During the past four years, the Multi-Scale Additive Manufacturing (MSAM) Laboratory, the largest R&D center in the field of additive manufacturing (AM) at the University of Waterloo in Canada, has collaborated with EOS to develop quality assurance algorithms and a closed-loop control system for detecting and healing flaws from lack of fusion induced during laser powder bed fusion (LPBF).
In-situ process monitoring is the key for validating the quality of AM-made parts and minimizing the need for post quality control. In this collaborative research, in-situ datasets collected from a co-axial photodiode installed in an EOS M 290 were subject to a set of correction factors to remove chromatic and monochromatic distortions from the signal. The corrected datasets were then analyzed using statistical and machine learning algorithms. These algorithms were systematically tuned and customized to detect lack of fusion flaws.
The flaw detection workflow by statistical and machine learning algorithms was similar to what follows:
- The algorithms were applied to the dataset collected during the printing of samples with intentional seeded flaws.
- The detection results were compared with the design and CT-scan data to customize the algorithm parameters in terms of moving average lengths and detection thresholds.
- The customized algorithms stemmed from step 2 were used for the detection of randomized flaws in the regular printed samples.
- The detection results (step 3) were validated by CT-scan through the volumetric approach and confusion matrix.
Figure 1 represents the schematic of the workflow.
Figure 2 demonstrates one example where a part with intentional seeded flaws was subject to the statistical and machine learning algorithms. The figure depicts the planar view of the CT-Scan of parts with seeded cylindrical voids (Ø, H = 200 µm).
The results indicate that flaws larger than 120 μm are detected by the statistical algorithm, while flaws of 100 μm in size are detectable by the machine learning algorithm.
The performance of the algorithms during printing of actual parts was assessed where parts with randomized flaws due to deviation of process parameters were built. By comparing the results of the algorithms with the associated CT- Scan when a volumetric segmentation approach and confusion matrix is used, the results show that the flaws induced were predicted with the true positive (TP) rate of >75% by machine learning algorithm and <30% by the statistical algorithm. However, the machine learning algorithm exhibited TP rate results similar to those of statistical algorithm in identifying flaws created by high hatching distances and high speeds, whereas the machine learning algorithm improved TN rates up to 31% and 20% for such samples, respectively. Additionally, the machine learning algorithm demonstrated an improvement in computational speed when it computed the defects per layer 86% faster than the statistical algorithm.
As a result of the comparison between these algorithms, the machine learning algorithm has been found to be more appropriate for implementing an intermittent closed-loop controller. The controller is now developed and validated with promising outcomes for healing the lack of fusion flaws created during the process. More information about the controller will be disclosed later.
For more information, please refer to
- Taherkhani, K., Sheydaeian, E., Eischer, C., Otto, M. & Toyserkani, E. Development of a defect-detection platform using photodiode signals collected from the melt pool of laser powder-bed fusion. Manuf. 46, 102152 (2021)
- Taherkhani, K., Eischer, C. & Toyserkani, E. An unsupervised machine learning algorithm for in-situ defect-detection in laser powder-bed fusion. Process. 81, 476–489 (2022)
Or contact Dr. Katayoon Taherkhani by emailing email@example.com
Feature image courtesy of EOS.
Subscribe to Our Email Newsletter
Stay up-to-date on all the latest news from the 3D printing industry and receive information and offers from third party vendors.
You May Also Like
3D Printing News Unpeeled: BLT, M Holland & Tecnológico de Monterrey
BLT has announced its half year results for 2023 with $2.44 million in profit for the first half year up from a $5.34 million loss last year for the same period....
AML3D Makes C-Suite Changes & Ramps Up its Metal 3D Printing Sales in Support of US Navy
The board of AML3D, the Australian original equipment manufacturer (OEM) of metal additive manufacturing (AM) platforms, recently concluded a four month review of the company’s leadership structure, which has resulted...
3D Printing Webinar and Event Roundup: September 17, 2023
It’s another busy week filled with 3D printing webinars and events! Topics include photopolymers and industrial automation, aerospace and 3D scanning, DIGITAL FOAM and composite 3D printers, biomaterial bioinks, and...
3D Printing Webinar and Event Roundup: September 10, 2023
This might possibly be the longest webinar and event roundup we’ve ever done at 3DPrint.com—that’s how many offerings there are this week! I won’t waste your time in this introduction...
Upload your 3D Models and get them printed quickly and efficiently.