Detecting first layer bond quality during FDM 3D printing using a discrete wavelet energy approach
Sharma, Bhisham N.
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Bhavsar, Pavan; Sharma, Bhisham N.; Moscoso-Kingsley, Wilfredo; Madhavan, Viswanathan. 2020. Detecting first layer bond quality during FDM 3D printing using a discrete wavelet energy approach. Procedia Manufacturing, vol. 48:pp 718-724
Fused Deposition Modeling (FDM) is a highly versatile additive manufacturing method for 3D printing thermoplastic-based components at small as well as larger production scales. By combining the filament with fibers from other materials including wood, metal, glass, and carbon, the method can easily be adapted to print complex parts using a variety of materials. However, despite its popularity, online print quality and machine monitoring continue to remain a challenge. Here, we present the preliminary results from our efforts on using cheap off-the-shelf sensors in combination with discrete wavelet transform analysis to identify the differences in the vibroacoustic signals measured near the print area during successful and failed first layer filament deposition on the build plate. A failure in creating a strong first layer bond between the extruded filament and the build plate always results in a print failure and is one of most common print issues occurring in FDM printing. By controlling the extruder and build plate temperatures, we control the filament - build plate bond strength while measuring the generated vibroacoustic signals using a PVDF piezo sensor. The measured signals are analyzed using a discrete wavelet transform to partition the signal energy into different energy levels. For the cases studied, we find that the relevant noticeable differences can be observed in specific energy levels during good and bad bond formation. Reconstructing the signal using these energy contributions provides a time domain representation of signal differences under different conditions. The obtained results demonstrate that a cheap and easy to implement method can be developed using PVDF sensors in combination with a wavelet-based signal analysis approach.
© 2020 Authors. Open access. This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.