In situ real time defect detection of 3D printed parts

In situ Enabling Approaches for Tissue Regeneration: Current Challenges and New Developments

“…Since the first approach in 2007, several research groups have explored in situ bioprinting using two different strategies: one based on accurate technique development to detect and print in situ (Cohen et al, 2010;Holzmond and Li, 2017;Li et al, 2017), and the other consists on in situ bioprinting in animal models to treat damaged tissues such as cartilage, bone, and skin (O'Connell et al, 2016;Duchi et al, 2017;Keriquel et al, 2017;Bella et al, 2018).…”

“…Ideally, an in situ printing technique should scan the defect, identify the damaged area, and print new tissue accordingly. A study developed by Holzmond and Li (2017) presented a "certify-as-you-build" quality assurance system with the ability to monitor the printing process, detect the geometry using 3D digital image correlation (3D-DIC), and compare the printed geometry with the computer model to identify print errors in situ. A case study was developed using a fused filament fabrication (FFF) 3D printer and demonstrating the in situ error detection of local and global defects (Holzmond and Li, 2017).…”

“…The tissue regeneration can be followed by assessing functional markers in real time, for example, by integrating biofabrication technologies with adequate sensors. As proposed by Holzmond and Li (2017), the concept of "certify-as-you-build" as a quality assurance system with the capability to monitor the fabrication process, should be extended to the post-processing stage to follow the events of tissue healing and regeneration and to ensure the expected therapeutic result.…”

“…Therefore, in situ approaches need stateof-the-art customized processing technologies. The advances in micro-and nano-fabrication technologies can provide the platforms to engineer structures at high resolution with the capacity to fully recreate in situ the complexity of a tissue defect (Holzmond and Li, 2017;Bella et al, 2018;Hakimi et al, 2018;Hudita et al, 2019).…”

Invited review article: Metal-additive manufacturing—Modeling strategies for application-optimized designs

“…While this typically results in information that can help drive the parameter choice process, it may not be applicable for parts with large and/or complex geometrical features. Other less common in situ process optimization methods such as on-line process monitoring and in-line quality control have also been investigated, but are challenging to implement reliably into existing machine technologies [12–14]. The use of volumetric heat-source energy density to optimize printing parameters has also been investigated, but has been shown to lack necessary information about the thermal phenomena that occurs during the process, and can be too generalized to apply to many different material systems [15].…”