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•Digital twin will reduce trial and error testing and accelerate the product qualification.•Twin consists of mechanistic, control & statistical models, machine learning & big ...data.•Twin will be used to obtain desired product attributes and to make printing cost effective.
The customized production of complex components by 3D printing has been hailed as a potentially transformative tool in manufacturing with important applications in health care, automotive and aerospace industries. However, after about a quarter of a century of research and development, only a handful of commercial alloys can be printed and the market value of all 3D printed products now amounts to a negligible portion of the manufacturing economy. This difficulty is attributable to a remarkable diversity in structure and properties of the printed components and susceptibility to defects. In addition, the current practice of qualifying components by prolonged trial and error with expensive printing equipment and feed stock material confine the printed products to a niche market where the high product cost and the delay in the qualification are not critical factors. Here we explain how a digital twin or a digital replica of the printing machine will reduce the number of trial and error tests to obtain desired product attributes and reduce the time required for part qualification to make the printed components cost effective. It is shown that a comprehensive digital twin of 3D printing machine consisting of mechanistic, control and statistical models of 3D printing, machine learning and big data can reduce the volume of trial and error testing, reduce defects and shorten time between the design and production.
Additive manufacturing (AM), also known as 3D printing, is gaining wide acceptance in diverse industries for the manufacturing of metallic components. The microstructure and properties of the ...components vary widely depending on printing process and process parameters, and prediction of causative variables that affect structure, properties and defects is helpful for their control. Since models are most useful when they can correctly predict experimental observations, we focus on the available mechanistic models of AM that have been adequately validated. Specifically, the applications of transport phenomena models in the studies of solidification, residual stresses, distortion, formation of defects and the evolution of microstructure and properties are critically reviewed. The functionality of AM models in understanding of the printability of commonly used AM alloys and the fabrication of functionally graded alloys are also assessed. Opportunities for future research are identified considering the gaps in knowledge in modeling. The uniqueness of this review includes substantive discussions of the rapid certification of the AM components aided by scale models, bidirectional models, cloud based big data, machine learning and digital twins of AM hardware.
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•An easy to use dimensionless number is proposed to mitigate lack of fusion defects.•The number correlates well with the measured void fractions for three alloys.•Relative ...susceptibilities of three alloys are demonstrated using this correlation.•A well-tested heat transfer and fluid flow model is used to calculate pool dimensions.•Monitoring temperature can help to reduce the lack of fusion defect.
Components manufactured by additive manufacturing often exhibit improper fusion among layers and hatches. Currently, there is no practical way to select process parameters and alloy systems based on scientific principles to mitigate these defects. Here, we develop, test and demonstrate a methodology to predict and prevent these defects based on a numerical heat transfer and fluid flow model for the laser powder bed fusion (PBF) additive manufacturing (AM). These defects are avoidable by adjusting laser power, scanning speed, layer thickness and hatch spacing. An easy to use parameter is proposed for practical use in shop floors. Relative susceptibilities of three widely used AM alloys are demonstrated using this parameter.
Additively manufactured parts are often distorted because of spatially variable heating and cooling. Currently there is no practical way to select process variables based on scientific principles to ...alleviate distortion. Here we develop a roadmap to mitigate distortion during additive manufacturing using a strain parameter and a well-tested, three-dimensional, numerical heat transfer and fluid flow model. The computed results uncover the effects of both the key process variables such as power, scanning speed, and important non-dimensional parameters such as Marangoni and Fourier numbers and non-dimensional peak temperature on thermal strain. Recommendations are provided to mitigate distortion based on the results.
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Since its inception, significant progress has been made in understanding additive manufacturing (AM) processes and the structure and properties of the fabricated metallic components. Because the ...field is rapidly evolving, a periodic critical assessment of our understanding is useful and this paper seeks to address this need. It covers the emerging research on AM of metallic materials and provides a comprehensive overview of the physical processes and the underlying science of metallurgical structure and properties of the deposited parts. The uniqueness of this review includes substantive discussions on refractory alloys, precious metals and compositionally graded alloys, a succinct comparison of AM with welding and a critical examination of the printability of various engineering alloys based on experiments and theory. An assessment of the status of the field, the gaps in the scientific understanding and the research needs for the expansion of AM of metallic components are provided.
Although additive manufacturing (AM), or three dimensional (3D) printing, provides significant advantages over existing manufacturing techniques, metallic parts produced by AM are susceptible to ...distortion, lack of fusion defects and compositional changes. Here we show that the printability, or the ability of an alloy to avoid these defects, can be examined by developing and testing appropriate theories. A theoretical scaling analysis is used to test vulnerability of various alloys to thermal distortion. A theoretical kinetic model is used to examine predisposition of different alloys to AM induced compositional changes. A well-tested numerical heat transfer and fluid flow model is used to compare susceptibilities of various alloys to lack of fusion defects. These results are tested and validated with independent experimental data. The findings presented in this paper are aimed at achieving distortion free, compositionally sound and well bonded metallic parts.
Properties and serviceability of additively manufactured components are affected by their geometry, microstructure and defects. These important attributes are now optimized by trial and error because ...the essential process variables cannot currently be selected from scientific principles. A recourse is to build and rigorously validate a digital twin of the additive manufacturing process that can provide accurate predictions of the spatial and temporal variations of metallurgical parameters that affect the structure and properties of components. Key building blocks of a computationally efficient first-generation digital twin of laser-based directed energy deposition additive manufacturing utilize a transient, three-dimensional model that calculates temperature and velocity fields, cooling rates, solidification parameters and deposit geometry. The measured profiles of stainless steel 316L and Alloy 800H deposits as well as the secondary dendrite arm spacing (SDAS) and Vickers hardness measurements are used to validate the proposed digital twin. The predicted cooling rates, temperature gradients, solidification rates, SDAS and micro-hardness values are shown to be more accurate than those obtained from a commonly used heat conduction calculation. These metallurgical building blocks serve as a phenomenological framework for the development of a digital twin that will make the expanding knowledge base of additive manufacturing usable in a practical way for all scientists and engineers.
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Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with ...site-specific chemical compositions and properties from 3D designs. However, the selection of alloys, printing processes and process variables results in an exceptional diversity of microstructures, properties and defects that affect the serviceability of the printed parts. Control of these attributes using the rich knowledge base of metallurgy remains a challenge because of the complexity of the printing process. Transforming 3D designs created in the virtual world into high-quality products in the physical world needs a new methodology not commonly used in traditional manufacturing. Rapidly developing powerful digital tools such as mechanistic models and machine learning, when combined with the knowledge base of metallurgy, have the potential to shape the future of metal printing. Starting from product design to process planning and process monitoring and control, these tools can help improve microstructure and properties, mitigate defects, automate part inspection and accelerate part qualification. Here, we examine advances in metal printing focusing on metallurgy, as well as the use of mechanistic models and machine learning and the role they play in the expansion of the additive manufacturing of metals.Several key industries routinely use metal printing to make complex parts that are difficult to produce by conventional manufacturing. Here, we show that a synergistic combination of metallurgy, mechanistic models and machine learning is driving the continued growth of metal printing.
3D printing is now widely used in aerospace, healthcare, energy, automotive and other industries. Metal printing, in particular, is the fastest growing sector, yet its development presents ...scientific, technological and economic challenges that must be understood and addressed.
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