Pool Penetration Research Articles

Penetration state recognition for tungsten inert gas welding via an alternating cusp-shaped magnetic field-assisted molten pool-oscillation

To ensure optimal penetration in direct current (DC) argon tungsten arc welding, a method was proposed to recognize the molten pool penetration during molten pool oscillation under alternating cusp-shaped magnetic field (ACSMF). An arc discharge model is established to analyze the oscillation mechanism of the molten pool under ACSMF. The periodic variation of arc shape induces the periodic oscillation to establish a mapping relationship with the fluctuation of arc voltage waveform. At the excitation current of 3 A and frequency of 10 Hz, the arc voltage waveform undergoes abrupt changes at 1.26 V and 1.37 V, corresponding to the critical and the full penetration states, respectively. The errors in identifying moving penetration are 0.8 % and 0.7 %, with a full penetration weld, demonstrating a penetration rate of 100 %, indicating effective penetration recognition. Arc sensing technology for tungsten inert gas (TIG) welding under ACSMF is poised for gradual application in single-side welding and double-side forming of medium and thick plates. It is anticipated to evolve towards real-time identification technology for molten pool penetration monitoring.

Monitoring of the Weld Pool, Keyhole Morphology and Material Penetration State in Near-Infrared and Blue Composite Laser Welding of Magnesium Alloy

The laser welding of magnesium alloys presents challenges attributed to their low laser-absorbing efficiency, resulting in instabilities during the welding process and substandard welding quality. Furthermore, the complexity of signals during laser welding processes makes it difficult to accurately monitor the molten state of magnesium alloys. In this study, magnesium alloys were welded using near-infrared and blue lasers. By varying the power of the near-infrared laser, the energy absorption pattern of magnesium alloys toward the composite laser was investigated. The U-Net model was employed for the segmentation of welding images to accurately extract the features of the melt pool and keyhole. Subsequently, the penetrating states were predicted using the convolutional neural network (CNN), and the novel approach employing Local Binary Pattern (LBP) features + a backpropagation (BP) neural network was applied for comparison. The extracted images achieved MPA and MIoU values of 89.54% and 81.81%, and the prediction accuracy of the model can reach up to 100%. The applicability of the two monitoring approaches in different scenarios was discussed, providing guidance for the quality of magnesium welding.

Wire arc additive manufacturing method for Ti–6Al–4V alloy to improve the grain refinement efficiency and mechanical properties

Wire and arc additive manufacturing (WAAM) technique has introduced a novel approach for producing complex Ti–6Al–4V parts with metric dimensions. However, the produced part leads to the development of a strong texture and anisotropic mechanical properties due to the formation of large columnar β grains. To resolve this issue, the plastic deformation of each deposited track through hammer peening was developed as a means to refine these large β grains. In this study, we have investigated an innovative approach to further enhance the efficiency of β grain refinement by minimizing the arc heat input associated with previous deposited layer, which is achieved by employing a C-type filler wire. Our findings reveal a notable enhancement in grain refinement efficiency through the utilization of a C-type filler wire with peening process, as compared to available conventional commercial round shape filler wire. Specifically, the employment of the C-type filler wire results in a reduced melt pool penetration depth of WAAM Ti–6Al–4V (3.3 mm), compared to the commercially available round shape (R-Type) filler wire (4.48 mm). Within the plastically deformed region by peening, fine and randomly oriented β grains are observed, extending to a depth of deformation reaching 844 ± 32.65 μm. Peening WAAM Ti–6Al–4V with the C-type filler wire leads to the development of isotropic mechanical properties in both horizontal and vertical directions, offering high strength due to the presence of small equiaxed β grains and thin α laths (0.56 ± 0.18 μm), in contrast to the use of conventional commercial round shape filler wire.

