Relevant bibliographies by topics / Additive manufacturing process

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Additive manufacturing process'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Additive manufacturing process"

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Scherwitz, Philipp, Steffen Ziegler, and Johannes Schilp. "Process Mining in der additiven Auftragsabwicklung/Process Mining for additive manufacturing." wt Werkstattstechnik online 110, no. 06 (2020): 429–34. http://dx.doi.org/10.37544/1436-4980-2020-06-69.

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Die Fähigkeit der additiven Fertigung in Losgröße 1 zu fertigen, erzeugt eine hohe Komplexität in der Auftragsabwicklung. Dies stellt die datenbasierte Optimierung der Prozessabläufe vor große Herausforderungen. Durch die geringen Stückzahlen, bei einer hohen Variantenanzahl, ist die Prozessaufnahme in der additiven Fertigung mit signifikanten Aufwänden verbunden. Abhilfe kann hier eine automatisierte Prozessaufnahme schaffen. Deshalb soll in diesem Beitrag die Technologie des Process Mining untersucht und darauf aufbauend eine Vorgehensweise für die datenbasierte Optimierung in der additiven Fertigung vorgestellt werden.   The capability of additive manufacturing to produce in batch size 1 creates a high degree of complexity in order processing. This creates great challenges for the data-based optimization of process flows. Due to the low number of pieces, with a high number of variants, the process recording in additive manufacturing is connected with significant expenditures. This can be overcome by automated process recording. Therefore, this article will examine the technology of process mining and, based on this, present a procedure for data-based optimization in additive manufacturing.
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Costa, José, Elsa Sequeiros, Maria Teresa Vieira, and Manuel Vieira. "Additive Manufacturing." U.Porto Journal of Engineering 7, no. 3 (April 30, 2021): 53–69. http://dx.doi.org/10.24840/2183-6493_007.003_0005.

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Additive manufacturing (AM) is one of the most trending technologies nowadays, and it has the potential to become one of the most disruptive technologies for manufacturing. Academia and industry pay attention to AM because it enables a wide range of new possibilities for design freedom, complex parts production, components, mass personalization, and process improvement. The material extrusion (ME) AM technology for metallic materials is becoming relevant and equivalent to other AM techniques, like laser powder bed fusion. Although ME cannot overpass some limitations, compared with other AM technologies, it enables smaller overall costs and initial investment, more straightforward equipment parametrization, and production flexibility.This study aims to evaluate components produced by ME, or Fused Filament Fabrication (FFF), with different materials: Inconel 625, H13 SAE, and 17-4PH. The microstructure and mechanical characteristics of manufactured parts were evaluated, confirming the process effectiveness and revealing that this is an alternative for metal-based AM.
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Tyralla, Dieter, and Thomas Seefeld. "Advanced Process Monitoring in Additive Manufacturing." PhotonicsViews 17, no. 3 (May 28, 2020): 60–63. http://dx.doi.org/10.1002/phvs.202000028.

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Femmer, Tim, Ina Flack, and Matthias Wessling. "Additive Manufacturing in Fluid Process Engineering." Chemie Ingenieur Technik 88, no. 5 (January 12, 2016): 535–52. http://dx.doi.org/10.1002/cite.201500086.

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Gohari, Hossein, Ahmad Barari, Hossam Kishawy, and Marcos S. G. Tsuzuki. "Intelligent Process Planning for Additive Manufacturing." IFAC-PapersOnLine 52, no. 10 (2019): 218–23. http://dx.doi.org/10.1016/j.ifacol.2019.10.067.

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Fadhel, Nawfal F., Richard M. Crowder, and Gary B. Wills. "Provenance in the Additive Manufacturing Process." IFAC-PapersOnLine 48, no. 3 (2015): 2345–50. http://dx.doi.org/10.1016/j.ifacol.2015.06.438.

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Ponche, Remi, Olivier Kerbrat, Pascal Mognol, and Jean-Yves Hascoet. "A novel methodology of design for Additive Manufacturing applied to Additive Laser Manufacturing process." Robotics and Computer-Integrated Manufacturing 30, no. 4 (August 2014): 389–98. http://dx.doi.org/10.1016/j.rcim.2013.12.001.

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Prashanth, Konda Gokuldoss, and Zhi Wang. "Additive Manufacturing: Alloy Design and Process Innovations." Materials 13, no. 3 (January 23, 2020): 542. http://dx.doi.org/10.3390/ma13030542.

