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Journal articles on the topic 'Additive manufacturing process'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

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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2

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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9

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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11

Habib, Md Ahasan, and Bashir Khoda. "Attribute driven process architecture for additive manufacturing." Robotics and Computer-Integrated Manufacturing 44 (April 2017): 253–65. http://dx.doi.org/10.1016/j.rcim.2016.10.003.

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12

Korinko, Paul S., John T. Bobbitt, Michael J. Morgan, Marissa Reigel, Fredrick A. List, and Sudarsanam Suresh Babu. "Characterization of Additive Manufacturing for Process Tubing." JOM 71, no. 3 (January 28, 2019): 1095–104. http://dx.doi.org/10.1007/s11837-019-03341-x.

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13

Wang, Xiaolong, Aimin Wang, Kaixiang Wang, and Yuebo Li. "Process stability for GTAW-based additive manufacturing." Rapid Prototyping Journal 25, no. 5 (June 10, 2019): 809–19. http://dx.doi.org/10.1108/rpj-02-2018-0046.

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Abstract Purpose Traditional gas tungsten arc welding (GTAW) and GTAW-based wire and arc additive manufacturing (WAAM) are notably different. These differences are crucial to the process stability and surface quality in GTAW WAAM. This paper addresses special characteristics and the process control method of GTAW WAAM. The purpose of this paper is to improve the process stability with sensor information fusion in omnidirectional GTAW WAAM process. Design/methodology/approach A wire feed strategy is proposed to achieve an omnidirectional GTAW WAAM process. Thus, a model of welding voltage with welding current and arc length is established. An automatic control system fit to the entire GTAW WAAM process is established using both welding voltage and welding current. The effect of several types of commonly used controllers is examined. To assess the validity of this system, an arc length step experiment, various wire feed speed experiments and a square sample experiment were performed. Findings The research findings show that the resented wire feed strategy and arc length control system can effectively guarantee the stability of the GTAW WAAM process. Originality/value This paper tries to make a foundation work to achieve omnidirectional welding and process stability of GTAW WAAM through wire feed geometry analysis and sensor information fusion control model. The proposed wire feed strategy is implementable and practical, and a novel sensor fusion control method has been developed in the study for varying current GTAW WAAM process.

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Cunico, Marlon Wesley Machado, Miriam Machado Cunico, Patrick Medeiros Cavalheiro, and Jonas de Carvalho. "Investigation of additive manufacturing surface smoothing process." Rapid Prototyping Journal 23, no. 1 (January 16, 2017): 201–8. http://dx.doi.org/10.1108/rpj-11-2015-0176.

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Purpose The additive manufacturing technologies have been facing an extraordinary growth along the past years. This phenomenon might be correlated with rise of low-cost FDM technologies into the non-professional market segment. In contrast with that, among the main disadvantages found in this sort of equipment are the final object finishing and low mechanical strength. For that reason, the purpose of this paper is to present and characterise a surface treatment which is based on solvent vapour attack and that is also known as smoothing process. In addition, a concise overview about the theory beneath this process is presented besides an experimental study that evaluates the main effects on the mechanical properties of object. Design/methodology/approach To analyse the benefits of this process, the authors preliminarily investigated the working mechanism that supports such surface treatment. It allowed them to identify and select a proper solvent for each material. The authors have also established that the exposure time repetition numbers (passes) were the main variables, whereas temperature, solvent type, drying time, object direction and object shape were constants. The main object dimensions, surface roughness, absorbed solvent mass and mechanical strength were the main study responses. Findings As a result of this work, the peak-peak roughness was reduced in 71 per cent, indicating the potential benefit of this process. On the other hand, excessive solvent exposure implied on relevant dimensional distortions and internal disruptures. It was also possible to see that the vapourised solvent penetrate into the object surface and fused layers and filaments. As consequence, the mechanical strength was also improved. Originality/value Despite the growth that additive manufacturing market segment has seen along the past years, the finishing and mechanical strength of low-cost equipment still lack for improvements. For that reason, applications like solvent vapour attack or smoothing process new perspectives for this non-professional segment, whereas roughness and mechanical strength are improved after its treatment. As a consequence, it is possible to consider a final object to be obtained directly from low-cost FDM in combination with smoothing process.

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15

KITAMURA, Yuta, Mitsuyoshi TSUNORI, Masashi MOURI, and Koji NEZAKI. "Process Simulation for Electron Beam Additive Manufacturing." Proceedings of The Computational Mechanics Conference 2018.31 (2018): 085. http://dx.doi.org/10.1299/jsmecmd.2018.31.085.