Effect of laser focal point position on porosity and melt pool geometry in laser powder bed fusion additive manufacturing

In laser powder bed fusion (PBF-LB) additive manufacturing (AM), the laser beam is the fundamental tool used to selectively melt metal powder layer-upon-layer to form a 3-dimensional part. Studies on the effect of the laser scanning parameters (power, speed, hatch distance, and scanning strategy in general) on part quality are abundant; however, far less emphasis has been given to the effect of the laser beam and how it is focused on the laser-material interaction plane. Here, we have studied the effect of laser beam focal point position on porosity and melt pool geometry in PBF-LB AM. In addition, we also study how the various energy density parameters developed for laser melting processes correlate with melt pool dimensions in a situation where the laser beam focal point position (and the beam diameter and laser intensity change at work plane caused by it), is taken into consideration. Furthermore, we assess the possibility of using co-axial, photodiode-based melt pool monitoring signals as a means to monitor the thermal emissions of the process, and how it correlates with the resulting melt pool geometry. It was found that melt pool penetration experiences a major decrease when the focal point position is shifted by more than ±1 mm (or 30% of Rayleigh length), which could be considered as a tolerance limit for acceptable focus shift in PBF-LB machines. Focus shifts larger than this were effectively captured by the photodiode signals, indicating the potential of using such photodiode-based melt pool monitoring systems for continuous monitoring of focus shift in PBF-LB AM. Finally, it was shown that all the studied energy density parameters, except volumetric energy density, were able to capture the trend in normalized melt pool dimensions when focus position is introduced as a variable. A new energy density metric by normalizing the melt pool monitoring signal intensity with the beam area was introduced and shown to correlate with the normalized melt pool dimensions.

Monitoring Welding Torch Position and Posture Using Reversed Electrode Images – Part I: Establishment of the REI-TPA Model

Sensing, modeling, and control are the key technologies of intelligent welding manufacturing. The position and attitude of the welding torch relative to the weld seam affects the quality of the produced weld directly, which is the basis of control and offline programming of the welding robot. Based on the existing research of reversed electrode image (REI) on monitoring of weld pool surface and penetration state, this work focused on the application of REI on monitoring of welding torch position and attitude (TPA). In this paper, a REI-TPA model was established to quantitively relate REI to TPA by considering the surface of the weld pool as a spherical mirror. With the REI-TPA model, the offset distance and deflection angle of the welding torch relative to the correct position and attitude can be calculated. The calculation is based on the measurement of the relative distance between the tip of REI and the electrode in a passive visual image of the weld pool, arc length, and weld pool geometry. This model can be applied to the real-time control of TPA, which is a supplement to the application of welding passive visual image and an extension to multi-information acquisition and processing methods in the robotic welding process.

Multi-laser powder bed fusion of Ti6Al4V: Diode area melting utilizing low-power 450 nm diode lasers

Diode Area Melting (DAM) is an innovative approach to laser powder bed fusion. This research for the first time utilized multiple low wavelength (450 nm) and low power lasers (4 W each) to simultaneously process Ti6Al4V from a powder bed, resulting in high density samples (> 99%). The use of nine lasers perpendicular to the scanning direction allowed for the creation of melt pools with widths larger than 1000 µm, reducing the number of passes within a layer compared to traditional single laser LPBF systems. However, the increase in melt pool width creates challenges related to maintaining uniform melt pool penetration depths, this intern creates issues achieving high density in multi-layer parts. A parameter map based on normalized energy density was created. The research also investigated the effect of variable process conditions (scanning speed, hatch distance, and layer height), normalized energy density on densification, microstructure, and hardness. Implementing a rescanning strategy on each layer reduced average surface roughness and increased density while reducing hardness by approximately 40%. X-ray Diffraction analyses showed that the β phase occurred with increased energy density in as-built samples. Coarser lamellar structures were observed in the lower sections due to slower cooling rates, while finer, more martensite structures were observed towards the top, resulting in a basket-weave type microstructure throughout the sample. Furthermore, the lamellar sub-grains were visible without etching. This innovative technology shows that an array of low-power lasers can melt and fuse Ti6Al4V powder to create dense samples.

Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features

Despite the high efficiency of keyhole plasma arc welding (K-PAW), it still has several deficiencies, such as narrow welding parameter ranges, easily disturbed welding process and instability of welding quality, etc. It is a significant prerequisite to predict the keyhole/penetration status accurately for maintaining the welding process stability and improving the welding quality. Most researchers have focused on visual inspection techniques and convolutional neural networks (CNN), establishing a mathematical model of the correlation between weld pool images and penetration status. While CNN could extract the features of single weld pool images, it is difficult to predict the evolution trend of the incoming moments. In this paper, a novel model based on CNN and LSTM (long-short term memory) was developed to extract both the spatial and temporal features of topside weld pool images, and consequently the complex keyhole behaviors were predicted and described. The comparative study is carried out on different models, i.e. the single CNN model, the single LSTM model, and the CNN-LSTM model that integrates both spatial features of single images and temporal features of sequence. The keyhole initialization and establishing period, as the typical and critical welding scenario in full-penetration welding, is investigate and compared, resulting in a predicted value that is close to reality. Furthermore, ahead prediction of keyhole status was adopted, maintaining over 80% accuracy even when predicting keyhole behaviors 2 s into the future. Consequently, the unified CNN-LSTM model effectively improves the prediction accuracy of keyhole/penetration status, promising for intelligent K-PAW technology.

