirements and time for qualification are reduced, but manufacturing precision and defect detection capability deteriorate
Solution Approach 1:
The system performs Full Layer Exposure testing as a preliminary qualification step before normal production. This preliminary action creates a comprehensive defect detection baseline that enables faster subsequent manufacturing while maintaining high precision through pre-identified system weaknesses and process parameters
2Manufacturing precision
If comprehensive defect analysis with Full Layer Exposure is performed, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The Full Layer Exposure test serves multiple functions simultaneously: it qualifies the additive manufacturing system, identifies hardware defects, detects process defects, and establishes baseline parameters for normal production. This multi-functionality achieves comprehensive defect identification without requiring separate specialized systems for each function
Solution Approach 2:
The system creates a digital copy or model of expected defect patterns through Full Layer Exposure testing. This digital baseline is then used for comparison during normal production, enabling sophisticated defect detection without requiring equally sophisticated physical inspection equipment for every part
3Reliability
If Full Layer Exposure testing is implemented for system qualification, then reliability improves, but productivity decreases
Solution Approach 1:
Full Layer Exposure testing is performed as a one-time preliminary qualification action before normal production begins. Once the system is qualified and baseline parameters are established, subsequent production can proceed at normal speed while maintaining reliability through the pre-validated system configuration and identified optimal parameters
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the qualification and verification of additive manufacturing processes by reducing resource requirements and identifying weak spots, improving product quality and process efficiency.
Implementation Method 1
The energy delivery system configured to use a beam on the feedstock material to melt the feedstock material and form the one or more objects
Data Source
AI summary
Systems and methods for analyzing an additive manufacturing machine are disclosed. The methods include generating an object with Full Layer Exposure (FLE) on a build plate of an additive manufacturing machine. The methods also include capturing data for the object with FLE. The methods further include identifying defects in the object with FLE utilizing the data. The methods further include identifying defects in the additive manufacturing machine utilizing the defects in the object with FLE identified. The methods yet further include, in response to identifying a process defect, performing at least one action chosen from among causing at least one parameter of an attribute associated with the additive manufacturing process to be adjusted and issuing at least one alert defining the process defect, and in response to identifying a hardware defect of the additive manufacturing machine, issuing at least one alert defining the hardware defect.