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TATEISHIT, Motoharu, and Kengo Yoshida. "Introduction of additive manufacturing process simulation technology." Proceedings of The Computational Mechanics Conference 2018.31 (2018): 126. http://dx.doi.org/10.1299/jsmecmd.2018.31.126.

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17

Asadollahi-Yazdi, Elnaz, Julien Gardan, and Pascal Lafon. "Multi-Objective Optimization of Additive Manufacturing Process." IFAC-PapersOnLine 51, no. 11 (2018): 152–57. http://dx.doi.org/10.1016/j.ifacol.2018.08.250.

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18

Ahsan, AMM Nazmul, Md Ahasan Habib, and Bashir Khoda. "Resource based process planning for additive manufacturing." Computer-Aided Design 69 (December 2015): 112–25. http://dx.doi.org/10.1016/j.cad.2015.03.006.

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19

Sing, Swee, and Wai Yeong. "Process–Structure–Properties in Polymer Additive Manufacturing." Polymers 13, no. 7 (March 30, 2021): 1098. http://dx.doi.org/10.3390/polym13071098.

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20

Wang, Yuanbin, Robert Blache, and Xun Xu. "Selection of additive manufacturing processes." Rapid Prototyping Journal 23, no. 2 (March 20, 2017): 434–47. http://dx.doi.org/10.1108/rpj-09-2015-0123.

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Purpose This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings All current studies follow a sequential decision process and focus on an “a priori” articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” articulation of preferences approach is proposed to support DfAM and an iterative design process. Originality/value This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

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Murai, Yu, Shinichi Fukushige, and Hideki Kobayashi. "Environmental Load Evaluation of Automobile Manufacturing Process Using Additive Manufacturing." Proceedings of Design & Systems Conference 2016.26 (2016): 2112. http://dx.doi.org/10.1299/jsmedsd.2016.26.2112.

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22

Liu, Xi-juan. "Modeling of additive manufacturing process relevant feature in layer based manufacturing process planning." Journal of Shanghai Jiaotong University (Science) 17, no. 2 (April 2012): 241–44. http://dx.doi.org/10.1007/s12204-012-1260-6.

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23

P. Cooper, Khershed, and Ralph F. Wachter. "Cyber-enabled manufacturing systems for additive manufacturing." Rapid Prototyping Journal 20, no. 5 (August 12, 2014): 355–59. http://dx.doi.org/10.1108/rpj-01-2013-0001.

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Purpose – The purpose of this paper is to study cyber-enabled manufacturing systems (CeMS) for additive manufacturing (AM). The technology of AM or solid free-form fabrication has received considerable attention in recent years. Several public and private interests are exploring AM to find solutions to manufacturing problems and to create new opportunities. For AM to be commercially accepted, it must make products reliably and predictably. AM processes must achieve consistency and be reproducible. Design/methodology/approach – An approach we have taken is to foster a basic research program in CeMS for AM. The long-range goal of the program is to achieve the level of control over AM processes for industrial acceptance and wide-use of the technology. This program will develop measurement, sensing, manipulation and process control models and algorithms for AM by harnessing principles underpinning cyber-physical systems (CPS) and fundamentals of physical processes. Findings – This paper describes the challenges facing AM and the goals of the CeMS program to meet them. It also presents preliminary results of studies in thermal modeling and process models. Originality/value – The development of CeMS concepts for AM should address issues such as part quality and process dependability, which are key for successful application of this disruptive rapid manufacturing technology.

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Jones, Jason B., David I. Wimpenny, and Greg J. Gibbons. "Additive manufacturing under pressure." Rapid Prototyping Journal 21, no. 1 (January 19, 2015): 89–97. http://dx.doi.org/10.1108/rpj-02-2013-0016.