Research on mapping recognition of arc welding molten pool characterisation and penetration state based on embedded system

AbstractIn order to explore the mapping recognition of arc welding molten pool characterisation and penetration state, according to the idea of embedded system construction, this article adopts the idea of software and hardware co‐design to find the zero‐crossing point of the second derivative in welding image edge detection, and give a threshold. When the absolute value of the first‐order derivative exceeds the threshold and has a different sign with the first‐order derivative of the previous edge, it is judged as a valid edge. The welding current adopts a symmetrical pulsed AC square wave, and the proportion of heat flow input is high. At the base current, the arc light is darker, so a clear image is obtained. This article designs a simulation experiment to verify the effect of the embedded system in this article. From the experimental research, it can be known that the embedded system constructed in this article can play a certain role in the mapping recognition of the arc welding molten pool characterisation and penetration state.

Computational modeling of laser welding for aluminum–copper joints using a circular strategy

Dissimilar metal welding poses a significant challenge for researchers in the welding and materials science communities due to the different properties of the metals involved. In this study, we present a novel approach for determining the optimal processing parameters for continuous laser welding (CLW) of dissimilar metals, specifically aluminum and copper, using a circular strategy. The approach utilizes computational fluid dynamics (CFD) simulations to model the welding process and predict joint quality. We validated a CFD-based numerical model for predicting the melt-pool shape across a range of laser power and circular welding speed values. Additionally, we developed artificial neural network (ANN) models that can predict melt pool interface width, penetration depth, and copper concentration for any combination of processing conditions. Using surrogate modeling, we identified the optimal values of laser power and welding speed that result in the highest quality joint. Our results showed that the optimal processing conditions were a laser power of 2950W and a circular speed of 55 mm/s, which resulted in a high-quality Al–Cu joint with maximum shear strength of 2200 N. Microstructural analysis and tensile-shear strength testing confirmed that the optimal conditions resulted in a low degree of intermixing and the absence of large intermetallic compounds (IMCs) in the weld seam. Overall, study offers a valuable contribution to the welding and materials science communities by presenting a novel approach to address a common challenge in dissimilar metal welding. Our approach can be applied to other types of dissimilar metal welding and may inform the development of new welding techniques in the future.

Additive manufacturing process parameter determination for a new Fe-C-Cu alloy

As a potential replacement for stainless steel alloys commonly used to print parts with laser powder bed fusion, a new Cu precipitation strengthened ferrous alloy, with composition Fe-0.2C-6Cu (wt%), was recently developed. This material is Co- and Ni-free, printable, has mechanical properties comparable to that of high strength stainless steels (approximately 1300 MPa UTS), and is cost-advantaged relative to existing low-alloy steels for additive manufacturing of parts with complex shapes. To gain traction for broader application, optimal laser powder bed fusion (LPBF) processing conditions to produce nearly defect-free parts without compromising mechanical strength are needed. Here, an optimal processing window based upon laser speeds that achieve minimal porosity was quantified via comparisons of measured melt pool penetration, scan speeds, printed material density and laser beam energy density on a commercial LPBF unit operating at 350 W, 80 μm hatch spacing, and 50 μm layer thickness based upon optimal parameters for 17-4PH. The window is 300 to 500 mm/s to achieve >99.8 % dense Fe-0.2C-6Cu (wt%) specimens. Other defects such as burning, spatter, and lack of fusion were also avoided in this window. The window was validated with in-situ synchrotron X-ray imaging that enabled visualization of the vapor cavity, melting, and solidification during a single laser track scan on a miniature powder bed sample. In addition, in-situ infrared imaging provided temperature fields, cooling rate and solidification range, and confirmed minimal printing defects and spatter within the processing window.

