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Defect Detection Using Dynamic Analysis for Additive Manufactured Metals

Defect Detection Using Dynamic Analysis for Additive Manufactured Metals
Author:
Publisher:
Total Pages:
Release: 2021
Genre:
ISBN:
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Abstract : Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Smart Additive Manufacturing

Smart Additive Manufacturing
Author: Mohammad Montazeri
Publisher:
Total Pages: 0
Release: 2019
Genre: Additive manufacturing
ISBN: 9781392485477
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The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%.

Defect Detection and Monitoring in Metal Additive Manufactured Parts Through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals

Defect Detection and Monitoring in Metal Additive Manufactured Parts Through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals
Author: Jacob Williams
Publisher:
Total Pages: 23
Release: 2018
Genre: Acoustic surface waves
ISBN:
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Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process that promises to herald a new age in manufacturing by removing many of the design and material-related constraints of traditional subtractive and formative manufacturing processes. However, the level and severity of defects observed in parts produced by the current class of LPBF systems will not be tolerated in safety-critical applications. Hence, there is a need to introduce information-rich process monitoring to assess part integrity simultaneously with fabrication so that opportune corrective action can be taken to minimize part defects. Spatially Resolved Acoustic Spectroscopy (SRAS) is a uniquely positioned nondestructive acoustic microscopy sensing approach that has been successfully used to probe the mechanical properties and assess the presence of defects in LPBF parts. However, the technique is sensitive to extraneous phenomena, such as surface reflectivity, which occur within the LPBF system and may occlude identification of surface breaking and subsurface defects. With a view to applying the SRAS technique for in-process monitoring in a production-scale LPBF environment and to overcome the foregoing challenge, this study proposes the use of a deep learning convolutional neural network that is termed Densely connected Convolutional Block Architecture for Multimodal Image Regression (DCB-MIR), which invokes SRAS-derived acoustic velocity maps of the part as input data and translates them to an output resembling an optical micrograph. Through this approach, we demonstrate that defects, such as porosity and surface imperfections in titanium alloy and nickel alloy specimens made using LPBF, which were not clearly discernable in the as-measured SRAS acoustic map and were obscured by artifacts in the optical image, are accurately identified. To quantify the accuracy of the approach, the cosine similarity between the predicted output images and target optical images was used as the objective function of DCB-MIR. The cosine similarity between the acquired SRAS signatures and the corresponding as-measured optical micrographs of samples typically ranged between -0.15 and 0.15. In contrast, when the optical micrograph-like images derived from DCB-MIR proposed in this work were compared with the optical signatures, the cosine similarity improved in the range of 0.25 to 0.60.

Optical Measurement of Surface Topography

Optical Measurement of Surface Topography
Author: Richard Leach
Publisher: Springer Science & Business Media
Total Pages: 333
Release: 2011-03-31
Genre: Technology & Engineering
ISBN: 3642120121
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The measurement and characterisation of surface topography is crucial to modern manufacturing industry. The control of areal surface structure allows a manufacturer to radically alter the functionality of a part. Examples include structuring to effect fluidics, optics, tribology, aerodynamics and biology. To control such manufacturing methods requires measurement strategies. There is now a large range of new optical techniques on the market, or being developed in academia, that can measure areal surface topography. Each method has its strong points and limitations. The book starts with introductory chapters on optical instruments, their common language, generic features and limitations, and their calibration. Each type of modern optical instrument is described (in a common format) by an expert in the field. The book is intended for both industrial and academic scientists and engineers, and will be useful for undergraduate and postgraduate studies.

Metal Additive Manufacturing

Metal Additive Manufacturing
Author: Dyuti Sarker
Publisher: John Wiley & Sons
Total Pages: 624
Release: 2021-10-26
Genre: Science
ISBN: 1119210836
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METAL ADDITIVE MANUFACTURING A comprehensive review of additive manufacturing processes for metallic structures Additive Manufacturing (AM)—also commonly referred to as 3D printing—builds three-dimensional objects by adding materials layer by layer. Recent years have seen unprecedented investment in additive manufacturing research and development by governments and corporations worldwide. This technology has the potential to replace many conventional manufacturing processes, enable the development of new industry practices, and transform the entire manufacturing enterprise. Metal Additive Manufacturing provides an up-to-date review of all essential physics of metal additive manufacturing techniques with emphasis on both laser-based and non-laser-based additive manufacturing processes. This comprehensive volume covers fundamental processes and equipment, governing physics and modelling, design and topology optimization, and more. The text adresses introductory, intermediate, and advanced topics ranging from basic additive manufacturing process classification to practical and material design aspects of additive manufacturability. Written by a panel of expert authors in the field, this authoritative resource: Provides a thorough analysis of AM processes and their theoretical foundations Explains the classification, advantages, and applications of AM processes Describes the equipment required for different AM processes for metallic structures, including laser technologies, positioning devices, feeder and spreader mechanisms, and CAD software Discusses the opportunities, challenges, and current and emerging trends within the field Covers practical considerations, including design for AM, safety, quality assurance, automation, and real-time control of AM processes Includes illustrative cases studies and numerous figures and tables Featuring material drawn from the lead author’s research and professional experience on laser additive manufacturing, Metal Additive Manufacturing is an important source for manufacturing professionals, research and development engineers in the additive industry, and students and researchers involved in mechanical, mechatronics, automatic control, and materials engineering and science.