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Purpose – This paper aims to investigate the effects on material properties of layer-by-layer application of pressure during fabrication of polymeric parts by additive manufacturing (AM). Although AM, also known popularly as 3D printing, has set a new standard for ease of use and minimal restraint on geometric complexity, the mechanical part properties do not generally compare with conventional manufacturing processes. Contrary to other types of polymer processing, AM systems do not normally use (in-process) pressure during part consolidation. Design/methodology/approach – Tensile specimens were produced in Somos 201 using conventional laser sintering (LS) and selective laser printing (SLP) – a process under development in the UK, which incorporates the use of pressure to assist layer consolidation. Findings – Mechanical testing demonstrated the potential to additively manufacture parts with significantly improved microstructure and mechanical properties which match or exceed conventional processing. For example, the average elongation at break and ultimate tensile strength of a conventionally laser-sintered thermoplastic elastomer (Somos 201) increased from 136 ± 28 per cent and 4.9 ± 0.4 MPa, to 513 ± 35 per cent and 10.4 ± 0.4 MPa, respectively, when each layer was fused with in-process application of pressure (126 ± 9 kPa) by SLP. Research limitations/implications – These results are based on relatively small sample size, but despite this, the trends observed are of significant importance to the elimination of voids and porosity in polymeric parts. Practical implications – Layerwise application of pressure should be investigated further for defect elimination in AM. Originality/value – This is the first study on the effects of layerwise application of pressure in combination with area-wide fusing.

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Prajapati, Devendra Kumar, and Ravinder Kumar. "Additive Manufacturing Sustainability in Industries." Advanced Science, Engineering and Medicine 12, no. 7 (July 1, 2020): 894–99. http://dx.doi.org/10.1166/asem.2020.2647.

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Additive manufacturing (AM) is an advanced technique to fabricate a three-dimensional object while utilizing materials with minimal wastage to produce complex shape geometries. This technique has escalated practically as well as academically, resulting in a wide range of utility in the current global scenario to ease the manufacturing of complex and intricate objects with the use of various materials, depending upon the properties and availability of the same. Every industries wants to achieve the sustainability, easily can be possible through this manufacturing process. Due to the scope for a large number of design, material and processing combinations, a detailed outlook to how additive manufacturing can be optimized for a highly sustainable and standardized manufacturing practice needs to be assessed and understood. This paper discusses the core knowledge available regarding this manufacturing process and highlights the different processes related to this technique through review of various research papers. And also discuss the sustainability of important additive manufacturing process. Along with the fundamental analysis of this process, the paper also discusses the various attributes of the process and the growth with respect to the latest trends and techniques currently used in industries.

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Ibabe, Julen, Antero Jokinen, Jari Larkiola, and Gurutze Arruabarrena. "Structural Optimization and Additive Manufacturing." Key Engineering Materials 611-612 (May 2014): 811–17. http://dx.doi.org/10.4028/www.scientific.net/kem.611-612.811.

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Additive Manufacturing technology offers almost unlimited capacity when manufacturing parts with complex geometries which could be impossible to get with conventional manufacturing processes. This paper is based on the study of a particular real part which has been redesigned and manufactured using an AM process. The challenge consists of redesigning the geometry of an originally aluminium made part, in order to get a new stainless steel made model with same mechanical properties but with less weight. The new design is the result of a structural optimization process based on Finite Element simulations which is carried out bearing in mind the facilities that an AM process offers.

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Zhang, Bin, Shunyu Liu, and Yung C. Shin. "In-Process monitoring of porosity during laser additive manufacturing process." Additive Manufacturing 28 (August 2019): 497–505. http://dx.doi.org/10.1016/j.addma.2019.05.030.

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Citarella, Roberto, and Venanzio Giannella. "Additive Manufacturing in Industry." Applied Sciences 11, no. 2 (January 18, 2021): 840. http://dx.doi.org/10.3390/app11020840.

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The advent of additive manufacturing (AM) processes applied to the fabrication of structural components has created the need for design methodologies and structural optimization approaches that take into account the specific characteristics of the fabrication process. While AM processes give unprecedented geometrical design freedom, which can result in significant reductions in the components’ weight (e.g., through part count reduction), on the other hand, they have implications for the fatigue and fracture strength, because of residual stresses and microstructural features. This is due to stress concentration effects, anisotropy, distortions and defects whose effects still need investigation. This Special Issue aims at gathering together research investigating the different features of AM processes with relevance for their structural behavior, particularly, but not exclusively, from the viewpoints of fatigue, fracture and crash behavior. Although the focus of this Special Issue is on AM, articles dealing with other manufacturing processes with related analogies can also be included, in order to establish differences and possible similarities.

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Prashanth, Konda Gokuldoss, and Sergio Scudino. "Quasicrystalline Composites by Additive Manufacturing." Key Engineering Materials 818 (August 2019): 72–76. http://dx.doi.org/10.4028/www.scientific.net/kem.818.72.

