Defect Detection in Selective Laser Melting

<p>Additively manufactured parts produced using selective laser melting (SLM) are prone to defects created during the build process due to part shrinkage while cooling. Currently defects are found only after the part is removed from the printer. To determine whether cracks can be detected before a print is completed, this project developed print parameters to print a test coupon with inherent defects – warpage and cracking. Data recorded during the build was then characterized to determine when the defects occurred.</p> <p>The test coupon was printed using two sets of print parameters developed to control the severity of warpage and cracking. The builds were monitored using an accelerometer recording at 12500 samples per second, an iphone recording audio at 48000 samples a second, and a camera taking a photo every build layer. Data was analyzed using image comparison, signal amplitude, Fourier Transform, and Wavelet Decomposition.</p> <p>The developed print parameters reduced warpage in the part by better distributing heat throughout the build envelope. Reducing warpage enabled the lower portion of the part to be printed intact, preserving it to experience cracking later in the build. From physical evidence on the part as well as time stamps from the machine script, several high energy impulse events in the accelerometer data were determined to be when cracking occurred in the build. This project’s preliminary investigation of accelerometers to detect defects in selective laser melting will be used in future work to create machine learning algorithms that would control the machine in real time and address defects as they arise.</p>

Name thesis:degree_name

MS in Mechanical Engineering

Discipline thesis:degree_discipline

Mechanical Engineering

Year dc:date.available

2018

Author dc:creator

• Foster, Moira

Contributors dc:contributor

• Peter Schuster

• Additive Manufacturing
• Selective Laser Melting
• Accelerometer
• Cracking
• Warping
• Defect
• Manufacturing

Identifier

10.15368/theses.2018.72

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3219