Recent advances in material development and process monitoring in polymer additive manufacturing

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Arit Das ◽  
Michael J. Bortner
Author(s):  
Deepankar Pal ◽  
Nachiket Patil ◽  
Kai Zeng ◽  
Brent Stucker

The complexity of local and dynamic thermal transformations in additive manufacturing (AM) processes makes it difficult to track in situ thermomechanical changes at different length scales within a part using experimental process monitoring equipment. In addition, in situ process monitoring is limited to providing information only at the exposed surface of a layer being built. As a result, an understanding of the bulk microstructural transformations and the resulting behavior of a part requires rigorous postprocess microscopy and mechanical testing. In order to circumvent the limited feedback obtained from in situ experiments and to better understand material response, a novel 3D dislocation density based thermomechanical finite element framework has been developed. This framework solves for the in situ response much faster than currently used state-of-the-art modeling software since it has been specifically designed for AM platforms. This modeling infrastructure can predict the anisotropic performance of AM-produced components before they are built, can serve as a method to enable in situ closed-loop process control and as a method to predict residual stress and distortion in parts and thus enable support structure optimization. This manuscript provides an overview of these software modules which together form a robust and reliable AM software suite to address future needs for machine development, material development, and geometric optimization.


2021 ◽  
Vol 17 ◽  
pp. 100264
Author(s):  
Vicky Subhash Telang ◽  
Rakesh Pemmada ◽  
Vinoy Thomas ◽  
Seeram Ramakrishna ◽  
Puneet Tandon ◽  
...  

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1098
Author(s):  
Swee Leong Sing ◽  
Wai Yee Yeong

Additive manufacturing (AM) methods have grown and evolved rapidly in recent years [...]


Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.


Author(s):  
David C. Deisenroth ◽  
Jorge Neira ◽  
Jordan Weaver ◽  
Ho Yeung

Abstract In laser powder bed fusion metal additive manufacturing, insufficient shield gas flow allows accumulation of condensate and ejecta above the build plane and in the beam path. These process byproducts are associated with beam obstruction, attenuation, and thermal lensing, which then lead to lack of fusion and other defects. Furthermore, lack of gas flow can allow excessive amounts of ejecta to redeposit onto the build surface or powder bed, causing further part defects. The current investigation was a preliminary study on how gas flow velocity and direction affect laser delivery to a bare substrate of Nickel Alloy 625 (IN625) in the National Institute of Standards and Technology (NIST) Additive Manufacturing Metrology Testbed (AMMT). Melt tracks were formed under several gas flow speeds, gas flow directions, and energy densities. The tracks were then cross-sectioned and measured. The melt track aspect ratio and aspect ratio coefficient of variation (CV) were reported as a function of gas flow speed and direction. It was found that a mean gas flow velocity of 6.7 m/s from a nozzle 6.35 mm in diameter was sufficient to reduce meltpool aspect ratio CV to less than 15 %. Real-time inline hotspot area and its CV were evaluated as a process monitoring signature for identifying poor laser delivery due to inadequate gas flow. It was found that inline hotspot size could be used to distinguish between conduction mode and transition mode processes, but became diminishingly sensitive as applied laser energy density increased toward keyhole mode. Increased hotspot size CV (associated with inadequate gas flow) was associated with an increased meltpool aspect ratio CV. Finally, it was found that use of the inline hotspot CV showed a bias toward higher CV values when the laser was scanned nominally toward the gas flow, which indicates that this bias must be considered in order to use hotspot area CV as a process monitoring signature. This study concludes that gas flow speed and direction have important ramifications for both laser delivery and process monitoring.


Author(s):  
Chul Y. Park ◽  
Keith E. Rupel ◽  
Chelsey E. Henry ◽  
Kevin F. Malik ◽  
Sayata Ghose ◽  
...  

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