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This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

For the purpose of saving nickel, this study aims to develop new duplex stainless steel cored wires suitable for wire arc additive manufacturing (WAAM) with the addition of nitrogen.

The effect of nitrogen content on the microstructure and mechanical properties of the thin-walled deposits is investigated in detail.

The microstructure of thin-walled deposits mainly consists of austenite, ferrite and secondary austenite. With increasing nitrogen content, the austenite in the deposited metals increases. The austenite proportion in the bottom region is more than that in the top region of the deposited metals. The χ phase is randomly distributed at the grain boundaries and within ferrite. The σ phase is mainly precipitated at ferrite and austenite grain boundaries. With increasing nitrogen content, the tensile strength of the deposited metals increases, but the impact toughness of the deposited metals deteriorates.

This study proposes new duplex stainless steel cored wires for WAAM, which realizes the objective of saving nickel.

High-performance wrought aluminum alloys, particularly AA6061, are pivotal in industries like automotive and aerospace due to their exceptional strength and good response to heat treatments. Investment casting offers precision manufacturing for these alloys, because casting AA6061 poses challenges like hot cracking and severe shrinkage during solidification. This study aims to address these issues, enabling crack-free investment casting of AA6061, thereby unlocking the full potential of investment casting for high-performance aluminum alloy components.

Nanotechnology is used to enhance the investment casting process, incorporating a small volume fraction of nanoparticles into the alloy melt. The focus is on widely used aluminum alloy 6061, utilizing rapid investment casting (RIC) for both pure AA6061 and nanotechnology-enhanced AA6061. Microstructural characterization involved X-ray diffraction, optical microscopy, scanning electron microscopy, differential scanning calorimetry and energy dispersive X-ray spectroscopy. Mechanical properties were evaluated through microhardness and tensile testing.

The study reveals the success of nanotechnology-enabled investment casting in traditionally challenging wrought aluminum alloys like AA6061. Achieving crack-free casting, enhanced grain morphology and superior mechanical properties, because the nanoparticles control grain sizes and phase growth, overcoming traditional challenges associated with low cooling rates. This breakthrough underscores nanotechnology's transformative impact on the mechanical integrity and casting quality of high-performance aluminum alloys.

This research contributes originality and value by successfully addressing the struggles in investment casting AA6061. The novel nano-treating approach overcomes solidification defects, showcasing the potential of integrating nanotechnology into rapid investment casting. By mitigating challenges in casting high-performance aluminum alloys, this study paves the way for advancements in manufacturing crack-free, high-quality aluminum alloy components, emphasizing nanotechnology's transformative role in precision casting.

This study used an indentation-based mechanical testing framework for the mechanical characterization of laser powder bed fusion (LPBF) processed Inconel 718 on a wrought Inconel 718 substrate. The purpose of the paper is to investigate the effectiveness of the indentation-based approach for localized mechanical evaluation.

The LPBF-processed wrought substrate was sectioned into three sections for microstructural and mechanical characterization. A 3D heat source model was used for the thermal analysis of the interface region. The developed interface region is probed using the Knoop hardness indenter in different orientations to determine the textural anisotropy and mechanical behavior of the region.

LPBF process develops a melted interface zone (MIZ) at the deposition-substrate interface. The MIZ exhibited a coarse grain structure region along with a larger primary dendritic arm spacing (PDAS), signifying a slower cooling rate. FE modeling of the LPBF process reveals heat accumulation in the substrate along with intrinsic heat treatment (IHT) induced due to layer-wise processing. The obtained yield locus shows strong anisotropy in the deposition region, whereas reduced anisotropy with a nearly uniform ellipse locus for the MIZ regions. This reduced anisotropy is attributable to IHT and heat accumulation in the substrate.

An alternative localized mechanical characterization tool has been investigated in this work. The approach proved sensitive to thermal variations during LPBF processing in an isolated region which extends its suitability to variable geometry parts. Moreover, the approach could serve as a screening tool for parts made from dissimilar metals.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

The purpose of this study is to develop a new numerical algorithm to simulate the phase-field model.

