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Expert System for Online Defect Detection in Medical Devices Produced by Electron Beam Melting Using Layer-by-Layer Optical Images

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3D PRINTING AND ADDITIVE manufacturing 2025年第1期12卷 36-47页

作者： Bonatti, Amedeo Franco Meringolo, Francesco Domenico Tubertini, Ilaria Lavecchia, Carolina Eleonora Favaro, Alberto Vozzi, Giovanni De Maria, Carmelo Univ Pisa Res Ctr E Piaggio Pisa Italy Univ Pisa Dept Informat Engn Pisa Italy Rejoint Srl Castel Maggiore Italy Univ Pisa Res Ctr E Piaggio Largo L Lazzarino 1 I-56122 Pisa Italy Univ Pisa Dept Informat Engn Largo L Lazzarino 1 I-56122 Pisa Italy

The implementation of online, nondestructive quality control (QC) solutions is a key aspect to consider especially in medical device manufacturing, where the production process should comply to relevant quality-related standards to guarantee a safe product for the patient. For metallic implants fabricated using electron beam melting (EBM), the presence of defects (e.g., porosities, cracks, delamination, balling effect) could affect the final part quality in terms of structural integrity and mechanical properties. In this context, we propose an expert system algorithm capable of automated detection of porosities that could occur in prosthetic components, such as tibial trays, printed using a cobalt-chromium alloy. The choice of focusing only on the porosities was done as these defects are particularly critical for orthopedic prosthesis, where they can negatively impact the fatigue behavior of the printed component. The algorithm was developed ad hoc to analyze images of the manufacturing process taken from a camera embedded in the printer (Arcam Q10plus). Images can be used to identify porosities, performing a nondestructive evaluation that supports the process of part qualification. The developed algorithm automates and improves the visual inspection task conducted by human experts, including quantitative assessment on the size and location of the porosities, and reporting the presence of high porosity density areas. The defect detection performance was evaluated through the design of two tasks: The layer-wise defect detection and the large pore identification. The defect detection was performed with a sensitivity of 91% and a precision of 76%. Furthermore, a comparison with the gold-standard nondestructive evaluation technique, that is, computed tomography evaluation, allowed to validate the algorithm (percent agreement of 98%). The developed expert system allows to quickly evaluate an entire printing volume with several components representing a reliable and fast

关键词： electron beam melting defect detection artificial intelligence expert system quality control

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Review on additive manufacturing and non-destructive testing

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JOURNAL OF manufacturing SYSTEMS 2023年 66卷 260-286页

作者： Ramirez, Isaac Segovia Marquez, Fausto Pedro Garcia Papaelias, Mayorkinos Univ Castilla La Mancha Ingenium Res Grp Ciudad Real Spain Univ Birmingham Sch Met & Mat Birmingham England

Additive manufacturing is based on high-precision material deposition to build a final part or component by using various techniques. It is being one of the main advances in the fourth industrial revolution. This type of manufacturing is not new, although it is growing. There are many types of additive manufacturing techniques, and the use of efficient inspection methods to ensure a certain level of quality, and to detect faults, porosities, etc., are required in the industry. nondestructive Testing is widely applied, and particularly in additive manufacturing, to ensure efficient quality control and preventive/predictive maintenance without changing the characteristics and initial state of the material. Each nondestructive Testing technique is based on different physical principles;therefore, the selection and correct use of each technique depends on the application, the manufacturing process, the type of material and the possible discontinuities, among many others. This article develops a complete, exhaustive, and updated review and analysis of the state of the art of nondestructive Testing applied in additive manufacturing. The main characteristics of the processes are analyzed, highlighting the most relevant works and the challenges that each technique should face. An analysis of techniques necessary for the development of nondestructive evaluation has been carried out, mainly Machine Learning techniques used for the quantification, detection and analysis of defects detected by nondestructive Testing techniques.

