A review on non-destructive evaluation and characterization of additively manufactured components

Sreeraj, P.; Mishra, Surendra Kumar; Singh, Pawandeep

doi:10.1007/s40964-021-00227-w

Cited by 13 publications

(6 citation statements)

References 154 publications

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“…Some defects form in subsurface layers. Their identification requires careful analysis and specific inspection techniques [26]. This would be the case for circular pores generated by entrapped gas, and other irregularly shaped voids such as cracks due at least in part to shrinkage during solidification (i.e., in casting or AM), lack of fusion, or improper forming.…”

Section: Sub-surface Porosity Defectsmentioning

confidence: 99%

Additive Manufacturing of Metal Load‐Bearing Implants 2: Surface Modification and Clinical Challenges

Zanetti,

Fragomeni,

Sanguedolce

et al. 2024

Chemie Ingenieur Technik

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Additive manufacturing (AM) permits sustainable production of personalized load‐bearing metal implants with complex structures. Regulations prescribe that implants have to meet strict requirements to not harm patients, and production technique should allow the certification of their performance. Process, materials, operating parameters, and customization to patient's needs could limit AM. Layer‐by‐layer material deposition and repeated thermal cycles may make outer surface of AM implants chemically and physically uneven and rough, eliciting biological response of host tissue and hindering therapeutic success. In this paper, we discuss the clinical challenges that AM must overcome to replace traditional techniques in implant and prostheses design.

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Section: Sub-surface Porosity Defectsmentioning

confidence: 99%

Additive Manufacturing of Metal Load‐Bearing Implants 2: Surface Modification and Clinical Challenges

Zanetti,

Fragomeni,

Sanguedolce

et al. 2024

Chemie Ingenieur Technik

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“…Similar to the measured wave velocities, the frequency of the resonance peak can also be divided into three levels. According to equation (10), for the same thickness, a lower resonance frequency in the resonance spectrum corresponds to a slower transverse wave velocity. Therefore, the differences in wave velocities among the transverse waves in different AM components were further confirmed.…”

Section: Characterization Of Anisotropymentioning

confidence: 99%

“…To achieve maximum strength, it becomes crucial to identify the optimal orientation of the principal direction under complex stress conditions. Therefore, there is a need to develop an effective method for characterizing anisotropy and evaluating the principal direction of SLM components [9,10]. Traditional methods for anisotropy characterization include metallographic analysis [11], x-ray diffraction (XRD) analysis [12], and electron backscatter diffraction (EBSD) analysis [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Non-contact characterization of material anisotropy of additive manufacturing components by electromagnetic acoustic resonance technique

Yuan,

Li,

Deng

2023

Meas. Sci. Technol.

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The degree of material anisotropy in additive manufacturing (AM) components is greatly influenced by the AM process parameters and machine setup. It is crucial to develop an effective technique for evaluating the material anisotropy in AM components to optimize AM process parameters and component design. This paper proposed a non-contact ultrasonic characterization method using the electromagnetic acoustic resonance (EMAR) technique to characterize the anisotropy of AM components. Various electromagnetic acoustic transducers (EMATs) were designed and utilized to characterize the material anisotropy and to determine the principal direction of the AM components. The degree of anisotropy in AM components was characterized using radial radiation EMATs. The relationship between the degree of anisotropy and the laser scanning angle was explored and further determined through the acoustic birefringence factor (ABF). Experimental results demonstrated that the anisotropy of AM components is intricately associated with the laser scanning angle, and specific angles can render the AM components isotropic. Moreover, understanding the principal directions is of significance for structural design and analyzing stress distribution in anisotropic components. Therefore, the principal directions of AM components were obtained by rotating the linear polarization EMAT. Changes in the resonance spectrum captured by the linear polarization EMAT while evaluating of principal directions were clearly illustrated, despite negligible alterations in linear ultrasonic features. Metallographic diagrams further validated the experimental findings. This investigation presented a highly accurate and reliable alternative for characterizing the anisotropy of AM components.

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“…Chen et al [9] asserted that extra effort is required to develop AM standards, that is, quality assurance. Furthermore, the evaluation process, either via nondestructive methods (e.g., X-ray, ultrasonic, callipers, machine vision, and roughness testing) or destructive methods (e.g., cutting into smaller samples) [10], can be costly and time-consuming. Once evaluated, the parts may need to be sent for further post-processing to improve their surface finish and mechanical properties [11].…”

Section: Introductionmentioning

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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A review on non-destructive evaluation and characterization of additively manufactured components

Cited by 13 publications

References 154 publications

Additive Manufacturing of Metal Load‐Bearing Implants 2: Surface Modification and Clinical Challenges

Additive Manufacturing of Metal Load‐Bearing Implants 2: Surface Modification and Clinical Challenges

Non-contact characterization of material anisotropy of additive manufacturing components by electromagnetic acoustic resonance technique

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

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