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Full-Text Articles in Manufacturing
Effects Of Particle Size Distribution With Efficient Packing On Powder Flowability And Selective Laser Melting Process, Zachary Young, Minglei Qu, Meelap Michael Coday, Qilin Guo, Seyed Mohammad H. Hojjatzadeh, Luis I. Escano, Kamel Fezzaa, Lianyi Chen
Effects Of Particle Size Distribution With Efficient Packing On Powder Flowability And Selective Laser Melting Process, Zachary Young, Minglei Qu, Meelap Michael Coday, Qilin Guo, Seyed Mohammad H. Hojjatzadeh, Luis I. Escano, Kamel Fezzaa, Lianyi Chen
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The powder bed-based additive manufacturing (AM) process contains uncertainties in the powder spreading process and powder bed quality, leading to problems in repeatability and quality of the additively manufactured parts. This work focuses on identifying the uncertainty induced by particle size distribution (PSD) on powder flowability and the laser melting process, using Ti6Al4V as a model material. The flowability test results show that the effect of PSDs on flowability is not linear, rather the PSDs near dense packing ratios cause significant reductions in flowability (indicated by the increase in the avalanche angle and break energy of the powders measured by …
Industry 4.0 Remanufacturing: A Novel Approach Towards Smart Remanufacturing, Prashansa Ragampeta
Industry 4.0 Remanufacturing: A Novel Approach Towards Smart Remanufacturing, Prashansa Ragampeta
Masters Theses
“Smart remanufacturing has become more popular in recent years as a result of its multiple benefits and the growing need for society to encourage a circular economy that leads to sustainability. One of the most common end-of-life (EoL) choices that can lead to a circular economy is remanufacturing. As a result, at the end-of-life stage of a product, it is critical to prioritize this choice over other accessible options because it is the only recovery option that retains the same quality as a new product. This work focuses on the numerous technologies that can aid in the improvement of smart …
A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman
A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman
Masters Theses
“Additive manufacturing (AM) is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. The convolutional neural network (CNN) has been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes. A network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the …
Characterization Of High Cycle Fatigue And Laser-Aided Machining And Polishing Of Additively Manufactured Materials, Mohammad Masud Parvez
Characterization Of High Cycle Fatigue And Laser-Aided Machining And Polishing Of Additively Manufactured Materials, Mohammad Masud Parvez
Doctoral Dissertations
“Additive manufacturing (AM) and laser-aided machining and polishing (LAMP) of materials are emerging manufacturing processes both for research and industrial sectors. The AM process can manufacture near-net-shape parts with complex geometries. Meanwhile, the LAMP process integrated with an AM system offers a high processing rate, minimum heat-affected zone, and easily adjustable process parameters during machining and polishing. In mechanical properties characterization of AM metals and alloys, fatigue is a vitally important test method to understand the behavior of materials in cycling loading and unloading circumstances since most mechanical failures of structures are due to fatigue. To characterize AM metal fatigue …