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Laser based powder bed fusion (LBPF) or selective laser melting (SLM) is making a leap march towards fabricating novel materials with improved functionalities. An attempt has been made here to fabricate hard quasicrystalline composites via SLM, which demonstrates that the process parameters can be used to vary the phases in the composites. The mechanical properties of the composite depend on their constituents and hence can be varied by varying the process parameters. The results show that SLM not only produces parts with improved functionalities and complex shape but also leads to defined phases and tunable properties.

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Rylands, Brogan, Tillmann Böhme, Robert Gorkin, Joshua Fan, and Thomas Birtchnell. "The adoption process and impact of additive manufacturing on manufacturing systems." Journal of Manufacturing Technology Management 27, no. 7 (September 5, 2016): 969–89. http://dx.doi.org/10.1108/jmtm-12-2015-0117.

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Purpose Company pressure for manufacturers is mounting from two angles: increasing pressure of global competition, and rapid advancements in technology such as additive manufacturing (AM) that are altering the way that goods are manufactured. The purpose of this paper is to explore the adoption process of AM within a manufacturing system and its business impact. Design/methodology/approach Research was conducted to collect empirical data at two manufacturing case companies in the North West England. Both cases are located in areas of industrial recovery using AM engineering innovation for value creation. Findings Early findings showed that the implementation of AM caused a shift in value propositions and the creation of additional value streams (VSs) for the case study companies. AM was shown to compliment and strengthen traditional manufacturing VSs rather than replacing them. Research limitations/implications Limitations include the generalizability due to the number and location of case companies included in this research. Practical implications It is worthwhile to explore the opportunities that AM brings with the existing customer base as it has the potential to add unexplored and untapped value. However, managers need to be mindful of the capability and resources required to put the VS into practice. Social implications Both cases resulted in skill retainment and development due to the implementation of AM. Hence, the innovation contributed to regional economic recovery and business survival. Originality/value This empirical research is one of the early field explorations focussing on the impact of AM on VS structures. Hence, this paper contributes to the area of technology enhanced manufacturing systems.

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31

Grandvallet, Christelle, Frederic Vignat, Franck Pourroy, Guy Prudhomme, and Nicolas Béraud. "An Approach to Model Additive Manufacturing Process Rules." International Journal of Mechanical Engineering and Robotics Research 6, no. 6 (2017): 9–15. http://dx.doi.org/10.18178/ijmerr.7.1.9-15.

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Koizumi, Yuichiro. "Solidification and Process Optimization in Metal Additive Manufacturing." Journal of The Japan Institute of Electronics Packaging 23, no. 6 (September 1, 2020): 446–51. http://dx.doi.org/10.5104/jiep.23.446.

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Gibson, Ian, and Amir Khorasani. "Metallic Additive Manufacturing: Design, Process, and Post-Processing." Metals 9, no. 2 (January 27, 2019): 137. http://dx.doi.org/10.3390/met9020137.

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Klahn, Christoph, Bastian Leutenecker, and Mirko Meboldt. "Design Strategies for the Process of Additive Manufacturing." Procedia CIRP 36 (2015): 230–35. http://dx.doi.org/10.1016/j.procir.2015.01.082.

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INOUE, Takayuki. "Applicability of Additive Manufacturing Process for Medical Devices." Journal of Smart Processing 8, no. 4 (2019): 114–18. http://dx.doi.org/10.7791/jspmee.8.114.

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KOIZUMI, Yuichiro. "Optimization of Additive Manufacturing Process Utilizing Computer Simulation." Journal of Smart Processing 8, no. 4 (2019): 132–38. http://dx.doi.org/10.7791/jspmee.8.132.

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Shukla, Pranjal, Balaram Dash, Degala Venkata Kiran, and Satish Bukkapatnam. "Arc Behavior in Wire Arc Additive Manufacturing Process." Procedia Manufacturing 48 (2020): 725–29. http://dx.doi.org/10.1016/j.promfg.2020.05.105.

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Wortmann, Nadine, Christoph Jürgenhake, Tobias Seidenberg, Roman Dumitrescu, and Dieter Krause. "Methodical Approach for Process Selection in Additive Manufacturing." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (July 2019): 779–88. http://dx.doi.org/10.1017/dsi.2019.82.

