Additive Manufacturing Variability Analytics and Adjustment
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Solution Overview
Problem
Current additive manufacturing systems face challenges in diagnosing aborted or failed builds and identifying performance issues, requiring significant time and human labor. Additionally, finding the root cause of failures is difficult and time-consuming, especially during a build, leading to unreliability and inefficiency in additive manufacturing machines and fleets.
Innovation Solution
The system employs a monitoring and analysis framework that collects data from multiple sources, including sensors and metadata, to analyze build-to-build variability and machine health. This framework includes a build-to-build variability analyzer circuit that computes statistical variability and identifies sources of variation, enabling corrective actions to improve repeatability and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual diagnosis is used to diagnose build failures and identify performance issues, then diagnostic accuracy can be maintained through expert knowledge, but significant time and human labor are required
Solution Approach 1:
The patent replaces manual mechanical diagnosis with an automated analytics system that uses sensors, processors, and algorithms to detect build failures and performance issues. The system automatically collects data from multiple sources, processes it through analytics engines, and generates diagnostic reports without human intervention, thereby reducing diagnosis time while maintaining accuracy through systematic analysis.
Solution Approach 2:
The analytics system enables self-diagnosis of build failures and machine performance issues. The system automatically monitors its own operation, detects anomalies, identifies root causes, and generates diagnostic information without requiring external expert intervention. This self-service capability significantly reduces the time and labor needed for diagnosis while maintaining high diagnostic accuracy through automated pattern recognition.
2Measurement precision
If manual analysis is used to find root cause of failures, then thorough investigation can be conducted, but the process is difficult and time-consuming
Solution Approach 1:
The patent segments the root cause analysis process into distinct analytical modules that examine different aspects of build failures independently. The system divides diagnostic data into multiple categories (sensor data, process parameters, machine state) and analyzes each segment separately before synthesizing comprehensive root cause identification. This segmentation enables thorough investigation while improving efficiency through specialized analysis of each data type.
Solution Approach 2:
The system replaces manual root cause investigation with automated analytics processing. Multiple sensors and data sources continuously monitor machine operation and build processes, automatically detecting anomalies and tracing them to root causes through algorithmic analysis. This substitution eliminates the time-consuming manual investigation process while maintaining thoroughness through comprehensive data collection and systematic analysis.
3Reliability
If individual machine monitoring is implemented, then build reliability can be improved, but fleet-level repeatability remains challenging
Solution Approach 1:
The patent implements a universal analytics platform that serves both individual machine monitoring and fleet-level analysis functions. The system collects and processes data from multiple machines using the same analytical algorithms and data structures, enabling consistent monitoring across the entire fleet. This multi-functionality allows the system to improve individual build reliability while simultaneously achieving fleet-level repeatability through standardized analysis approaches.
Solution Approach 2:
The system merges individual machine data with fleet-level data into a unified analytics framework. By combining data from multiple machines and analyzing them together using the same processing logic, the system identifies both individual build issues and fleet-wide patterns. This merging enables simultaneous improvement of individual reliability and fleet repeatability through comprehensive data integration and unified analysis.
Data Source
AI summary
Systems, apparatus, computer-readable medium, and associated methods to monitor, analyze, and adjust at least one of a build and/or an additive manufacturing machine configuration are disclosed. An example apparatus includes memory circuitry, instructions, and processor circuitry to execute the instructions to implement at least a feature extractor, a variability analyzer, and an output generator. The feature extractor is to: i) group data for a plurality of builds associated with one or more additive manufacturing machines; and ii) extract features from the grouped data. The variability analyzer is to: i) process the grouped data with respect to the features to determine a measure of variability for each feature; and ii) compare the measure of variability for each feature to a respective allowable limit associated with the respective feature. The output generator is to provide actionable output to adjust at least a first additive manufacturing machine.