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Mäntyjärvi, Kari, Terho Iso-Junno, Henri Niemi, and Jarmo Mäkikangas. "Design for Additive Manufacturing in Extended DFMA Process." Key Engineering Materials 786 (October 2018): 342–47. http://dx.doi.org/10.4028/www.scientific.net/kem.786.342.

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As a new manufacturing method, Additive Manufacturing has begun to get a foothold in the manufacturing industry. The effective exploitation of the technology requires many times a re-design of the product or re-considering the manufacturing technology. Design for additive manufacturing differs considerably from design to other manufacturing methods, therefore design guidelines for additive manufacturing has been developed. The purpose of this paper is to present a new variant of the Design for Manufacturing and Assembly (DFMA) method which supports additive manufacturing.
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Fadhel, Nawfal F., Richard M. Crowder, and Gary B. Wills. "Maintaining Provenance throughout the Additive Manufacturing Process." International Journal for Information Security Research 4, no. 3 (September 1, 2014): 459–68. http://dx.doi.org/10.20533/ijisr.2042.4639.2014.0053.

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Dissertations / Theses on the topic "Additive manufacturing process"

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Philip, Ragnartz, and Axel Staffanson. "Improving the product development process with additive manufacturing." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-40344.

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The following report consists of a master thesis (30 credits) within product development. The thesis is written by Philip Ragnartz and Axel Staffanson, both studying mechanical engineering at Mälardalens University. Developing new components for a production line is costly and time consuming as they must be made from manual measurements and must go through all the conventional manufacturing (CM) steps. Eventual design mistakes will be discovered after the component have been manufactured and tested. To fix the design a completely new component must be designed and therefore double the overall lead time. The purpose of this thesis is to establish how additive manufacturing (AM) can best be used to minimize the cost and lead time in the development of new components. The study was performed by looking at the current product development process in the automotive industry at a large company, here by referred to as company A. 56 components already manufactured at company A´s own tools department was examined and compared to different AM methods. The aim of this was to get a larger pool of data to get an average on production time and cost and see how this differ to the different AM methods. Additionally, two work holders were more closely examined in a case study. Work holder one is a component in the production line that occasionally must be remanufactured. It was examined if this problem could be solved with a desktop plastic printer to hold up for a production batch. Work holder two was the development of a new component, this was to examine the use of printing the component in an early stage impact the development process. The findings from this study is that AM can today not be used in a cost efficient way in manufacturing or development of simple components. This is due to the cost of a metal 3D-printer is still very high, and the building material even higher. This results in components that gets very expensive to make compared to producing them with CM. For design evaluation to be cost efficient there will have to be a design fault in over 12 % of the newly design components for it to be cost effective to print the design for validation before sending it to be manufactured. There are however a lot bigger potential savings in the lead time. Producing the end product with a metal 3D-printer can cut down the lead time up to 85 %. This is thanks to the fact that the printer will produce the component all in one step and therefore not get stuck in between different manufacturing processes. The same goes for design evaluation with printing the component in plastic to confirm the design and not risk having to wait for the component to be manufactured twice. Despite the facts that it is not cost efficient to use AM there are other factors that play an important role. To know that the designed components will work will create a certainty and allow the development process to continue. In some cases it will also allow the designer to improve the design to function better even if the first design would have worked. As AM is expanding machines and build materials will become cheaper. Eventually it will become cheaper to 3D-print even simple components compared to CM. When this occurs, a company cannot simply buy a 3D-printer and make it profitable. There is a learning curve with AM that will take time for the designers to adapt to. Therefore, it is good to start implementing it as soon as possible as it allows for more intricate designs and require experience to do so.
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Han, Tianyang. "Ultrasonic Additive Manufacturing of Steel: Process, Modeling, andCharacterization." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1607039366940573.

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Strano, Giovanni. "Multi-objective optimisation in additive manufacturing." Thesis, University of Exeter, 2012. http://hdl.handle.net/10871/8405.