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AbstractIn recent years, rapid technical progress has led to additive manufacturing achieving a high degree of technological maturity that enables a broad range of applications. This is reinforced in particular by the advantages of the technology, such as the production of complex components, smaller quantities and fast reaction times. However, a lack of knowledge of the various process techniques, such as insufficient potential assessment, specific design guidelines or even of process restrictions, often lead to different errors.This paper presents a methodological approach to support designers in the manufacturing process selection of specific parts at an early stage of product development. In a four-stage procedure, potential part candidates are first identified and part classes formed on the basis of characteristics. Building on this, AM thinking is to be stimulated, for example, with the aid of design guidelines. A comparison between conventionally and additively manufactured parts can be made using a simplified cost model. The results are incorporated into a process model that supports companies in the systematic selection of manufacturing processes.

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Garcia, Fabricio Leon, Virgínia Aparecida da Silva Moris, Andréa Oliveira Nunes, and Diogo Aparecido Lopes Silva. "Environmental performance of additive manufacturing process – an overview." Rapid Prototyping Journal 24, no. 7 (October 8, 2018): 1166–77. http://dx.doi.org/10.1108/rpj-05-2017-0108.

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Kadkhoda-Ahmadi, Shervin, Alaa Hassan, and Elnaz Asadollahi-Yazdi. "Activity Modeling of Preliminary Additive Manufacturing Process Planning." Procedia CIRP 84 (2019): 874–79. http://dx.doi.org/10.1016/j.procir.2019.05.018.

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Häfele, Tobias, Jan-Henrik Schneberger, Jerome Kaspar, Michael Vielhaber, and Jürgen Griebsch. "Hybrid Additive Manufacturing – Process Chain Correlations and Impacts." Procedia CIRP 84 (2019): 328–34. http://dx.doi.org/10.1016/j.procir.2019.04.220.

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Megahed, Mustafa, Hans-Wilfried Mindt, Narcisse N’Dri, Hongzhi Duan, and Olivier Desmaison. "Metal additive-manufacturing process and residual stress modeling." Integrating Materials and Manufacturing Innovation 5, no. 1 (February 24, 2016): 61–93. http://dx.doi.org/10.1186/s40192-016-0047-2.

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Yao, Bing, Farhad Imani, and Hui Yang. "Markov Decision Process for Image-Guided Additive Manufacturing." IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 2792–98. http://dx.doi.org/10.1109/lra.2018.2839973.

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Vandone, Ambra, Stefano Baraldo, and Anna Valente. "Multisensor Data Fusion for Additive Manufacturing Process Control." IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 3279–84. http://dx.doi.org/10.1109/lra.2018.2851792.

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Xu, Xiaochi, Chaitanya Krishna Prasad Vallabh, Ajay Krishnan, Scott Volk, and Cetin Cetinkaya. "In-Process Thread Orientation Monitoring in Additive Manufacturing." 3D Printing and Additive Manufacturing 6, no. 1 (March 2019): 21–30. http://dx.doi.org/10.1089/3dp.2018.0135.

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Ríos, Sergio, Paul A. Colegrove, Filomeno Martina, and Stewart W. Williams. "Analytical process model for wire + arc additive manufacturing." Additive Manufacturing 21 (May 2018): 651–57. http://dx.doi.org/10.1016/j.addma.2018.04.003.

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Alkadi, Faez, Kyung-Chang Lee, Abdullateef H. Bashiri, and Jae-Won Choi. "Conformal additive manufacturing using a direct-print process." Additive Manufacturing 32 (March 2020): 100975. http://dx.doi.org/10.1016/j.addma.2019.100975.

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Newman, Stephen T., Zicheng Zhu, Vimal Dhokia, and Alborz Shokrani. "Process planning for additive and subtractive manufacturing technologies." CIRP Annals 64, no. 1 (2015): 467–70. http://dx.doi.org/10.1016/j.cirp.2015.04.109.

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Fan, Zongyue, and Bo Li. "Meshfree Simulations for Additive Manufacturing Process of Metals." Integrating Materials and Manufacturing Innovation 8, no. 2 (April 11, 2019): 144–53. http://dx.doi.org/10.1007/s40192-019-00131-w.

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Yan, Wentao, Stephen Lin, Orion L. Kafka, Cheng Yu, Zeliang Liu, Yanping Lian, Sarah Wolff, Jian Cao, Gregory J. Wagner, and Wing Kam Liu. "Modeling process-structure-property relationships for additive manufacturing." Frontiers of Mechanical Engineering 13, no. 4 (February 12, 2018): 482–92. http://dx.doi.org/10.1007/s11465-018-0505-y.

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