关键词： Additive manufacturing 3D-print Non-destructive testing Non-destructive evaluation

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Quantitative nondestructive evaluation of Cold Spray Manufactured Aluminum Alloy 6061 and Copper Samples

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JOURNAL OF THERMAL SPRAY TECHNOLOGY 2024年第2-3期33卷 688-704页

作者： Kumar, Kishore Kumar Indu Patel, Mann Baijukumar Boese, Samuel Gouldstone, Andrew Champagne Jr, Victor K. Ozdemir, Ozan c. Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA Kostas Res Inst Burlington MA USA US Army Res Lab Aberdeen MD USA

Cold spray (CS) is a solid-state process for depositing thick layers of material via the successive high-velocity impact of powder particles onto a solid surface, which leads to high rates of deformation, interparticle bonding, and coating build-up. Although CS is finding commercial utilization in non-load-bearing repair and coating applications, clear nondestructive characterization procedures are necessary to realize its potential in load-bearing structural applications. In this study, the viability of electrical conductivity and through thickness ultrasound wave velocity measurement methods was studied to serve as a means for nondestructive quantitative measurement for quality control in CS and potentially other additive manufacturing (AM) methods. Eddy current, ultrasound, porosity, hardness, and uniaxial tensile strength tests were conducted on CS deposited layers of aluminum alloy 6061 and copper on aluminum alloy 6061 and commercially pure copper substrates, respectively. CS gas dynamic parameters were intentionally and systematically varied to result in corresponding discrete differences in mechanical properties of deposits. Ultrasound measurements of longitudinal wave velocity and eddy current electrical conductivity measurements showed good correlation with process conditions, microstructural characterization results, and destructive mechanical tests (hardness, tensile). The results of this work show that ultrasound wave velocity and electrical conductivity correlate well with increased particle impact velocity in CS deposited aluminum and copper blocks, which evidently show an incremental decrease in porosity, increase in hardness, and increase in tensile strength. The outlined ultrasound and eddy current nondestructive testing methods present effective means for quantitative assessment of cold spray deposited structures while intact with the substrate.

关键词： cold spray eddy current electrical conductivity nondestructive testing quantitative ultrasound

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Semi-supervised learning for steel surface inspection using magnetic flux leakage signal

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JOURNAL OF INTELLIGENT manufacturing 2025年第2期36卷 1021-1031页

作者： Park, Jae-Eun Kim, Young-Keun Handong Global Univ Dept Mech & Control Engn Pohang 37554 South Korea

This paper proposes a semi-supervised learning model for detecting multi-defect classification and localization on the steel surface for industries with limited labeled datasets. This study uses 1-D data from magnetic flux leakage (MFL) testing, a powerful and cost-effective nondestructive inspection method for steel bars. Most steel surface defect systems are based on supervised learning classification with 2-D image datasets. However, acquiring labeled datasets for developing supervised learning models is practically limited in the actual steel manufacturing process. Furthermore, due to the frequent occurrence of multiple defect classes on the same steel bar, the problem of multi-defect classification and localization needs to be addressed. Therefore, this paper proposes a steel bar surface inspection system for multi-defect classification and localization based on a semi-supervised learning model and MFL signals. The proposed system solves the multi-defect classification and localization problem by reducing the feature dimension with an autoencoder. Then, it classifies the defects based on the semi-supervised support vector machines that require only a small portion of the labeled dataset. Also, the classification process is repeated on the overlapped small steel section to address the multi-defect classification and localization issue. When it was evaluated on an industry MFL inspection dataset, the accuracy ranged from 81% to 90% when the labeled data ratio varied from 2% to 90%.

