Analytical modelling of in situ layer-wise defect detection in 3D-printed parts: additive manufacturing (original) (raw)
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International journal of online and biomedical engineering, 2023
3D printers are known for providing parts with relatively good accuracy. However, the level of accuracy in the dimensions of printed objects may not matter if they do not have a mechanical purpose. When multiple 3D-printed parts are intended to be integrated with each other to create a larger system, even a fraction of a millimeter can have a significant impact on the entire system. This study aims to investigate the variation in dimension when a single print file is replicated using the same slicing settings. The findings are then analyzed using quality control tools and compared to the designed measurements. Fused deposition modeling (FDM) technology or fused filament fabrication (FFF) technology was chosen for this study due to its availability to the common user, its relatively low cost, and its increasing popularity in different applications and industries. The material used in this study is polylactic acid (PLA) which is a thermoplastic and the most widely used plastic filament in 3D printing. It has a low melting point, high strength, low thermal expansion, and is relatively cheap. The dimensional accuracy of FDM-produced parts was evaluated by comparing the dimensions of the fabricated specimens with their computer-aided design (CAD) models. Statistical analysis revealed that the mean dimensional deviations were within the specified tolerance limits for most of the tested parts. This suggests that FDM technology is reliable in terms of achieving dimensional accuracy. KEYWORDS 3D printed, FDM technology, FFF technology, accuracy of 3D printed parts, polylactic acid (PLA) 1 INTRODUCTION Additive manufacturing (AM), commonly known as 3D printing has revolutionized the manufacturing industry and had a huge impact on how various industries operate. Additive manufacturing has provided users with the freedom to design complex parts that are not easily manufactured using traditional manufacturing methods. This
Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity, internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.
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Additive manufacturing has experienced a surge in popularity in both commercial and private sectors over the past decade due to the growing demand for affordable and highly customized products, which are often in direct opposition to the requirements of traditional subtractive manufacturing. Fused Filament Fabrication (FFF) has emerged as the most widely-used additive manufacturing technology, despite challenges associated with achieving contour accuracy. To address this issue, the authors have developed a novel camera-based process monitoring method that enables the detection of errors in the printing process through a layer-by-layer comparison of the actual contour and the target contour obtained via G-Code processing. This method is generalizable and can be applied to different printer models with minimal hardware adjustments using off-the-shelf components. The authors have demonstrated the effectiveness of this method in automatically detecting both coarse and small contour devi...
2017
Today's Additive Manufacturing (AM) is mostly layer-based. Despite AM's great capabilities in fabrication of complex geometries, product's surface roughness is a limiting factor in many industrial applications. Therefore, application of AM parts in industrial services highly relies on appropriate modeling, inspection, and post-processing of the fabricated surfaces. A thorough investigation of surface roughness to improve surface quality of AM products is the focus of this thesis by developing methodologies to complete the three tasks of modelling, inspection, and post-processing of AM surfaces. A theoretical formulation to model surface roughness of layer based manufactured parts is developed by defining centerline using a Total Least Square (TLS) approach and the model is validated experimentally. The developed model is also used for surface topography of AM parts as a new metrology approach. Optical scanning data point cloud of Fused Deposition Modeling (FDM) parts are used to conduct inspection based on the developed methodology. 3D topography of the surfaces are reconstructed when a good agreement with the corresponding 2D profilometer inspection is observed. Acetone vapour bath smoothing is used for post-processing of FDM parts. The number of smoothing cycles, and the duration of each cycle are considered as the main smoothing parameters. Effect of geometric complexity and smoothing parameters are studied and the best smoothing settings are proposed for a desired level of smoothing requirement. The developed experimental models allow engineers to plan the smoothing process based on the build orientation and geometric complexity of the product.
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