Additive Manufacturing Feedback Loop for In-Process Quality Control
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Solution Overview
Problem
Additive manufacturing (AM) processes face challenges due to shifts in machine parameters, leading to uncertain part quality and high scrap rates, as conventional post-build testing is expensive, time-consuming, and inefficient, lacking real-time monitoring and adjustment capabilities to maintain desired part performance.
Innovation Solution
Implementing a system that monitors AM process parameters in real-time, using material property prediction models within a feedback control loop to adjust input parameters during the build process, ensuring part quality meets predetermined specifications by correcting deviations in laser power, scan speed, and melt-pool characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If post-build physical and mechanical testing is used to evaluate part quality, then part quality can be assessed, but the process becomes very expensive, time consuming and inefficient
Solution Approach 1:
The system performs real-time monitoring and prediction of part properties during the additive manufacturing build process, allowing quality assessment to be conducted before the build is complete. This preliminary action eliminates the need for time-consuming post-build testing while maintaining measurement precision through continuous process parameter monitoring and predictive modeling.
2Measurement precision
If post-build testing is used to evaluate part quality, then quality assessment is possible, but parts can be scrapped for small defects resulting in low yield rates
Solution Approach 1:
The system implements a feedback control loop where real-time monitoring of process parameters feeds into predictive models that forecast part properties. When deviations are detected, the system provides feedback to adjust process parameters mid-build, preventing defect formation and eliminating the need to scrap parts, thereby maintaining high production yield rates while ensuring quality assessment accuracy.
3Productivity
If real-time monitoring and adjustment of AM process parameters is implemented, then part quality and production yield can be improved, but system complexity increases
Solution Approach 1:
The system integrates multiple functions into a unified platform: real-time monitoring of process parameters, predictive modeling of part properties, automated analysis of deviations, and dynamic adjustment of build parameters. This multi-functional integration achieves improved productivity and yield while managing system complexity through consolidation rather than separate independent systems.
4Measurement precision
If conventional post-build testing is used, then quality assessment is possible, but expensive and labor intensive inspection processes are required
Solution Approach 1:
The system enables the additive manufacturing process to self-monitor and self-assess its own output quality through real-time parameter monitoring and predictive modeling. This self-service capability eliminates the need for external expensive and labor-intensive inspection processes, maintaining measurement precision while significantly reducing the energy and resource costs associated with quality assessment.
Data Source
AI summary
A method of additive manufacturing machine (AMM) build process control includes obtaining AMM machine and process parameter settings, accessing sensor data for monitored physical conditions in the AMM, calculating a difference between expected AMM physical conditions and elements of the monitored conditions, providing the machine and process parameter settings, monitored conditions, and differences to one or more material property prediction models, computing a predicted value or range for the monitored conditions, comparing the predicted value or range to a predetermined target range, based on a determination that predicted value(s) are within the predetermined range, maintaining the machine and process parameter settings, or based on a determination that one or more of the predicted value(s) is outside the predetermined range, generating commands to compensate the machine and process parameter settings, and repeating the closed feedback loop at intervals of time during the build process. A system and a non-transitory medium are also disclosed.