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Additive Manufacturing (AM) has demonstrated great potential to advance product design and manufacturing, and has showed higher flexibility than conventional manufacturing techniques for the production of small volume, complex and customised components. In an economy focused on the need to develop customised and hi-tech products, there is increasing interest in establishing AM technologies as a more efficient production approach for high value products such as aerospace and biomedical products. Nevertheless, the use of AM processes, for even small to medium volume production faces a number of issues in the current state of the technology. AM production is normally used for making parts with complex geometry which implicates the assessment of numerous processing options or choices; the wrong choice of process parameters can result in poor surface quality, onerous manufacturing time and energy waste, and thus increased production costs and resources. A few commonly used AM processes require the presence of cellular support structures for the production of overhanging parts. Depending on the object complexity their removal can be impossible or very time (and resources) consuming. Currently, there is a lack of tools to advise the AM operator on the optimal choice of process parameters. This prevents the diffusion of AM as an efficient production process for enterprises, and as affordable access to democratic product development for individual users. Research in literature has focused mainly on the optimisation of single criteria for AM production. An integrated predictive modelling and optimisation technique has not yet been well established for identifying an efficient process set up for complicated products which often involve critical building requirements. For instance, there are no robust methods for the optimal design of complex cellular support structures, and most of the software commercially available today does not provide adequate guidance on how to optimally orientate the part into the machine bed, or which particular combination of cellular structures need to be used as support. The choice of wrong support and orientation can degenerate into structure collapse during an AM process such as Selective Laser Melting (SLM), due to the high thermal stress in the junctions between fillets of different cells. Another issue of AM production is the limited parts’ surface quality typically generated by the discrete deposition and fusion of material. This research has focused on the formation of surface morphology of AM parts. Analysis of SLM parts showed that roughness measured was different from that predicted through a classic model based on pure geometrical consideration on the stair step profile. Experiments also revealed the presence of partially bonded particles on the surface; an explanation of this phenomenon has been proposed. Results have been integrated into a novel mathematical model for the prediction of surface roughness of SLM parts. The model formulated correctly describes the observed trend of the experimental data, and thus provides an accurate prediction of surface roughness. This thesis aims to deliver an effective computational methodology for the multi- objective optimisation of the main building conditions that affect process efficiency of AM production. For this purpose, mathematical models have been formulated for the determination of parts’ surface quality, manufacturing time and energy consumption, and for the design of optimal cellular support structures. All the predictive models have been used to evaluate multiple performance and costs objectives; all the objectives are typically contrasting; and all greatly affected by the part’s build orientation. A multi-objective optimisation technique has been developed to visualise and identify optimal trade-offs between all the contrastive objectives for the most efficient AM production. Hence, this thesis has delivered a decision support system to assist the operator in the "process planning" stage, in order to achieve optimal efficiency and sustainability in AM production through maximum material, time and energy savings.
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Joshi, Anay. "Geometric Complexity based Process Selection and Redesign for Hybrid Additive Manufacturing." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin151091601846356.

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Ding, J. "Thermo-mechanical analysis of wire and arc additive manufacturing process." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7897.

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Conventional manufacturing processes often require a large amount of machining and cannot satisfy the continuously increasing requirements of a sustainable, low cost, and environmentally friendly modern industry. Thus, Additive Manufacturing (AM) has become an important industrial process for the manufacture of custom-made metal workpieces. Among the different AM processes, Wire and Arc Additive Manufacture (WAAM) has the ability to manufacture large, low volume metal work-pieces due to its high deposition rate. In this process, 3D metallic components are built by depositing beads of weld metal in a layer by layer fashion. However, the non-uniform expansion and contraction of the material during the thermal cycle results in residual stresses and distortion. To obtain a better understanding of the thermo-mechanical performance of the WAAM process, a study based on FE simulation was untaken in this thesis. The mechanism of the stress generation during the deposition process was analysed via a 3D transient thermo-mechanical FE model which is verified with experimental results. To be capable of analysing the thermo-mechanical behaviour of large-scale WAAM components, an efficient FE approach was developed which can significantly reduce the computational time. The accuracy of this model was validated against the transient model as well as experimental measurements. With the help of the FE models studies on different deposition parameters, deposition sequences and deposition strategies were carried out. It has been proved that the residual stresses and the distortions are possible to be reduced by using optimised deposition parameters and sequences. In addition, a robot path generation prototype has been developed to help efficiently integrate these optimised process settings in the real-wold WAAM process.
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Luo, Xiaoming. "Process planning for an Additive/Subtractive Rapid Pattern Manufacturing system." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3389124.