Influence of heat source model on the behavior of laser cladding pool

The reflection of the laser energy by the powder makes the laser energy no longer exhibit a Gaussian distribution. The purpose of this paper is to investigate the effect of the heat source model on the behavior of the molten pool in laser cladding. First, the Gaussian heat source is modified according to the law of laser energy reflection by powder, and a laser heat source model considering powder reflection is established. Then, the AM900 superalloy laser cladding model was established by using the fluid dynamics method with the modified Gaussian heat source and ordinary Gaussian heat source, respectively. The influence of the heat source model on the behavior of the laser cladding pool was studied. The thermal cycle curve was collected and the cladding layer morphology was obtained in the actual cladding process to verify the model. It is found that the impact of the droplet has the greatest impact on the flow pattern of the molten pool. The modified Gaussian heat source can reduce the peak temperature in the center of the molten pool (about 130 K), which can reflect the actual laser beam during cladding. The flow velocity of the molten metal simulated with the modified Gaussian heat source is slower than that of the ordinary Gaussian heat source, resulting in smaller melt pool width and penetration depth (reduced to 0.27 and 0.12, respectively). The improved heat source model is more in line with the actual cladding process.

Embedding thermocouples in SS316 with laser powder bed fusion*

Recent advances in manufacturing technologies have enabled the fabrication of complex geometries for a wide range of applications, including the energy, aerospace, and civil sectors. The ability to integrate sensors at critical locations within these complex components during the manufacturing process could benefit process monitoring and control by reducing reliance on models to relate surface measurements to internal phenomena. This study investigated embedding thermocouples in a SS316 matrix using laser powder bed fusion. Under optimal processing conditions, embedded thermocouples were characterized post-building, finding good bonding to the matrix with no melt pool penetration to the sensing elements. Moreover, the embedded thermocouple performed similarly to an identical non-embedded thermocouple during thermal testing to 500 °C with only a slight difference in response time, which was attributed to the differences in mass and the associated thermal time constants.

Melt pool geometry dependent microstructural evolution induced by inoculants in IN718 processed by selective laser melting

This preliminary study aims to clarify how Co, TaCr2 and TiB2 grain refinement inducing inoculants influence the melt pool physics and resulting grain structure in superalloy IN718 processed by laser powder bed fusion (L-PBF). It was demonstrated that the presence of solid inoculant particles could affect the melt pool convection leading to a noticeable reduction in the melt pool width and penetration depth as well as the balling phenomenon. Interestingly, it was found that the penetration depth of the melt pool in samples containing TiB2 appeared to be unchanged due to the presence of nano-particles. Characterization of the resulting grain structures of the various samples revealed that the difference in melt pool geometries contributed to a predominant columnar-grained structure with< 101 > texture in samples containing Co or TaCr2 inoculants while an equiaxed-grained structure with< 001 > texture in samples containing TiB2 inoculants. The effectiveness of inoculants was found to be closely related to their impact on the melt pool physics, in addition to the degree of lattice disregistry with the IN718 matrix.

Toward the Understanding of CoAl2O4 Additions on the Formation of Microstructure in Alloy 718 Processed by Laser Powder Bed Fusion

This investigation rationalizes the impact of oxide particles on the melt pool and subsequent formation of microstructure in superalloy Inconel 718 (Alloy 718) processed by laser powder bed fusion (L-PBF). A mixture of Alloy 718 powder with 0.2 wt pct of CoAl2O4 was processed without scan rotation. It was found that the physical presence of oxides appeared to increase the degree of melt pool penetration by inhibiting the heat transfer. Without favorable thermal conditions to trigger heterogeneous nucleation, a more noticeable tendency to form columnar grains was characterized in samples with CoAl2O4. It is believed that the presence of oxide particles in the melt pool contributed to decreasing the cooling rates during solidification and retarded the columnar-to-equiaxed transition. A comparatively deeper degree of melt pool penetration also enhanced the relative density of samples with CoAl2O4. Despite the occurrence of the balling phenomenon, the reduced Marangoni convection also contributed to less micro-humping and better continuity of the melt tracks that decreased the surface roughness by at least 16.7 pct. The addition of CoAl2O4 to the powder feedstock significantly impacted the processing window of L-PBF processed Alloy 718 and may potentially be used as a means to engineer the microstructure.