关键词： Steel surface inspection Magnetic flux leakage (MFL) Semi-supervised learning Dimensionality reduction Autoencoder Semi-supervised support vector machine (S3VM)

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Special Issue on Recent Advanced manufacturing Science and Technology

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INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY 2024年第4期18卷 461-462页

作者： Matsumura, Takashi Suzuki, Norikazu Tokyo Denki Univ Adachi Ku Tokyo Japan Chuo Univ Bunkyo Ku Tokyo Japan

Recently, manufacturing technologies have progressed owing to high industrial demand. For example, in the automobile and aircraft industries, manufacturing processes require technologies that allow for high machining rates of lightweight and/or difficult-to-cut materials. Fabricating medical equipment involves the machining of biocompatible materials with high mechanical strength. Information devices require high-quality ultraprecision manufacturing processes. Furthermore, measurement and characterization technologies are also essential for manufacturing. Along with the evolution of manufacturing technologies, scientific studies have been performed on manufacturing phenomena and process control based on physical and/or mathematical *** special issue was promoted by the International conference on Leading Edge manufacturing/Materials & processing (LEM&P2023) held from June 12, 2023 to June 16, 2023 at Rutgers University in New Brunswick, sponsored by the Japan Society of Mechanical Engineers. This conference was co-located with the manufacturing Science Engineering conference (MSEC), ASME, and North American manufacturing Research conference (NAMRC), *** special issue includes nine papers that describe the innovations and detailed progress in the following areas:- Characterization of materials- Fundamental study and modeling of material removal process- manufacturing control and optimization- manufacturing processes for new hard materials- Micro-/Nano-scale manufacturing- Tool manufacturing and performance- Metrology and evaluation- Surface characterizationThis special issue includes technical and scientific discussions that suggest new key technologies for future manufacturing. We hope that this will help readers understand manufacturing processes and improve their *** thank the authors and reviewers for their generous cooperation and the editing staff for their contributions.

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DIGITAL TWIN DEVELOPMENT FOR ADDITIVE manufacturing

DIGITAL TWIN DEVELOPMENT FOR ADDITIVE MANUFACTURING

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2024 Pressure Vessels and Piping conference

作者： Park, Y. H. Love, A. Behseresht, S. Pastrana, O. Valdez Sakai, J. Univ New Mexico State Univ Mech & Aerosp Engn Dept Las Cruces NM 88003 USA

ISBN: (纸本)9780791888476

Additive manufacturing (AM) is considered as a transformative manufacturing technology due to its ability to create complex structures and reduce material waste. However, the AM technology also introduces many challenges such as poor surface quality and structural integrity. To address these issues, a novel approach known as a digital twin (DT) has emerged. A digital twin is a digital representation of a physical object or system that enables real-time monitoring and control of their behavior. This study introduces a hybrid physicalvirtual digital twin environment that combines a sensing-driven DT with a process simulation-driven DT. Specifically, we propose a hybrid digital twin environment (HDTE) customized for a fused deposition modeling (FDM) 3D printer. The HDTE is implemented into a 3D development platform. The present DT environment integrates external sensors to collect real-time data on temperature and location of the extruder on a 3D printer. It also includes a virtual AM process simulation using a commercial finite element software to predict temperature distribution and mechanical behavior during 3D printing. Position and temperature assessment experiments confirm the DT's ability to replicate component positions under static and dynamic conditions as well as temperature variation within the 3D printer. Comparative analysis between the simulation and experimental data shows excellent agreement and further validate the DT's capabilities. The present DT holds the potential for a significant advancement in 3D printing technology by offering real-time insights, predictive capabilities, and improved process control.

关键词： Additive manufacturing Thermo-mechanical analysis Damage FEA Johnson-Cook

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APPLICABILITY OF NDT METHODS FOR THE DETECTION OF TYPICAL DEFECTS IN SELECTIVE LASER MELTING PARTS 32

APPLICABILITY OF NDT METHODS FOR THE DETECTION OF TYPICAL DE...