First, the derivative of the temporal direction is discretized by a second-order linearized finite difference scheme where it conserves the energy stability of the mathematical model. Then, the isogeometric collocation (IGC) method is used to approximate the derivative of spacial direction. The IGC procedure can be applied on irregular physical domains. The IGC method is constructed based upon the nonuniform rational B-splines (NURBS). Each curve and surface can be approximated by the NURBS. Also, a map will be defined to project the physical domain to a simple computational domain. In this procedure, the partial derivatives will be transformed to the new domain by the Jacobian and Hessian matrices. According to the mentioned procedure, the first- and second-order differential matrices are built. Furthermore, the pseudo-spectral algorithm is used to derive the first- and second-order nodal differential matrices. In the end, the Greville Abscissae points are used to the collocation method.

In the numerical experiments, the efficiency and accuracy of the proposed method are assessed through two examples, demonstrating its performance on both rectangular and nonrectangular domains.

This research work introduces the IGC method as a simulation technique for the phase-field crystal model.

This paper aims to present spot pattern welding (SPW) as a scanning strategy for laser-foil-printing (LFP) additive manufacturing (AM) in place of the previously used continuous pattern welding (CPW) (line-raster scanning). The SPW strategy involves generating a sequence of overlapping spot welds on the metal foil, allowing the laser to form dense and uniform weld beads. This in turn reduces thermal gradients, promotes material consolidation and helps mitigate process-related risks such as thermal cracking, porosity, keyholing and Marangoni effects.

304L stainless steel (SS) feedstock is used to fabricate test specimens using the LFP system. Imaging techniques are used to examine the melt pool dimensions and layer bonding. In addition, the parts are evaluated for residual stresses, mechanical strength and grain size.

Compared to CPW, SPW provides a more reliable heating/cooling relationship that is less dependent on part geometry. The overlapping spot welds distribute heat more evenly, minimizing the risk of elevated temperatures during the AM process. In addition, the resulting dense and uniform weld beads contribute to lower residual stresses in the printed part.

To the best of the authors’ knowledge, this is the first study to thoroughly investigate SPW as a scanning strategy using the LFP process. In general, SPW presents a promising strategy for securing embedded sensors into LFP parts while minimizing residual stresses.

The purpose of this study is to investigate the influence of varying concentrations of nano-SiO₂ particle doping on the structure and properties of the micro-arc oxidation (MAO) coating of 7075 aluminum alloy. This research aims to provide novel insights and methodologies for the surface treatment and protection of 7075 aluminum alloy.

The surface morphology of the MAO coating was characterized using scanning electron microscope. Energy spectrometer was used to characterize the elemental content and distribution on the surface and cross section of the MAO coating. The phase composition of the MAO coating was characterized using X-ray diffractometer. The corrosion resistance of the MAO coating was characterized using an electrochemical workstation.

The results showed that when the addition of nano-SiO₂ particles is 3 g/L, the corrosion resistance is optimal.

This study investigated the influence of different concentrations of nano-SiO₂ particles on the structure and properties of the MAO coating of 7075 aluminum alloy.

Herein, the authors report the effects of printing parameters, joining method, and annealing conditions on the structural performance of fusion-joined short-beam sections produced by additive manufacturing.

The authors first identified appropriate printing parameters for joining segmented short beams and then used those parameters to print and fusion-join segments with different configurations of stiffeners to form a longer section of a wing or small wind turbine blade structure.

It was found that the beams with three lateral and three base stiffening ribs give the highest flexural strength among the three beams investigated. Results on joined beams annealed at different conditions showed that annealing at 70 °C for 0.5 h yields higher performance than annealing at the same temperature for longer times. It is also found that in the case of the hot-plate-welded three-dimensional (3D)-printed structures, no annealing is needed for reaching a high strength-to-weight ratio, but annealing is helpful for maximizing the modulus-to-weight ratio. Both thermal buckling and edge wrapping were observed under annealing at 70°C for 0.5 h for 3D-printed beams comprising two lateral and four base stiffening plates.

Fusion-joining of additively manufactured segments is needed owing to the constraint in building volume of a typical commercial 3D-printer. However, study of the effect of process parameters is needed to quantify their effect on mechanical performance. This investigation has therefore identified key printing parameters and annealing conditions for fusion-joining short segments to form larger structures, from multiple 3D-printed sections, such as wind blade structures.