Additive Manufacturing

Additive Manufacturing
Author: Pulak Mohan Pandey
Publisher: CRC Press
Total Pages: 221
Release: 2023-02-06
Genre: Technology & Engineering
ISBN: 1000835014
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The text explores the development, use, and effect of additive manufacturing and digital manufacturing technologies for diverse applications. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields including industrial, manufacturing, and materials science. This book: Discusses the application of 3D virtual models to lasers, electron beams, and computer-controlled additive manufacturing machines Covers applications of additive manufacturing in diverse areas including healthcare, electronics engineering, and production engineering Explains the use of additive manufacturing for biocomposites and functionally graded materials Highlights rapid manufacturing of metallic components using 3D printing Illustrates production and optimization of dental crowns using additive manufacturing This book covers recent developments in manufacturing technology, such as additive manufacturing, 3D printing, rapid prototyping, production process operations, and manufacturing sustainability. The text further emphasizes the use of additive manufacturing for biocomposites and functionally graded materials. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, automotive engineering, aerospace engineering, and materials science.

Quality Analysis of Additively Manufactured Metals

Quality Analysis of Additively Manufactured Metals
Author: Javad Kadkhodapour
Publisher: Elsevier
Total Pages: 856
Release: 2022-11-15
Genre: Technology & Engineering
ISBN: 0323886647
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Quality Analysis of Additively Manufactured Metals: Simulation Approaches, Processes, and Microstructure Properties provides readers with a firm understanding of the failure and fatigue processes of additively manufactured metals. With a focus on computational methods, the book analyzes the process-microstructure-property relationship of these metals and how it affects their quality while also providing numerical, analytical, and experimental data for material design and investigation optimization. It outlines basic additive manufacturing processes for metals, strategies for modeling the microstructural features of metals and how these features differ based on the manufacturing process, and more. Improvement of additively manufactured metals through predictive simulation methods and microdamage and micro-failure in quasi-static and cyclic loading scenarios are covered, as are topology optimization methods and residual stress analysis techniques. The book concludes with a section featuring case studies looking at additively manufactured metals in automotive, biomedical and aerospace settings. Provides insights and outlines techniques for analyzing why additively manufactured metals fail and strategies for avoiding those failures Defines key terms and concepts related to the failure analysis, quality assurance and optimization processes of additively manufactured metals Includes simulation results, experimental data and case studies

Metal Additive Manufacturing Defects Analysis and Prediction of Their Effect on Fatigue Performance

Metal Additive Manufacturing Defects Analysis and Prediction of Their Effect on Fatigue Performance
Author: Niloofar Sanaei
Publisher:
Total Pages: 0
Release: 2020
Genre: Additive manufacturing
ISBN:
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The technology of additive manufacturing (AM) which is building up parts by joining materials directly informed by 3D model data, is developing rapidly in many industries. It offers advantages such as delivering intricate and complex geometries with the least post processing required. The components made by AM are of interest in biomedical and aerospace applications. AM technologies should produce unique critical parts, such as prosthetics which undergo cyclic loading throughout their service life. Therefore, understanding fatigue behavior of AM metals is one of the most important steps in their part design. In addition, in many applications, the loading condition is multiaxial. Even in parts under uniaxial loading condition, the stress state may still be a combination of axial and shear stress due to complex geometry or interaction of residual stresses and existence of defects. The intrinsic defects of AM metals and the effect of process and post processing parameters on them were investigated in this study. The microstructure and defect content of AM metals and their correlation with fatigue performance were reviewed. The objective was to analyze characteristics of intrinsic AM defects and their variability for various materials, processing and post-processing conditions. The aim was also to evaluate the fatigue behavior of the defective AM metals, develop an appropriate defect sensitive fatigue life prediction framework, and verify the predictions with experimental data. Fracture Mechanics concepts along with phenomenological, empirical, and semi-empirical models such as Murakami's area parameter were applied. Comprehensive defect analysis and fracture surface analysis were performed in 2D by digital microscopy and scanning electron microscopy and in 3D using X-ray computed tomography. Important surface and internal defect characteristics such as size, shape (aspect ratio), location, and orientation, the material microstructure, and the loading and failure mechanism for each batch of specimens were investigated. The key factors were incorporated into fatigue life predictions with appropriate representations. Statistical functions to model the distribution of the defect characteristics, and Extreme Value Statistics (EVS) concepts to compute a prospective maximum defect size were used for fatigue life prediction. Based on the failure analysis, for tensile failure dominated specimens Maximum Principle Stress (MPS) and for shear failure dominated specimens Fatemi-Socie (FS) parameter were used to calculate the stress intensity factor range for defect-based uniaxial and multiaxial fatigue life predictions. The fracture mechanics-based framework could successfully be used for fatigue life prediction of AM metals based on their defect content.

Data-Driven Modeling for Additive Manufacturing of Metals

Data-Driven Modeling for Additive Manufacturing of Metals
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Total Pages: 79
Release: 2019-10-09
Genre: Technology & Engineering
ISBN: 0309494230
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Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.