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Sequeira, Almeida P. M. "Process control and development in wire and arc additive manufacturing." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7845.

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This thesis describes advancements in the modelling, optimisation, process control and mechanical performance of novel high deposition rate gas metal arc welding processes for large scale additive manufacturing applications. One of the main objectives of this study was to develop fundamental understanding of the mechanisms involved during processing with particular focus on single layer welds made of carbon steel using both pulsed-current gas metal arc welding and cold metal transfer processes. The effects of interactions between critical welding process variables and weld bead and plate fusion characteristics are studied for single and multi-layers. It was shown that several bead and plate fusion characteristics are strongly affected by the contact tip to work distance, TRIM, wire feed speed, wire feed speed to travel speed ratio, and wire diameter in pulsed-current gas metal arc welding. The arc-length control, dynamic correction and the contact tip to work distance are shown to strongly influence the weld bead geometry in the cold metal transfer process. This fundamental knowledge was essential to ensure the successful development of predictive interaction models capable of determining the weld bead geometry from the welding process parameters. The models were developed using the least-squares analysis and multiple linear regression method. The gas tungsten constricted arc welding process was utilised for the first time for out-of-chamber fabrication of a large scale and high-quality Ti-6Al-4V component. The main focus was, however, in the use of the cold metal transfer process for improving out-of-chamber deposition of Ti-6Al-4V at much higher deposition rates. The effect of the cold metal transfer process on the grain refinement features in the fusion zone of single layer welds under different torch gas shielding conditions was investigated. It was shown that significant grain refinement occurs with increasing helium content. The morphological features and static mechanical performance of the resulting multi-layered Ti-6Al-4V walls were also examined and compared with those in gas tungsten constricted arc welding. The results show that a considerable improvement in static tensile properties is obtained in both testing directions with cold metal transfer over gas tungsten constricted arc welding. It was suggested that this improvement in the mechanical behaviour could be due to the formation of more fine-grained structures,which are therefore more isotropic. The average ultimate tensile strength and yield strength of the as-deposited Ti-6Al-4V material processed via cold metal transfer meet the minima specification values recommended for most Ti-6Al-4V products. Neutron diffraction technique was used to establish the effect of repeated thermo-mechanical cycling on the generation, evolution and distribution of residual stresses during wire and arc additive manufacturing. The results show a significant redistribution of longitudinal residual stresses along both the substrate and multi-bead with repeated deposition. However, a nearly complete relaxation occurs along the built, once the base plate constraint is removed.
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Hayagrivan, Vishal. "Additive manufacturing : Optimization of process parameters for fused filament fabrication." Thesis, KTH, Lättkonstruktioner, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238184.

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An obstacle to the wide spread use of additive manufacturing (AM) is the difficulty in estimating the effects of process parameters on the mechanical properties of the manufactured part. The complex relationship between the geometry, parameters and mechanical properties makes it impractical to derive an analytical relationship and calls for the use of a numerical model. An approach to formulate a numerical model in developed in this thesis. The AM technique focused in this thesis is fused filament fabrication (FFF). A numerical model is developed by recreating FFF build process in a simulation environment. Machine instructions generated by a slicer to build a part is used to create a numerical model. The model acts as a basis to determine the effects of process parameters on the stiffness and the strength of a part. Determining the stiffness of the part is done by calculating the response of the model to a uniformly distributed load. The strength of the part depends on it's thermal history. The developed numerical model serves as a basis to implement models describing the relation between thermal history and strength. The developed model is suited to optimize FFF parameters as it encompass effects of all FFF parameters. A genetic algorithm is used to optimize the FFF parameters for minimum weight with a minimum stiffness constraint.
Ett hinder för att additiv tillverkning (AT), eller ”3D-printing”, ska få ett bredare genomslag är svårigheten att uppskatta effekterna av processparametrar på den tillverkade produktens mekaniska prestanda. Det komplexa förhållandet mellan geometri och processparametrar gör det opraktiskt och komplicerat att härleda analytiska uttryck för att förutsäga de mekaniska egenskaperna. Alternativet är att istället använda numeriska modeller. Huvudsyftet med denna avhandling har därför varit att utveckla en numerisk modell som kan användas för att förutsäga de mekaniska egenskaperna för detaljer tillverkade genom AT. AT-tekniken som avses är inriktad på Fused Filament Fabrication (FFF). En numerisk modell har utvecklats genom att återskapa FFF-byggprocessen i en simuleringsmiljö. Instruktioner (skriven i GCode) som används för att bygga en detalj genom FFF har här översatts till en numerisk FE-modell. Modellen används sen för att bestämma effekterna av processparametrar på styvheten och styrkan hos den tillverkade detaljen. I detta arbete har strukturstyvheten hos olika detaljer beräknats genom att utvärdera modellens svar för jämnt fördelade belastningsfall. Styrkan, vilket är starkt beroende på den tillverkade detaljens termiska historia, har inte utvärderats. Den utvecklade numeriska modellen kan dock fungera som underlag för implementering av modeller som beskriver relationen mellan termisk historia och styrka. Den utvecklade modellen är anpassad för optimering av FFF-parametrar då den omfattar effekterna av alla FFF-parametrar. En genetisk algoritm har använts i detta arbete för att optimera parametrarna med avseende på vikt för en given strukturstyvhet.
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Nickchen, Tobias [Verfasser]. "Deep learning for automating additive manufacturing process chains / Tobias Nickchen." Paderborn : Universitätsbibliothek, 2021. http://d-nb.info/1234058804/34.