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32nd International conference on Metallurgy and Materials (METAL)

作者： Moskovchenko, Alexey Svantner, Michal Houdkova, Sarka Univ West Bohemia New Technol Res Ctr Plzen Czech Republic

ISBN: (纸本)9788088365129

nondestructive testing is an important part of any production process that is required to ensure the quality of materials and parts. The development of reliable nondestructive testing (NDT) methods applicable to novel materials and manufacturing processes such as additive manufacturing is a challenging task because of the complex geometry and anisotropic material properties. The majority of review publications related to the NDT methods in the additive manufacturing process are focused on only one aspect: either the defect classification or NDT methods and quality control systems. In this study, various NDT methods such as X-ray testing, acoustic emission, IR thermography, etc. were analyzed from the point of view of an application for selective laser melting (SLM, one of the most common additive manufacturing processes of metal parts). Typical defects appearing in SLM parts were classified and matched with NDT methods able to detect them. Recommendations on the use of different methods for in-situ process monitoring and printed parts inspection are given.

关键词： Selective laser melting nondestructive testing defects additive manufacturing

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In-line monitoring of the Fused Filament Fabrication additive manufacturing process 17

In-line monitoring of the Fused Filament Fabrication additiv...

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conference on nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII

作者： Forster, R. Feteira, A. Soulioti, D. Grammatikos, S. Kordatos, E. Sheffield Hallam Univ Mat & Engn Res Inst Howard St Sheffield S1 1WB England Norwegian Univ Sci & Technol ASEMlab Lab Adv & Sustainable Engn Mat Dept Mfg & Civil Engn N-2815 Gjovik Norway

ISBN: (纸本)9781510660816;9781510660823

In the present work, a novel combination method of in-line monitoring and offline non-destructive evaluation was developed for the detection and monitoring of defects in additively manufactured specimen. The new methodology includes Infrared Thermography, Acoustic Emission and Micro-computerised Tomography to allow for the detection of anomalies during the printing process and the verification of their presence after the printing process without the need for destructive testing. It was found that the in-line monitoring can provide information on the efficacy of the printing process which is substantiated by the offline assessment.

关键词： In-line monitoring additive manufacturing non-destructive evaluation infrared thermography acoustic emission

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REAL-TIME AI-DRIVEN INTERPRETATION OF ULTRASONIC DATA FROM RESISTANCE SPOT WELD process MONITORING FOR ADAPTIVE WELDING

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MATERIALS evaluation 2023年第7期81卷 61-70页

作者： Scott, Ryan Stocco, Danilo Chertov, Andriy Maev, Roman G. R. Univ Windsor Inst Diagnost Imaging Res Windsor ON N9B 3P4 Canada Tessonics Inc Windsor ON N9A 4J3 Canada

Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.

关键词： resistance spot welding ultrasound artificial intelligence deep learning NDE 4.0

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Non-Destructive Measurement of Bottom Width in Deep Trench Isolation Structures using IRCD Metrology 38

Non-Destructive Measurement of Bottom Width in Deep Trench I...

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conference on Metrology, Inspection, and process control XXXVIII

作者： Keller, Nick Poulingue, Marc Grynko, Ross Ribaudo, Troy Antonelli, G. Andrew Li, Victor Ravasio, Marcello Le Cunff, Delphine Onto Innovat Inc Wilmington MA 01887 USA ST Micro Elect Geneva Switzerland

ISBN: (纸本)9781510672178;9781510672161

As scaling in semiconductor devices continues, the aspect ratios of deep trench isolation (DTI) structures have increased. DTI structures are used in power devices, power management ICs and image sensors as a method to isolate active devices by reducing crosstalk, parasitic capacitance, latch-up and allowing for an increase breakdown voltage of active devices. Measurement of these structures in high volume manufacturing (HVM), with non-destructive technology, has mostly been limited to the depth and top width of the DTI structure, while the bottom width (BCD) has not been able to be reliably measured. Here we present two different optical metrologies, "conventional" OCD and IRCD, that operate in the UV-VIS-NIR and MIR region of the electromagnetic spectrum, respectively, and discuss the measurability of DTI sidewall profile, bottom width, and depth in BCD (Bipolar CMOS DMOS) power management IC devices for each method at various pitches and line to space ratios. Experimental data will be presented showing sensitivity and discrimination of IRCD to a DOE specifically on the bottom width for three different structures.

关键词： Simulations Semiconducting wafers Metrology Etching nondestructive evaluation process control Scatterometry

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