Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition

Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper proposes a novel multimodal sensor fusion method for real-time location-dependent defect detection in the robotic L-DED process. The multimodal fusion sources include a microphone sensor capturing the laser-material interaction sound and a visible spectrum CCD camera capturing the coaxial melt pool images. A hybrid convolutional neural network (CNN) is proposed to fuse acoustic and visual data. The key novelty in this study is that the traditional manual feature extraction procedures are no longer required, and the raw melt pool images and acoustic signals are fused directly by the hybrid CNN model, which achieved the highest defect prediction accuracy (98.5 %) without the thermal sensing modality. Moreover, unlike previous region-based quality prediction, the proposed hybrid CNN can detect the onset of defect occurrences. The defect prediction outcomes are synchronized and registered with in-situ acquired robot tool-center-point (TCP) data, which enables localized defect identification. The proposed multimodal sensor fusion method offers a robust solution for in-situ defect detection.Comment: 8 pages, 10 figures. This paper has been accepted to be published in the proceedings of IDETC-CIE 202

In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

Laser cladding materials-process-performance-investigation

Laser cladding is an additive manufacturing process that is used for surface repair and restoration applications in many industries such as in aerospace, marine and transport sections. In this study, modelling of residual stress from laser cladding process, fatigue performance testing and fatigue life prediction analysis is reported. AISI 4340 steel substrate-clad pair system was selected for this investigation as it is widely used in relevant applications discussed. In this work, clad specimens were fabricated, cut, polished and etched for microstructure observation and micro-hardness indentation studies to characterize clad to substrate material properties. A framework for fatigue performance characterization of laser clad specimens is presented in this study. Five types of laser clad specimens were designed for fatigue tests to characterize the fatigue failure behavior of laser clad AISI 4340 steel powder on steel substrate and compared to 4340 substrate fatigue S-N curve results. Type I specimen is designed to characterize fatigue failure behavior from the clad weld toe region of as-clad specimens. A significant reduction in fatigue performance for Type I specimen was observed due to failure from clad-toe features. Type II and Type III specimen designs investigate the feasibility of using pre and post-clad machining operations to improve fatigue strength. Type IV specimen design is used to characterize clad specimens with extended clad surface area. The post-clad ground specimen show significant improvement in fatigue strength. Type V specimen design with post-clad heat-treatment is able to recover fatigue strength back to comparable level with the unclad substrate material S-N curve performances. A three dimensional finite element (FE) model was developed to simulate residual stress induced from laser cladding of AISI 4340 steel. A laser power attenuation model was proposed for the laser-powder-interaction zone under the coaxial powder feeding nozzle. The thermal analysis integrated the deposition of clad beads with laser heat input. This was implemented using user-defined subroutines to thermally activate clad element conductivity and surface heat transfer film conditions simultaneously with the attenuated laser heat flux. The FE model was subsequently applied to simulate cladding of 10 clad beads over an area to study the effects of depositing multiple successive clad beads on residual stress field. Fractured surface of Type I as-clad specimen show that multiple surface cracks initiate from the clad-toe region due to clad bead overlap features deposited in a raster scan pattern. A fatigue crack growth modeling algorithm capturing the observed fatigue behavior of periodic multiple co-planar semi-elliptical cracks initiating from these features was developed based on crack closure concepts for small cracks to predict the fatigue S-N curve.Doctor of Philosophy (MAE

Achieving grain refinement and ultrahigh yield strength in laser aided additive manufacturing of Ti−6Al−4V alloy by trace Ni addition

Fabricating fine equiaxed grains without undesirable secondary phases is highly challenging for additively manufactured Ti−6Al−4V alloy. The reference amount of Ni addition, which can achieve grain refinement without secondary phase formation, is 0.9 wt. % based on Thermo-Calc calculation. The Ti−6Al−4V−0.9Ni alloy produced by laser-based directed energy deposition demonstrate refined microstructure and an ultrahigh yield strength (1309 MPa). A modified quantitative model is proposed to analyse the strengthening mechanism, and the results demonstrate that the yield strength increment is mainly ascribed to the refined α phase. This work can contribute to the development of customised titanium alloy using additive manufacturing

Integrated numerical modelling and deep learning for multi-layer cube deposition planning in laser aided additive manufacturing

10.1080/17452759.2021.1922714Virtual and Physical Prototyping163318-33

Data-driven adaptive control for laser-based additive manufacturing with automatic controller tuning

Closed-loop control is desirable in direct energy deposition (DED) to stabilize the process and improve the fabrication quality. Most existing DED controllers require system identifications by experiments to obtain plant models or layer-dependent adaptive control rules, and such processes are cumbersome and time-consuming. This paper proposes a novel data-driven adaptive control strategy to adjust laser voltage with the melt pool size feedback. A multitasking controller architecture is developed to incorporate an autotuning unit that optimizes controller parameters based on the DED process data automatically. Experimental validations show improvements in the geometric accuracy and melt pool consistency of controlled samples. The main advantage of the proposed controller is that it can adapt to DED processes with different part shapes, materials, tool paths, and process parameters without tweaking. System identification is not required even when process conditions are changed, which reduces the controller implementation time and cost for end-users.Agency for Science, Technology and Research (A*STAR)Published versionThis research was funded by A*ccelerate, grant number ACCL/19-GAP077-R20A