Aptness of the swarf substrate for the additive manufacturing application

A novel swarf substrate is proposed for the additive manufacturing process. It was prepared by compressing waste metal chips obtained from the conventional machining processes into a flat plate or any desired shape. It could be disintegrated after the deposition, ensuring ease of part removal. Fused metal chips on the bottom layer require minimal post-processing. A fibre laser and two other arc-based heat sources were used to demonstrate the swarf substrate viability for additive manufacturing. Low alloy steel (ER70S-6) and Inconel 625 were used in this study. Melt pool penetration in swarf substrate was observed to be unique and typically formed a tree root structure after solidification. This structure provides strength for holding the further layers upright. The working parameters were optimised for uniform and continuous deposition. Material integrity was confirmed by hardness study comparison with similar parts deposited on the conventional solid substrate. The dilution of swarf material (typically mild steel) was observed to be limited within 2–3 mm layer height, as confirmed by the Energy-dispersive x-ray spectroscopy. The cooling rate was found to be decreasing with layer height and saturated after few initial layers of the deposition similar to the conventional solid substrate. Excellent formability of the swarf substrate allowed to utilise it for unique applications such as deposition of curved features and support structure. The presented work demonstrates additive manufacturing of parts using such geometrical features, including coil spring and alloy wheel. • A novel approach to recycle the swarf as substrate for additive manufacturing. • Successfully performed for three heat sources, i.e. laser beam, MIG and TIG. • A unique tree root structure formed by the swarf/chips to hold the beads. • The substrate facilitates overhang and curved feature deposition. • Material and heat source does not affect the performance of the swarf substrate.

External Illumination Enables Coaxial Sensing of Surface and Subsurface Molten Pool Geometry in LPBF

Laser powder bed fusion (LPBF) attracts the attention of high-end manufacturing sectors for its capability of depositing free-form components with elevated mechanical properties. However, due to the intrinsic nature of the feedstock material and the interaction with the laser beam, the process is prone to defect formation and manufacturing inaccuracies. Therefore, the development of a monitoring architecture capable of measuring the geometrical features of the process tool (i.e., the melt pool generated by the laser-material interaction) is of paramount importance. This information may then be exploited to evaluate process stability. In this work, a high-speed camera was implemented coaxially in the optical chain of an LPBF system to extrapolate the geometrical features of the molten pool surface and its oscillatory behaviour, with elevated spatial and temporal resolution. A secondary light source was tested in both coaxial and off-axis configuration to dominate process emission and assess optimal illumination conditions for extracting the molten pool’s geometrical features. Preliminary results showed that the off-axis configuration of the illumination light enabled direct measurement of the molten pool surface geometry. A newly developed image processing algorithm based on illuminated images obtained via the coaxial observation frame was employed to provide automated identification of the melt pool geometry. Moreover, bright reflections of the external illumination over the melt surface could be clearly observed and used to characterise the oscillatory motion of the molten material. This information may therefore be taken as an indirect indicator of the molten pool penetration depth, hence providing information regarding the subsurface geometry. A successive experimental investigation showed the capability of the monitoring architecture to resolve the molten pool’s length, width and area with elevated acquisition frequency. Molten pool surface oscillations in the kHz range could be correlated to the penetration depth while the molten pool width measured via the high-speed imaging setup corresponded to the track width of the depositions. Hence, the methodological approach for the concurrent measurement of the molten pool’s geometry in three spatial dimensions was demonstrated and may be used to track the stability of LPBF depositions.

Measurements of Liquid AISI 304L Steel Density and Surface Tension, and Influence of Surface‐Active Elements at High Temperatures

Within the framework of the welding of steel components in pressurized water reactors, the optimization of the weld pool penetration is a major issue. The thermal‐hydraulics of the molten steel pool can be driven by Marangoni flows at the free surface. To compute these flows, liquid steel's thermophysical properties such as density ρ and surface tension σ are required. Such properties for austenitic vessel steel AISI 304L at liquid state are scarce, and even not available above 1800 °C, due to technological issues. The viscometer temperature installation (VITI) is designed to implement sessile droplet (SD) and maximum bubble pressure (MBP) techniques. These techniques allow to determine original ρ and σ data for two grades of AISI 304L steel, namely 304LP1 and 304LP4—low and high sulfur content, at various temperatures. In particular, thanks to the innovative design of MBP configuration, original surface tension data are obtained from the melting point up to 2100 °C. For the first time, experimental evidence of the Sahoo et al.'s (1988) model at high temperature is provided, emphasizing the decisive role played by surface‐active elements such as sulfur and oxygen on surface tension, whose content evolution is assessed by dedicated chemical analyses.

