In-Line Height Measurement Technique for Directed Energy Deposition Processes

Borovkov, Herman; Yedra, Aitor García de la; Zurutuza, Xabier; Angulo, Xabier; Álvarez, Pedro; Pereira, Juan Carlos; Cortés, Fernando

doi:10.3390/jmmp5030085

Cited by 17 publications

(8 citation statements)

References 33 publications

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“…Machine-vision sensing is a cost-effective and reliable method for obtaining multiple information through image processing, as opposed to expensive IR cameras [193,194]. It has been reported that results obtained from digital cameras only deviate by a mere 5% from the precise results obtained through an optical 3D scanner [193], making it a cost-effective options for layer height control [195][196][197]. Ultimately, the advantages of using machinevision sensing outweigh its alternative, laser-based scanning techniques, with the later further discussed in the following section.…”

Section: Vision-based Signalsmentioning

confidence: 99%

Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

Imran,

Che Idris,

De Silva

et al. 2024

Technologies

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This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.

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Section: Vision-based Signalsmentioning

confidence: 99%

Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

Imran,

Che Idris,

De Silva

et al. 2024

Technologies

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“…In this study, the focus is to monitor the geometrical accuracy of the build parts; to detect normal, under-and overdeposition. There are two monitoring approaches of obtaining geometrical process signatures [16,36]: laser scanning-based [37][38][39][40][41][42] and machine vision-based [43][44][45][46][47][48], each with their strengths and weaknesses. Laser scanning-based monitoring system has been used to obtain more precise depth information on fabricated parts, i.e.…”

Section: B Geometrical Sensingmentioning

confidence: 99%

In-Situ Process Monitoring and Defects Detection Based on Geometrical Topography With Streaming Point Cloud Processing in Directed Energy Deposition

Imran,

Kim,

Jung

et al. 2023

IEEE Access

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The 3D printing industry faces challenges in ensuring reliable and repeatable processes, as increasing slopes can lead to defects and faults forming layer by layer in fabricated parts. Detecting these defects through post-processing quality inspections is time-consuming and laborious. To address this, a new method is proposed that incorporates a scoring scheme to quantitatively evaluate process performance using clad height data during printing. This approach aims to save time and cost while preserving the structural integrity of the part. The study develops a layer-wise point cloud processing technique to convert the incoming unordered data streams into rasterized points. By transforming raw signals into spatially equidistant points representing clad height in a 2-D Cartesian plane, a heatmap tomography of each layer is generated. Subsequently, a novel Defects-Finder algorithm is developed to locate and cluster surface defects based on the assigned scores. The findings demonstrate the algorithm's ability to identify the root cause of propagated faults, which can result in severe defects. Additionally, the study employs various statistical measures in the layer-level analysis to evaluate miniature process faults or shifts, which may get overshadowed, including cases of under and over-deposition. Through comparison and validation with physical artifacts, the proposed method proves effective in identifying and assessing process faults. Ultimately, this method enhances big data visualization for cost-effective quality control and boosts the overall productivity of additive manufacturing processes. By streamlining defect detection and performance evaluation, it addresses the challenges faced by the industry in ensuring reliable 3D printing outcomes.

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“…Among the several outputs that need to be monitored and controlled to ensure the quality of the process/component, standoff distance and layer height are very important. In fact, a wrong standoff distance can lead to height deviations from the ideal building path [6].…”

Section: Introductionmentioning

confidence: 99%

In process monitoring of geometrical characteristics in laser metal deposition: A comparative study

Latte

2023

Materials Research Proceedings

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Abstract. Although it is experiencing a wide diffusion, there are still several limits to the use of additive manufacturing (AM) for the fabrication of metal components. This includes low productivity, poor dimensional accuracy and uncertainty about the mechanical properties of the final parts. The main cause of these undesirable effects lies in the intrinsic complexity of the metal AM processes, such as Laser Metal Deposition (LMD). Accurate monitoring of the process and optimization of the process parameters are therefore of fundamental importance to ensure the overall quality of the product. Nowadays, several optical methods are under development for the monitoring of geometric characteristics and their correlation with specific process parameters, such as the standoff distance. This paper presents a comparative study between two optical in-process monitoring methods performed on AISI 316L multilayer samples produced by the LMD process. The first is a laser line section method based on a laser diode mounted on the deposition head, while the second method uses a high-resolution CMOS camera placed on the horizontal plane with a front view of the sample. In both cases, ad-hoc image processing algorithms were used to process the data, reconstruct the morphology of the component, and extract geometrical information. Results were then validated using an offline scanning system and micrographic analyses. Both proposed systems allowed the monitoring of the deposition quality using appropriate Key Performance Indicators (KPIs). The study showed a good capability of the prototype systems to detect deposition defects due to undesired variations of the process conditions and to monitor the standoff distance.

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In-Line Height Measurement Technique for Directed Energy Deposition Processes

Cited by 17 publications

References 33 publications

Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

In-Situ Process Monitoring and Defects Detection Based on Geometrical Topography With Streaming Point Cloud Processing in Directed Energy Deposition

In process monitoring of geometrical characteristics in laser metal deposition: A comparative study

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