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Karande, Niraj Nitin. "Adoption of Additive Manufacturing in process industries : A case study." Thesis, Uppsala universitet, Industriell teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426129.

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This paper explores the adoption of additive manufacturing technology in the process industries and tries to provide a holistic view about the preference and scope of this technology in the process industry sector. There has been vast literature about use of this technology in the automobile, aerospace, and medical sector. This study will help us to understand how Additive Manufacturing technology is shaping the other process industries and explore if it has same significance. To address the research questions qualitative research method is used in this study with semi-structured interviews with the respondents in process industries and Additive Manufacturing suppliers. All respondents are selected using purposive sampling and remote interviews were conducted with them.The first finding of this study was that additive manufacturing can neither be stated directly as radical or disruptive innovation because this technology has shown both radical as well as disruptive changes in the process industry. Secondly, this technology is adopted in the process industry based on the three innovation attributes: relative advantage, trialability, and observability. Following this, there is discussion on important barriers and how companies are taking efforts to overcome this barrier and adopt this technology easily. Further, this study implies that there is still an immense scope to explore this technology to reap its full benefits. This study gives understanding to AM suppliers that small-scale firms in process industry could be a possible direction to explore for more business opportunities apart from automobile and aerospace industry. For potential researchers in additive manufacturing, this study stands to give understanding for adoption pattern and innovation attributes for which it is valued.
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Books on the topic "Additive manufacturing process"

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Understanding additive manufacturing. Cincinnati, Ohio: Hanser Publications, 2011.

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Rosen, D. W. (David W.) and Stucker B. (Brent), eds. Additive manufacturing technologies: Rapid prototyping to direct digital manufacturing. London: Springer, 2010.

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Slovenia) International Conference on Additive Technologies (3rd 2010 Nova Gorica. Additive layered manufacturing: Education, application and business. Edited by Drstvenšek Igor editor, Dolinšek Slavko editor, and Univerza v Mariboru. Fakulteta za strojništvo. Maribor: Faculty for Mechanical Engineering, University of Maribor, 2010.

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Eschey, Christian. Maschinenspezifische Erhöhung der Prozessfähigkeit in der additiven Fertigung. München: Herbert Utz Verlag, 2013.

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Process–Structure–Properties in Polymer Additive Manufacturing. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1372-0.

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Maniruzzaman, Mohammed. 3D and 4D Printing in Biomedical Applications: Process Engineering and Additive Manufacturing. Wiley & Sons, Limited, John, 2019.

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Maniruzzaman, Mohammed. 3D and 4D Printing in Biomedical Applications: Process Engineering and Additive Manufacturing. Wiley & Sons, Incorporated, John, 2018.

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Maniruzzaman, Mohammed. 3D and 4D Printing in Biomedical Applications: Process Engineering and Additive Manufacturing. Wiley & Sons, Incorporated, John, 2018.

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Additive Manufacturing. Taylor & Francis Group, 2015.

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Additive Manufacturing: Opportunities, Challenges, Implications. Nova Science Publishers, Incorporated, 2016.

