Researchers from the University of Liverpool outline their findings regarding the automatic detection of faults in additive manufacturing products in a recently published paper, ‘Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning.’ Their work continues an ongoing trend in perfecting 3D printing and additive manufacturing techniques to enhance numerous industries currently delving into the technology.
The scientists have created a machine learning algorithm, using a semi-supervised approach, to detect AM product flaws. The algorithm draws data from parts that are already certified as well as those with unknown quality. While this not only furthers the quality of 3D printing, the approach is much more efficient and affordable—in relation to laser powder-bed fusion printing.
The researchers compiled data using ‘high precision photodiodes,’ a type of very sensitive sensor able to process measurements to assess quality.
“Understanding the correlations between this data and build quality is a challenging area,” state the researchers. “However, advances in machine learning have made it possible to create and apply intelligent algorithms to large datasets for decision making.”
“Such algorithms can identify patterns in large data, after being trained. The current work is based on the hypothesis that, using large amounts of process measurements from L-PBF machines, machine learning can be used to quickly and cheaply classify the success of L-PBF builds.”
The semi-supervised approach is exactly what it sounds like—a mode in between completely supervised with labeled data and defined sets and unsupervised learning where patterns much be discovered with unlabeled data.
“With a semi-supervised approach, the user provides some labeled data and some unlabeled data at the same time,” state the researchers. “The model may then attempt to establish a decision boundary and classifies the data into clusters; based on the characteristics of the provided labeled and unlabeled information.”
The researchers point out that a semi-supervised approach works well in a scenario rich in unlabeled data, with only a few labeled data—saving time and money in the number of experiments performed.
This is a data-based project, based only on patterns from the photodiode measurements, helping the researchers identify the causes of 3D printing defects. Often, they are the result of poor settings, inferior supports, issues with powder, or temperature or material problems. The four following parameters have the most effect on part quality:
- Part bed temperature
- Laser power
- Scan speed
- Scan spacing
For this study, two L-PBF builds were used, and the researchers built 50 tensile test bars, with 25 yielded in each build. Data was gleaned from each build, as ‘the x and y position of the laser was collected alongside time history measurements from 2 photodiodes sensors (sample frequency equal to 100 kHz, resulting in approximately 400 GB of data per build).’
Tensile tests were performed by the research time, and each bar was judged as either acceptable or faulty, with a 77 percent test rate.
“The results show that semi-supervised learning is a promising approach for the automatic certification of AM builds that can be implemented at a fraction of the cost currently required,” concluded the researchers.
“Future work aims to investigate whether classification can be improved through the use of additional, complimentary sensing systems (acoustic sensors and thermal imaging cameras, for example).”
With the inception of 3D printing came the continual marveling of all we can create—but also continual suspicion regarding whether the parts can hold up for functional use, some of it which is meant to be highly industrial. Testing of parts has become an extensive field on its own, whether in improving CT scanning procedures, testing 3D printed motors for the military, or even using robotics for such purposes. Find out more about testing of parts in relation to laser powder-bed fusion here. What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at 3DPrintBoard.com.[Source / Images: Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning]
You May Also Like
3D Printing People: A Dialogue Beyond Industry at TIPE 2022
Women in 3D Printing (Wi3DP) has pulled off another virtual event show coup. After an immensely successful inaugural event in 2021, the non-profit has hosted an even bigger 2022 event. And...
3D Printing Webinar and Event Roundup: January 16, 2022
We’re back in business this week with plenty of webinars and events, both virtual and in-person, starting with the second edition of the all-female-speaker TIPE 3D Printing conference. There are...
Women in 3D Printing’s Posts Agenda for TIPE Conference and Virtual Career Fair
This January 18-20, Women in 3D Printing (Wi3DP) is back for the second time in a row with its TIPE 3D Printing Conference and Virtual Career Fair. Like its inaugural...
Ford and Czinger to Give Automotive 3D Printing Keynotes at AMUG 2022
As the 2022 AMUG Conference approaches, the Additive Manufacturing Users Group (AMUG) has announced its keynote speakers. Headlining the event, set to take place in Chicago, Illinois from April 3-7, are Kevin...
View our broad assortment of in house and third party products.