Review of NDT and process monitoring techniques usable to produce high-quality parts by welding or additive manufacturing

2018 ◽  
Vol 62 (5) ◽  
pp. 1097-1118 ◽  
Author(s):  
D. Chauveau
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
High Quality ◽  
Monitoring Techniques

scholarly journals Digitization of Welding Processes

2021 ◽  
Author(s):  
Angshuman Kapil ◽  
Syed Quadir ◽  
Abhay Sharma
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Large Scale ◽  
Welding Current ◽  
Suitable Candidate ◽  
Monitoring Techniques ◽  
Wide Range ◽  
Welding Processes ◽  
Integrating Process ◽  
Unique Capability

Welding processes offer a unique capability with a wide range of applications in industries. In recent times, welding has established itself as a tool for large scale additive manufacturing. In general, the quality and repeatability assurance for welding and specifically for additive manufacturing necessitates integrating process monitoring techniques with existing welding and additive manufacturing processes. The process-specific signals such as welding current fluctuations, temperature, and acoustic, generated during the welding operations, make them a suitable candidate for digitization. This chapter comprehensively describes the process monitoring techniques relevant to welding and additive manufacturing. Firstly, various sensors used during welding are described for their construction and working. Subsequently, specific applications of the sensors in digitizing the welding processes are presented.

scholarly journals Towards Wire and Arc Additive Manufacturing of High-Quality Parts

Procedia CIRP ◽  
2020 ◽  
Vol 95 ◽  
pp. 54-59
Author(s):  
Daniel Baier ◽  
Andreas Bachmann ◽  
Michael F. Zaeh
Keyword(s):  
Additive Manufacturing ◽  
High Quality

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

2018 ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Experimental Studies ◽  
Precision Machining ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Ultra Precision ◽  
And Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

scholarly journals A Review on Additive Manufacturing of Micromixing Devices

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 73
Author(s):  
Marina Garcia-Cardosa ◽  
Francisco-Javier Granados-Ortiz ◽  
Joaquín Ortega-Casanova
Keyword(s):  
Life Cycle ◽  
Additive Manufacturing ◽  
High Performance ◽  
Future Prospects ◽  
High Quality ◽  
Printing Technology ◽  
Wide Range ◽  
The Moment ◽  
Manufacturing Technologies

In recent years, additive manufacturing has gained importance in a wide range of research applications such as medicine, biotechnology, engineering, etc. It has become one of the most innovative and high-performance manufacturing technologies of the moment. This review aims to show and discuss the characteristics of different existing additive manufacturing technologies for the construction of micromixers, which are devices used to mix two or more fluids at microscale. The present manuscript discusses all the choices to be made throughout the printing life cycle of a micromixer in order to achieve a high-quality microdevice. Resolution, precision, materials, and price, amongst other relevant characteristics, are discussed and reviewed in detail for each printing technology. Key information, suggestions, and future prospects are provided for manufacturing of micromixing machines based on the results from this review.

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

2020 ◽  
Author(s):  
Zhuo Wang ◽  
Chen Jiang ◽  
Mark F. Horstemeyer ◽  
Zhen Hu ◽  
Lei Chen
Keyword(s):  
Temperature Field ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Surrogate Modeling ◽  
Data Driven ◽  
Modeling Framework ◽  
High Quality ◽  
Experimental Calibration ◽  
Multi Scale ◽  
Metallic Additive

Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.

Effects of Shield Gas Flow on Meltpool Variability and Signature in Scanned Laser Melting

2020 ◽  
Author(s):  
David C. Deisenroth ◽  
Jorge Neira ◽  
Jordan Weaver ◽  
Ho Yeung
Keyword(s):  
Additive Manufacturing ◽  
Aspect Ratio ◽  
Flow Velocity ◽  
Process Monitoring ◽  
Gas Flow ◽  
Laser Energy ◽  
Flow Speed ◽  
Powder Bed ◽  
Flow Speeds ◽  
Gas Flow Velocity

Abstract In laser powder bed fusion metal additive manufacturing, insufficient shield gas flow allows accumulation of condensate and ejecta above the build plane and in the beam path. These process byproducts are associated with beam obstruction, attenuation, and thermal lensing, which then lead to lack of fusion and other defects. Furthermore, lack of gas flow can allow excessive amounts of ejecta to redeposit onto the build surface or powder bed, causing further part defects. The current investigation was a preliminary study on how gas flow velocity and direction affect laser delivery to a bare substrate of Nickel Alloy 625 (IN625) in the National Institute of Standards and Technology (NIST) Additive Manufacturing Metrology Testbed (AMMT). Melt tracks were formed under several gas flow speeds, gas flow directions, and energy densities. The tracks were then cross-sectioned and measured. The melt track aspect ratio and aspect ratio coefficient of variation (CV) were reported as a function of gas flow speed and direction. It was found that a mean gas flow velocity of 6.7 m/s from a nozzle 6.35 mm in diameter was sufficient to reduce meltpool aspect ratio CV to less than 15 %. Real-time inline hotspot area and its CV were evaluated as a process monitoring signature for identifying poor laser delivery due to inadequate gas flow. It was found that inline hotspot size could be used to distinguish between conduction mode and transition mode processes, but became diminishingly sensitive as applied laser energy density increased toward keyhole mode. Increased hotspot size CV (associated with inadequate gas flow) was associated with an increased meltpool aspect ratio CV. Finally, it was found that use of the inline hotspot CV showed a bias toward higher CV values when the laser was scanned nominally toward the gas flow, which indicates that this bias must be considered in order to use hotspot area CV as a process monitoring signature. This study concludes that gas flow speed and direction have important ramifications for both laser delivery and process monitoring.

Thermographic in-situ process monitoring of the electron-beam melting technology used in additive manufacturing

2013 ◽  
Author(s):  
Ralph B. Dinwiddie ◽  
Ryan R. Dehoff ◽  
Peter D. Lloyd ◽  
Larry E. Lowe ◽  
Joe B. Ulrich
Keyword(s):  
Electron Beam ◽  
Additive Manufacturing ◽  
Process Monitoring ◽  
Electron Beam Melting ◽  
Melting Technology
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