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Book chapters on the topic "Additive manufacturing process"

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Srivastava, Manu, Sandeep Rathee, Sachin Maheshwari, and T. K. Kundra. "Generalized Additive Manufacturing Process Chain." In Additive Manufacturing, 51–60. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9781351049382-5.

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Gebhardt, Andreas, and Jan-Steffen Hötter. "Characteristics of the Additive Manufacturing Process." In Additive Manufacturing, 21–91. München: Carl Hanser Verlag GmbH & Co. KG, 2016. http://dx.doi.org/10.3139/9781569905838.002.

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Kumar, Sanjay. "Sheet Based Process." In Additive Manufacturing Processes, 171–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45089-2_11.

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Gibson, Ian, David Rosen, and Brent Stucker. "Guidelines for Process Selection." In Additive Manufacturing Technologies, 303–27. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2113-3_13.

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Gibson, Ian, David W. Rosen, and Brent Stucker. "Guidelines for Process Selection." In Additive Manufacturing Technologies, 333–56. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1120-9_12.

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Gibson, Ian, David Rosen, Brent Stucker, and Mahyar Khorasani. "Guidelines for Process Selection." In Additive Manufacturing Technologies, 429–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56127-7_15.

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Kumar, Sanjay. "Role of Post-Process." In Additive Manufacturing Solutions, 41–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80783-2_4.

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Milewski, John O. "Process Development." In Additive Manufacturing of Metals, 211–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58205-4_10.

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Gibson, Ian, David Rosen, and Brent Stucker. "Generalized Additive Manufacturing Process Chain." In Additive Manufacturing Technologies, 43–61. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2113-3_3.

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Gibson, Ian, David W. Rosen, and Brent Stucker. "Generalized Additive Manufacturing Process Chain." In Additive Manufacturing Technologies, 59–77. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1120-9_3.

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Conference papers on the topic "Additive manufacturing process"

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Felsch, T., F. Silze, and M. Schnick. "Process Control for Robot Based Additive Manufacturing." In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2019. http://dx.doi.org/10.1109/etfa.2019.8869530.

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Venkatesan, Uppili Srinivasan, and S. S. Pande. "Efficient Process Planning Strategies for Additive Manufacturing." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2666.

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This work reports the development of robust and efficient algorithms for optimum process planning of Additive Manufacturing (AM) processes needing support structures during fabrication. In particular, it addresses issues like part hollowing, support structure generation and optimum part orientation. Input to the system is a CAD model in STL format which is voxelized and hollowed using the 2D Hollowing strategy. A novel approach to design external as well as internal support structures for the hollowed model is developed considering the wall thickness and material properties. Optimum orientation of the hollowed part model is computed using Genetic Algorithm (GA). The Fitness Function for optimization is the weighted average of process performance parameters like build time, part quality and material utilization. A new performance measure has been proposed to choose the weightages for performance parameters to obtain overall optimum performance. The paper presents, in detail, the design and development of algorithms with results for typical case studies. The proposed methodology will significantly contribute to improving part quality, productivity and material utilization for AM processes.
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Lakshmanan, Kannappan, Narasimalu Srikanth, and Loganathan Pranava Saai. "Additive manufacturing process towards wind turbine components." In 2017 Asian Conference on Energy, Power and Transportation Electrification (ACEPT). IEEE, 2017. http://dx.doi.org/10.1109/acept.2017.8168547.

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Taki, Kentaro, and Hiroshi Ito. "Numerical Simulation of 3D Additive Manufacturing Process." In Proceedings of the 4M/ICOMM2015 Conference. Singapore: Research Publishing Services, 2015. http://dx.doi.org/10.3850/978-981-09-4609-8_107.

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Deepa, C., and K. Tharageswari. "Smart material process in additive healthcare manufacturing." In Third International Conference on Material Science, Smart Structures and Applications: (ICMSS 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0039749.

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Dubrov, Alexander V., Fikret Kh Mirzade, and Vladimir D. Dubrov. "On multi-scale modelling of dendrite growth during laser metal deposition process." In 3D Printed Optics and Additive Photonic Manufacturing, edited by Georg von Freymann, Alois M. Herkommer, and Manuel Flury. SPIE, 2018. http://dx.doi.org/10.1117/12.2307555.