Nonintrusive estimation of subsurface geometrical attributes of the melt pool through the sensing of surface oscillations in laser powder bed fusion

Molten pool geometry, whose surface parameters may be extrapolated through direct process observations, has been identified as a fundamental indicator of stability in laser powder bed fusion (LPBF). However, a parameter that cannot be directly measured on industrial systems by means of conventional sensing equipment is the molten pool depth. Indeed, methods based on x-ray imaging demonstrated in the literature have helped to better understand the process. However, retrofitting such solutions to industrial systems does not appear as a viable route currently. Within the present investigation, a nonintrusive sensing method for the indirect measurement of subsurface molten pool geometry based on the detection of surface oscillations is presented. The analysis of frames acquired using a high-speed camera and a secondary illumination light allows the identification of the crests of capillary waves through bright reflections on the surface of the molten pool. The characteristic oscillation frequency of the surface ripples may be correlated with the penetration depth or to other subsurface geometrical parameters. Proof of concept testing of the sensing principle was conducted on two different materials, namely, AISI316L and IN718, by means of single track LPBF depositions. Experiments were conducted at different levels of laser emission power to induce variations in molten pool characteristics. The process was observed by employing an off-axis illumination light and a high-speed camera, which allowed acquisitions with high spatial and temporal resolution. The acquired frames were postprocessed to extract the oscillation indicator, and analysis of the power spectral density of the signal allowed for the identification of the oscillation frequency. Results show that oscillation frequencies range from 3 to 5.5 kHz. Molten pool penetration depth and cross-sectional area could be correlated with the oscillation frequencies for the inline detection of these parameters during LPBF depositions. For both materials, higher oscillation frequencies corresponded to a shallower molten pool and a smaller mass of molten material. Moreover, different characteristic curves of oscillation frequency variations as a function of the melt pool cross-sectional area were determined for IN718 and AISI316L.

Observing molten pool surface oscillations during keyhole processing in laser powder bed fusion as a novel method to estimate the penetration depth

Various in-situ monitoring techniques have been developed for the detection of process drifts in the Laser Powder Bed Fusion (LPBF) process. Currently, optical emission monitoring can retrieve information regarding molten pool characteristics, such as temperature, width, length and area which provide substantial process signatures. Nonetheless, a fundamental indicator for the retrieval of a complete set of spatially distributed information is missing: the molten pool depth. Within the present investigation, a system for the estimation of the penetration depth based on the detection of molten pool surface oscillations is reported. Initially, the fundamentals of the monitoring technique are presented. The principle relies upon the observation of molten pool surface ripples through the measurement of probe light reflections in the melt area. Proof of concept testing of the sensing principle was conducted through an experimental investigation on a prototypal platform. A monitoring system (consisting of a high-speed camera and a secondary illumination light) was employed to view the process while realising both bead-on-plate material remelting and single track powder bed fusion depositions of AISI316L at different levels of laser emission power. Oscillation frequencies were extracted from the high-speed imaging acquisitions after image processing and signal analysis. The surface wave oscillations were measured to be in the range of 3.5–5.5 kHz in keyhole conditions. Metallographic cross-sections allowed to observe the effective molten pool penetration depth and cross-sectional area and were correlated to oscillation frequencies. Higher values of oscillation indicated shallower penetration and consequently a smaller mass of molten material.

Discrimination of molten pool penetration based on genetic algorithm optimization of BP neural network

The metal spatter and light intensity of CO2 welding in the vicinity of the melt pool during the short transition of the melt droplet seriously affect the realtime and reliability of weld feature extraction. The mapping relationship between welding pool characteristic parameters and melting depth is established by using BP neural network optimized by genetic algorithm. The results show that the training results and test results of the optimized BP neural network model of genetic algorithm have little error and meet the requirements of precision. The model can well reflect the relationship between the melting depth and the characteristic parameters of the melting pool.