Data-Driven Modeling for Additive Manufacturing of Metals Proceedings of a Workshop (2019) / Chapter Skim
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2 Process Monitoring and Control
2 Process Monitoring and Control
Pages 3-18
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, Ben Dutton (Manufacturing Technology Centre) , and Amit Surana (United Technologies Research Center)
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• What are the challenges of moving from monitoring to feedback control? MEASUREMENTS AND MODELING FOR PROCESS MONITORING AND CONTROL Bianca Maria Colosimo, Politecnico di Milano Colosimo described Politecnico di Milano's AddMe.Lab, a laboratory combining industrial machines and novel prototypes for AM processes such as selective laser melting, electron beam melting, directed energy deposition with powder and wire feedstocks, and binder jetting.
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TABLE 2.1 Defect Sources and Categories by Publication Categories of defects Residual stresses, Microstructural Geometric cracks, and inhomogeneity Sources of defects Porosity Balling defects Surface defects delamination and impurity Equipment Beam Foster et al., Moylan et al., scanning/ 2015 2014; Foster et deflection al., 2015 Build chamber Ferrar et al., Li et al., Edwards et al., Spears and Gold, environment 2012; Spears 2012 2013; Chlebus 2016 and Gold, et al., 2011; 2016 Buchbinder et al., 2014; Kempen et al., 2013 Powder Foster et al., Foster et Foster et Foster et al., 2015 handling and 2015 al., 2015; al., 2015; deposition Kleszczynski et Kleszczynski et al., 2012 al., 2012 Baseplate Prabhakar et Prabhakar al., 2015 et al., 2015 5 continued
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6 TABLE 2.1 Continued Categories of defects Residual stresses, Microstructural Geometric cracks, and inhomogeneity Sources of defects Porosity Balling defects Surface defects delamination and impurity Process Parameters Matthews Li et al., Yasa et Li et al., 2012; Mercelis and Carter et al., and scan et al., 2016; 2012; Kruth al., 2009; Kruth et al., Kruth, 2006; 2014; Arisoy et strategy Yasa et al., et al., 2004; Mousa, 2016; 2004; Matthews Parry et al., al., 2017; Niu 2009; Attar, Tolochko Kleszczynski et al., 2016; 2016; Cheng et and Chang, 1999; 2011; Gong et al., 2004; et al., 2012; Attar, 2011; al., 2016; Van Huang et al., 2016; et al., 2013; Zhou et al., Thomas, 2009 Gong et al., Belle et al., Thijs et al., 2010; Read et al., 2015; Attar, 2013; Zaeh and 2013; Casavola Scharowsky et al., 2015; Kruth 2011; Gong Kanhert, 2009; et al., 2008; Zäh 2015; Puebla et al., et al., 2004; et al., 2013 Delgado et al., and Lutzmann, 2012; Biamino et Weingarten et 2012 2010; Zaeh and al., 2011 al., 2015; Thijs Branner, 2010; et al., 2010; Kempen et al., Scharowsky 2013; Kruth et et al., 2015; al., 2004; Carter Puebla et al., et al., 2014 2012; Tammas Williams et al., 2015; Biamino et al., 2011; Zeng, 2015 Byproducts Liu et Liu et al., 2015; and material al., 2015; Khairallah et al., ejections Khairallah 2016 et al., 2016
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Grasso and B.M. Colosimo, 2017, Process defects and in-situ monitoring methods in metal powder-bed fusion: A review, Measurement Science and Technology 28(4)
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Some pending issues, however, include correlating the process signature with product quality and modeling defects appropriately. She also outlined key sensing questions: How should the appropriate sensors and their spatial and temporal resolutions be chosen?
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Ultrasonic sensors can be used to detect subsurface defects by sending ultrasonic waves through the part, and acoustic sensors can detect meltpool quality and part failure by monitoring the acoustic emissions from the melt pool and cracks. Heigel explained that real-time monitoring enables both statistical process control and feedback control.
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but lack fidelity to interpret processing quality. For directed energy deposition systems, coaxial melt-pool imaging is currently being used for real-time monitoring and control and feedback control because the process dynamics are comparatively slower.
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He noted that there are some challenges in powder-bed versus directed energy deposition. For example, ultrasonic measurements tend to be transferrable, but differences in process speed can create different sized melt pools and cause a different formation.
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simulations based on the mechanical process equivalent method in its software Amphyon.2 Some goals and applications for this simulation are fast computation of residual stress and distortion, fast estimation, identification of critical areas, adaptation of the design, and simulation-based adaptation of support structures. Ade Makinde ( eneral Electric Global Research Center)
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of defects and sizes that the part can handle would be helpful in establishing quality requirements for a part. In response to a question about the effect of sensor distribution, Dutton mentioned that most current sensing methods are only looking at the top surface and can miss deeper defects.
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Heigel responded that although NIST is not specifically looking into this, data storage is an important consideration. Colosimo encouraged widespread data sharing to enable faster progress.
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2018. In-situ thermographic mea surement during additive manufacturing selective laser melting [Master thesis]
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2015. A brief survey of sensing for metal-based powder bed fusion additive manufacturing.
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2014. Infrared thermography for laser based powder bed fusion additive manufacturing processes.
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Investigation of residual stresses induced during the selective laser melting process. Key Engineering Materials 554–557:1828–1834. Van Elsen, M
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