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Dubrov, Alexander V., Fikret Kh Mirzade, Vladimir D. Dubrov, and Pavel S. Rodin. "Numerical simulation of thermal behavior for process parameters optimization in laser additive manufacturing." In 3D Printed Optics and Additive Photonic Manufacturing, edited by Georg von Freymann, Alois M. Herkommer, and Manuel Flury. SPIE, 2018. http://dx.doi.org/10.1117/12.2307535.

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Owsiński, Robert, and Adam Niesłony. "Fatigue properties in additive manufacturing methods applying Ti6Al4V." In 2ND INTERNATIONAL CONFERENCE ON CHEMISTRY, CHEMICAL PROCESS AND ENGINEERING (IC3PE). Author(s), 2018. http://dx.doi.org/10.1063/1.5066511.

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BRENKEN, BASTIAN, ANTHONY FAVALORO, EDUARDO BAROCIO, VLASTIMIL KUNC, and R. BYRON PIPES. "Thermoviscoelasticity in Extrusion Deposition Additive Manufacturing Process Simulations." In American Society for Composites 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/asc2017/15223.

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Luo, Zhibo, Fan Yang, Guoying Dong, Yunlong Tang, and Yaoyao Fiona Zhao. "Orientation Optimization in Layer-Based Additive Manufacturing Process." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59969.

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The advent of Additive Manufacturing (AM) process has greatly broadened the machining methods. Compared to conventional manufacturing methods, the process planning for AM is totally different. It should avoid process-induced defects such as warpage of overhang features. Process planning for AM should also generate necessary support structure not only to support the overhang structure but also to minimize thermal warpage and residual stress. In order to do so, a general process planning for AM is put forward in this paper. Given a specific part, the first step is the determination of build orientation. The choice of build orientation is one of the critical factors in AM since the build time, the material consumption, the removal of support structure, the deformation within final parts, the mechanical performance, and the surface roughness are all related to the build orientation. This paper utilizes the genetic algorithm to optimize the build orientation by considering the minimum volume of the support structure and the minimum strain energy of a part under specific working conditions. First, a general and feasible process planning for AM is proposed. Then detailed process planning for the optimization of build orientation is developed. The volume of support structure and strain energy are considered independently and corresponding optimal build orientations are obtained through genetic algorithm. A single weighted aggregate optimization function is then constructed to optimize the volume of support structure and strain energy simultaneously. Finally, a bracket is used to verify the feasibility of the proposed method.
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Reports on the topic "Additive manufacturing process"

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Beghini, Lauren L., Michael Stender, and Michael Veilleux. Process Modeling for Additive Manufacturing. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562431.

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Lee, Yousub, Srdjan Simunovic, and A. Kate Gurnon. Quantification of Powder Spreading Process for Metal Additive Manufacturing. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1615799.

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Love, Lonnie, Brian Post, Alex Roschli, and Phillip Chesser. Big Area Additive Manufacturing Engineering Development, Process Trials, and Composite Core Fabrication. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1606868.

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Wedgewood, Alan, Pasita Pibulchinda, Eduardo Vaca, Charles Hill, and Michael Bogdanor. Materials Development and Advanced Process Simulation for Additive Manufacturing with Fiber-Reinforced Thermoplastics. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1769016.

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Sridharan, Niyanth, Ryan R. Dehoff, Brian H. Jordan, and Sudarsanam Suresh Babu. Development of coatings for ultrasonic additive manufacturing sonotrode using laser direct metal deposition process. Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1331097.

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Kirka, Michael M., Kinga A. Unocic, Keith Kruger, and Alison Forsythe. Process Development for Haynes® 282® Using Additive Manufacturing. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1435227.

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Kamath, C. Determination of Process Parameters for High-Density, Ti-6Al-4V Parts Using Additive Manufacturing. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1413166.

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Huning, Alex, Randall Fair, Alyson Coates, and Bruce Lin. TCR Input to NUREG-1537 Process for Advanced Nuclear Technologies Derived from Additive Manufacturing. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1805005.

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Martin, A. Laser Powder Bed Fusion Additive Manufacturing In-Process Monitoring and Optimization Using Thermionic Emission Detection. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1647152.

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Clark, Brett W., Kimberly A. Diaz, Chinaza Darlene Ochiobi, and Kamran Paynabar. Solving the Big Data (BD) Problem in Advanced Manufacturing (Subcategory for work done at Georgia Tech. Study Process and Design Factors for Additive Manufacturing Improvement). Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1221177.

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