Simultaneous Compositional and Grain Size Measurements Using Laser Opto-Ultrasonic Dual Detection for Additive Manufacturing

Accurate internal cavities and kissing bond sizing in metal plates by using the time-of-flight of laser-induced ultrasound waves

This paper presents a nondestructive method for accurately identifying internal flaws in metal plates, which is crucial for ensuring structural integrity in safety-critical applications. The technique relies on analyzing laser-induced ultrasound (LIU) longitudinal wave time-of-flight, as demonstrated through a theoretical five-layer model. Experimental validation was conducted using a piezo-sensor in contact with a slab containing millimetric artificial cavities immersed in air, resulting in a discrepancy of 5.05%. In contrast, experiments performed in a water medium exhibited a lower discrepancy of 2.5%. (Discrepancy refers to differences between measurements obtained through an experimental time-of-flight analysis and caliper measurements.) The results obtained in water-based experiments affirm the accuracy of the proposed model. B-scan measurements and the five-layer model were utilized to generate 2D reconstructed images, enabling precise localization and sizing of cavities and kissing bonds between plates, finding an average size of kissing bond of 30 µm. In conclusion, the proposed five-layer model, based on a longitudinal wave time-of-flight analysis, provides a straightforward framework for an easy cavity and kissing bond measurements in metal plates.

A study of the spectral signal effect of self-holes in metal additive manufacturing components using laser-induced breakdown spectroscopy (LIBS)

Understanding the detection mechanism of hole defects in metal additive manufacturing (AM) components is of great significance for the detection of metal AM component defects using laser-induced breakdown spectroscopy (LIBS). In this work, the mapping relationship between the hole defects of metal AM components and the LIBS spectral signal was studied using the controlled variable method. The effect of hole defects mostly showed a suppression effect and peaked at a hole depth of 1.0 mm when the LIBS system was at its optimal excitation parameter. To explore the possible reasons behind the inhibitory effect of self-holes, the variation law of the plasma temperature with and without hole defects was further investigated. Our results showed that the plasma temperature change curve was similar to the spectral line intensity change trend. Finally, the linear relationship between the focal length effect and the hole effect, and the relationship between the constraint effect and the hole effect were studied. The minimum fitting R2 between the constraint effect and the hole effect was 0.979. We believed that the inhibition of the hole effect was mainly caused by the absorption and loss of energy in the plasma during the process of plasma radiation and shock wave reflection from the hole wall. By studying the detection mechanism of hole defects in metal additive manufacturing components excited by LIBS and finding the effective characteristics of hole defects in metal AM components, it is helpful to achieve higher precision and higher sensitivity defect detection.

A Review of Non-Destructive Testing (NDT) Techniques for Defect Detection: Application to Fusion Welding and Future Wire Arc Additive Manufacturing Processes

In Wire and Arc Additive Manufacturing (WAAM) and fusion welding, various defects such as porosity, cracks, deformation and lack of fusion can occur during the fabrication process. These have a strong impact on the mechanical properties and can also lead to failure of the manufactured parts during service. These defects can be recognized using non-destructive testing (NDT) methods so that the examined workpiece is not harmed. This paper provides a comprehensive overview of various NDT techniques for WAAM and fusion welding, including laser-ultrasonic, acoustic emission with an airborne optical microphone, optical emission spectroscopy, laser-induced breakdown spectroscopy, laser opto-ultrasonic dual detection, thermography and also in-process defect detection via weld current monitoring with an oscilloscope. In addition, the novel research conducted, its operating principle and the equipment required to perform these techniques are presented. The minimum defect size that can be identified via NDT methods has been obtained from previous academic research or from tests carried out by companies. The use of these techniques in WAAM and fusion welding applications makes it possible to detect defects and to take a step towards the production of high-quality final components.

Laser ultrasonic improvement and its application in defect detection based on the composite coating method

Herein, we studied the increasing tendency of photoacoustic (PA) conversion efficiency of the Au/polydimethylsiloxane (PDMS) composite. The thickness of the Au layer was optimized by modeling the PA process based on the Drude-Lorentz model and finite element analysis method, and corresponding results were verified. The results showed that the optimal Au thickness of the Au/PDMS composite was 35 nm. Finally, the Au/PDMS composites were coated onto the surface of aluminum alloys, which improved the thermoelastic laser ultrasonic (LU) signals to near 100 times. Besides, the defect mapping was performed by thermoelastic LU signals with Au/PDMS coating and ablation LU signals without coating; the Pearson correlation coefficient was higher than 0.95. The application in the defect detection in metal could provide guides for nondestructive detection on metals by laser ultrasound.