Superior strength-ductility in laser aided additive manufactured high-strength steel by combination of intrinsic tempering and heat treatment

This work investigated laser aided additive manufacturing (LAAM) high-strength steel by leveraging the intrinsic tempering effect to facilitate the formation of high-fraction of metal carbides (e.g. M23C6 and M7C3) in the as-built samples. The intrinsic tempering effect contributes to a superior mechanical property than traditional manufacturing methods in as-built condition, promoting subsequent heat treatments (HTs) for excellent mechanical properties. The influence of HTs on the microstructures and mechanical properties were characterised in multi-scales. A large number of carbides are intrinsically formed due to the tempering effect during deposition. The high-density dislocations in the as-built sample facilitate the formation of massive nano-twins and carbides during HT. The HTed sample achieves a true tensile stress of about 1.81 GPa together with a true strain of about 21%, achieving an excellent strength-ductility combination compared to wide-range high-strength steels processed by additive manufacturing and conventional methods. The grain and twin boundaries strengthening, precipitation strengthening and dislocation strengthening contribute to the high strength, while the good ductility originates from twinning induced plasticity (TWIP) and transformation-induced plasticity (TRIP) effects, and high work-hardening rate, during deformation. The findings imply a potential way to develop AM-customised materials by fully understanding and utilising the IHT effect

Microstructure and mechanical behavior of laser aided additive manufactured low carbon interstitial Fe₄₉.₅Mn₃₀Co₁₀Cr₁₀C₀.₅ multicomponent alloy

Laser aided additive manufacturing (LAAM) was used to fabricate bulk Fe49.5Mn30Co10Cr10C0.5 interstitial multicomponent alloy using pre-alloyed powder. The room temperature yield strength (σy), ultimate tensile strength (σUTS) and elongation (εUTS) were 645 MPa, 917 MPa and 27.0 % respectively. The as-built sample consisted of equiaxed and dendritic cellular structures formed by elemental segregation. These cellular structures together with oxide particle inclusions were deemed to strengthen the material. The other contributing components include dislocation strengthening, friction stress and grain boundary strengthening. The high εUTS was attributed to dislocation motion and activation of both twinning and transformation-induced plasticity (TWIP and TRIP). Tensile tests performed at −40 °C and −130 °C demonstrated superior tensile strength of 1041 MPa and 1267 MPa respectively. However, almost no twinning was observed in the fractured sample tested at −40 °C and −130 °C. Instead, higher fraction of strain-induced hexagonal close-packed (HCP) ε phase transformation of 21.2 % were observed for fractured sample tested at −40 °C, compared with 6.3 % in fractured room temperature sample.Agency for Science, Technology and Research (A*STAR)This research was supported by Agency for Science, Technology and Research (A*Star), Republic of Singapore, under the IAF-PP program “Integrated large format hybrid manufacturing using wire-fed and powder-blown technology for LAAM process”, Grant No: A1893a0031

Numerical and experimental study of laser aided additive manufacturing for melt-pool profile and grain orientation analysis

Laser aided additive manufacturing (LAAM), a blown powder additive manufacturing process, can be widely adopted for surface modification, repair and 3D printing. A robust numerical model was developed to simulate convective fluid flow and balancing of surface tension forces at the air-fluid interface to predict melt-pool free surface curvature and solidified clad dimensions. The free surface physical interface was calculated using the Arbitrary Lagrangian Eulerian (ALE) moving mesh approach. Powder deposition efficiency was considered by activating mesh normal velocity at melted regions based on localized powder mass flux intensity from the discrete coaxial powder nozzles. The heat flux equation used for representing the laser heat source considered attenuation effect from the interaction between the powder jets and laser as well as heat sink effects of un-melted powder particles entering the melt-pool. The predicted thermal gradient directions agree well with grain growth orientations obtained from electron backscatter diffraction (ESBD) analysis in three different cross-sectional orientations. Experimental validation of clad width, height and melt-pool depth shows a maximum error of 10% for a wide range of processing parameters which consider the effects of varying laser power, laser scanning speed and powder feeding rate.ASTAR (Agency for Sci., Tech. and Research, S’pore

Review on laser directed energy deposited aluminum alloys

Lightweight aluminum (Al) alloys have been widely used in frontier fields like aerospace and automotive industries, which attracts great interest in additive manufacturing (AM) to process high-value Al parts. As a mainstream AM technique, laser-directed energy deposition (LDED) shows good scalability to meet the requirements for large-format component manufacturing and repair. However, LDED Al alloys are highly challenging due to their inherent poor printability (e.g. low laser absorption, high oxidation sensitivity and cracking tendency). To further promote the development of LDED high-performance Al alloys, this review offers a deep understanding of the challenges and strategies to improve printability in LDED Al alloys. The porosity, cracking, distortion, inclusions, element evaporation and resultant inferior mechanical properties (worse than laser powder bed fusion) are the key challenges in LDED Al alloys. Processing parameter optimizations, in-situ alloy design, reinforcing particle addition and field assistance are the efficient approaches to improving the printability and performance of LDED Al alloys. The underlying correlations between processes, alloy innovation, characteristic microstructures, and achievable performances in LDED Al alloys are discussed. The benchmark mechanical properties and primary strengthening mechanism of LDED Al alloys are summarized. This review aims to provide a critical and in-depth evaluation of current progress in LDED Al alloys. Future opportunities and perspectives in LDED high-performance Al alloys are also outlined