Measuring the Depth of Subsurface Defects in Additive Manufacturing Components by Laser-Generated Ultrasound

A new method to measure the depth of subsurface defects in additive manufacturing components is proposed based on the velocity dispersion analysis of Lamb waves by the wavelet-transform of laser ultrasound. Firstly, the mode-conversion from laser-generated surface waves to Lamb waves caused by subsurface defects at different depths is studied systematically. Secondly, an additive manufactured 316L stainless steel sample with six subsurface defects has been fabricated to validate the efficiency of the proposed method. The measured result of the defect depth is very close to the real designed value, with a fitting coefficient of 0.98. The defect depth range for high accuracy measurement is suggested to be lower than 0.8 mm, which is enough to meet the inspection of layer thickness during additive manufacturing. The result indicates that the proposed method based on laser-generated ultrasound (LGU) velocity dispersion analysis is robust and reliable for defect depth measurement and meaningful to improve the processing quality and processing efficiency of additive/subtractive hybrid manufacturing.

Review of Aluminum Alloy Development for Wire Arc Additive Manufacturing

Processing of aluminum alloys by wire arc additive manufacturing (WAAM) gained significant attention from industry and academia in the last decade. With the possibility to create large and relatively complex parts at low investment and operational expenses, WAAM is well-suited for implementation in a range of industries. The process nature involves fusion melting of a feedstock wire by an electric arc where metal droplets are strategically deposited in a layer-by-layer fashion to create the final shape. The inherent fusion and solidification characteristics in WAAM are governing several aspects of the final material, herein process-related defects such as porosity and cracking, microstructure, properties, and performance. Coupled to all mentioned aspects is the alloy composition, which at present is highly restricted for WAAM of aluminum but received considerable attention in later years. This review article describes common quality issues related to WAAM of aluminum, i.e., porosity, residual stresses, and cracking. Measures to combat these challenges are further outlined, with special attention to the alloy composition. The state-of-the-art of aluminum alloy selection and measures to further enhance the performance of aluminum WAAM materials are presented. Strategies for further development of new alloys are discussed, with attention on the importance of reducing crack susceptibility and grain refinement.

The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review

Wire arc additive manufacturing is currently rising as the main focus of research groups around the world. This is directly visible in the huge number of new papers published in recent years concerning a lot of different topics. This review is intended to give a proper summary of the international state of research in the area of wire arc additive manufacturing. The addressed topics in this review include but are not limited to materials (e.g., steels, aluminum, copper and titanium), the processes and methods of WAAM, process surveillance and the path planning and modeling of WAAM. The consolidation of the findings of various authors into a unified picture is a core aspect of this review. Furthermore, it intends to identify areas in which work is missing and how different topics can be synergetically combined. A critical evaluation of the presented research with a focus on commonly known mechanisms in welding research and without a focus on additive manufacturing will complete the review.

Laser ultrasonic imaging for defect detection on metal additive manufacturing components with rough surfaces

Defects or discontinuities are inevitable during the melting and consolidation process of metal additive manufacturing. Online inspection of microdefects during the processing of layer-by-layer fusion is urgently needed for quality control. In this study, the laser ultrasonic C-scan imaging system is established to detect the surface defects of selective laser melting (SLM) samples that have a different surface roughness. An autosizing method based on the maximum correlation coefficient and lag time is proposed to accurately measure the defect length. The influences of the surface roughness on the laser ultrasound signal-to-noise ratio distribution and defect sizing accuracy are also studied. The results indicate that the proposed system can detect notches with a depth of 50 µm and holes with a diameter of 50 µm, comparable in size to raw powder particles. The average error for the length measurement can reach 1.5% if the notch is larger than 2 mm. Meanwhile, the sizing error of a 1 mm length notch is about 9%. In addition, there is no need to remove the rough surface of the as-built SLM samples during the detection process. Hence, we propose that the laser ultrasonic imaging system is a potential method for online inspection of metal additive manufacturing.

Investigation of the mechanism and influence of laser wavelength and energy on laser opto-ultrasonic dual detection

Laser opto-ultrasonic dual (LOUD) detection, which uses laser irradiation of samples to generate spectral and ultrasonic signals simultaneously, can perform multimodal detection of element composition and structural property. As such, it has been applied to the detection of additive manufacturing (AM) components. Further, optimized parameters lead to better detection results. To the best of our knowledge, however, there is no study on the effect of laser properties on LOUD detection. Therefore, we studied the mechanism and influence of laser wavelength and energy on LOUD detection. In this work, the intensity, signal-to-noise ratio (SNR), and stability evolution of the laser excitation spectrum and ultrasonic signals at different wavelengths and energies were analyzed. It was found in the plasma evolution that high electron number density means a large amount of ablated mass generated, which was favorable for laser ultrasonic excitation and can produce higher SNR and a more stable signal. However, it also led to more atoms of the ground-state, which resulted in the self-absorption effect and reduced spectrum intensity in the spectrum analysis. Therefore, with self-absorption correction, better stability, and higher signal intensity, an SNR of spectral and ultrasonic signals can be obtained using 355 nm laser excitation at optimal energy. As a result, in the quantitative analysis of Cu and Si elements by LOUD detection, the determination coefficients (R²) were higher than 0.995, and the average relative errors were less than 2.5%, the limit of detection could reach the order of 100 ppm. Further, the defect size of 0.55 mm in the wire +arc additive manufacturing sample was detected by LOUD detection, and the average relative error was 5.59% compared with the digital radiography results, which indicate that laser wavelength and laser energy affect the intensity and stability of spectral and ultrasonic signals in LOUD detection, which means selecting appropriate laser parameters is important to obtain a high precision detection.

Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy

There are two main challenges in the diagnosis of blood cancer. The first is to diagnose cancer from healthy control, and the second is to identify the types of blood cancer. The chemometrics method combined with laser-induced breakdown spectroscopy (LIBS) can be used for cancer detection. However, chemometrics methods were easily influenced by the spectral feature redundancy and noise, resulting in low accuracy rate because of their simple structure. We proposed an approach using LIBS combined with the ensemble learning based on the random subspace method (RSM). The serum samples were dripped onto a boric acid substrate for LIBS spectrum collection. The complete blood cancer sample set include leukemia [acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML)], multiple myeloma (MM), and lymphoma. The results showed that the accuracy rates using k nearest neighbors (kNN) and linear discriminant analysis (LDA) only were 88.14% and 94.45%, respectively, while using RSM with LDA (RSM-LDA), the average accuracy rate was improved from 94.45% to 98.34%. Furthermore, the variable importance of spectral lines (Na, K, Mg, Ca, H, O, N, C-N) were evaluated by the RSM-LDA model, which can improve the recognition ability of blood cancer types. Comparing the RSM-LDA model and only with LDA, the results showed that the average accuracy rate for cancer type identification was improved from 80.4% to 91.0%. These results demonstrate that LIBS combined with the RSM-LDA model can discriminate the blood cancer from the health control, as well as the recognition the types for blood cancers.

Simultaneous Compositional and Grain Size Measurements Using Laser Opto-Ultrasonic Dual Detection for Additive Manufacturing

Metal-based additive manufacturing (AM) is a disruptive technique with great potential across multiple industries; however, its manufacturing quality is unstable, leading to an urgent requirement for component properties detection. The distribution of grain size has an important effect on many mechanical properties in AM, while the distribution of added elements, such as titanium (Ti), has a measurable effect on the grain size of an aluminum (Al) alloy. Therefore, the detection of the distributions of grain size and elements is of great significance for AM. In this study, we investigated the distribution of grain size and elements simultaneously for wire + arc additive manufacturing (WAAM) with an Al alloy using laser opto-ultrasonic dual (LOUD) detection. The average grain size obtained from the acoustic attenuation of ultrasonic signals was consistent with the results of electron backscatter diffraction (EBSD), with a coefficient of determination (R²) of 0.981 for linear fitting. The Ti element distribution obtained from optical spectra showed that the enrichment of Ti corresponded to the grain refinement area in the detected area. The X-ray diffraction (XRD) spectra showed that the spectral peaks were moved from Al to AlTi and Al₂Ti forms in the Ti-rich areas, which confirmed the LOUD results. The results indicated that LOUD detection holds promise for becoming an effective method of analyzing the mechanical and chemical properties of components simultaneously, which could help explain the complex physical and chemical changes in AM and ultimately improve the manufacturing quality.

Simultaneous Compositional and Grain Size Measurements Using Laser Opto-Ultrasonic Dual Detection for Additive Manufacturing

Metal-based additive manufacturing (AM) is a disruptive technique with great potential across multiple industries; however, its manufacturing quality is unstable, leading to an urgent requirement for component properties detection. The distribution of grain size has an important effect on many mechanical properties in AM, while the distribution of added elements, such as titanium (Ti), has a measurable effect on the grain size of an aluminum (Al) alloy. Therefore, the detection of the distributions of grain size and elements is of great significance for AM. In this study, we investigated the distribution of grain size and elements simultaneously for wire + arc additive manufacturing (WAAM) with an Al alloy using laser opto-ultrasonic dual (LOUD) detection. The average grain size obtained from the acoustic attenuation of ultrasonic signals was consistent with the results of electron backscatter diffraction (EBSD), with a coefficient of determination (R²) of 0.981 for linear fitting. The Ti element distribution obtained from optical spectra showed that the enrichment of Ti corresponded to the grain refinement area in the detected area. The X-ray diffraction (XRD) spectra showed that the spectral peaks were moved from Al to AlTi and Al₂Ti forms in the Ti-rich areas, which confirmed the LOUD results. The results indicated that LOUD detection holds promise for becoming an effective method of analyzing the mechanical and chemical properties of components simultaneously, which could help explain the complex physical and chemical changes in AM and ultimately improve the manufacturing quality.