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This study presents a comparative analysis of two laser powder-based directed energy deposition (DED) techniques—conventional Laser Metal Deposition (LMD) and High Speed Laser Cladding (HSLC)—for coating high value-added components using the same hybrid machine . Experimental tests were performed to characterize the HSLC and compared with the results obtained in previous works associated with conventional LMD. The same base material (42CrMoS4 steel alloy) and two filler materials (a Ni-based alloy and an Fe-based alloy) were used in both cases, with tests performed on both preheated and non-preheated substrates. Powder efficiency, material deposition rate (MDR), coated surface per minute or coating rate (CR), argon consumption and microhardness were analyzed. The results indicate that for the Ni-based alloy, preheating the substrate is necessary in both techniques to avoid crack formation. In the case of the Fe-based alloy, cracks were only observed in the HSLC variant when the substrate was preheated. Significant differences in productivity were observed between the two technology variants for both materials, particularly in terms of CR and MDR, with HSLC showing a significant increase in both metrics. HSLC achieved coating rates between 4500 and 6860 mm 2 /min, representing a minimum productivity improvement of 130% compared to LMD. MDR values reached up to 1.72 kg/h for Ni-based and 1.39 kg/h for Fe-based alloys, significantly higher than the < 0.8 kg/h observed in LMD. Argon consumption per deposited kilogram was reduced by up to 80%. Maximum hardness values of 62–64 HRC (Fe-based) and 54–57 HRC (Ni-based) were achieved.

Cognitive load stimulates neural activity, essential for understanding the brain’s response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes. The binary arithmetic optimization (BAO) algorithm employed to reduce the feature space and extract multi-domain features from modes, thereby optimizing classification performance. Using six optimized machine learning (ML) classifiers, we conducted an exhaustive study that encompassed both lead-wise and overall feature classification. We improved our method by combining R-LMD-based multi-domain features with BAO and optimized ensemble learning (OEL) classifiers. It was 97.4% accuracy (AC) at finding cognitive load in the MAT (mental arithmetic task) dataset and 96.1% AC at finding it in the STEW (simultaneous workload) dataset. In the same vein, this work introduces lead-wise cognitive load detection, which offers both temporal and spatial information regarding brain activity during cognitive tasks. We analyzed the 19 and 14 leads for the MAT and STEW, respectively. The F3 lead was notably noteworthy in its ability to analyze a variety of cognitive tasks, obtaining the maximum classification AC of 94.5% and 94%, respectively. Our approach (R-LMD+BAO+OEL) outperformed existing state-of-the-art techniques in cognitive load detection.

Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

With the development of society and the economy, there is an increasing demand for surface treatment techniques that can efficiently utilize metal materials to obtain good performances in the fields of mechanical engineering and the aerospace industry. The laser metal deposition (LMD) technique for cladding has become a research focus in recent years because of its lower dilution rate, small heat-effect zone and good metallurgical bonding between the coating and substrate. This paper reviews the simulation technology for the melt pool’s grain growth mechanism, temperature and stress distribution that are directly related to defect formation in LMD technology. At the same time, the defect suppression method and the performance improvement method of the cladded layer in LMD technology are introduced. Finally, it is pointed out that the active selection of materials according to the required performance, combined with the controllable processing technology, to form the corresponding microstructure, and finally, to actively realize the expected function, is the future development direction of LMD technology.

Acoustic emissions in directed energy deposition processes such as wire arc additive manufacturing and directed energy deposition with laser beam/metal are investigated within this work, as many insights about the process can be gained from this. In both processes, experienced operators can hear whether a process is running stable or not. Therefore, different experiments for stable and unstable processes with common process anomalies were carried out, and the acoustic emissions as well as process camera images were captured. Thereby, it was found that stable processes show a consistent mean intensity in the acoustic emissions for both processes. For wire arc additive manufacturing, it was found that by the Mel spectrum, a specific spectrum adapted to human hearing, the occurrence of different process anomalies can be detected. The main acoustic source in wire arc additive manufacturing is the plasma expansion of the arc. The acoustic emissions and the occurring process anomalies are mainly correlating with the size of the arc because that is essentially the ionized volume leading to the air pressure which causes the acoustic emissions. For directed energy deposition with laser beam/metal, it was found that by the Mel spectrum, the occurrence of an unstable process can also be detected. The main acoustic emissions are created by the interaction between the powder and the laser beam because the powder particles create an air pressure through the expansion of the particles from the solid state to the liquid state when these particles are melted. These findings can be used to achieve an in situ quality assurance by an in-process analysis of the acoustic emissions.

Green laser sources are advantageous in the processing of copper due to the increase of absorptivity compared with more commonly available infrared lasers. Laser metal deposition of copper with a green laser onto various substrate metals namely copper, aluminium, steel and titanium alloy was carried out and observed through high-speed imaging. The effects of process parameters such as laser power, cladding speed and powder feed rate, and material attributes such as absorptivity, surface conditions and thermal conductivity are tied together to explain the size and geometry of the melt pool as well as the fraction of the power used for melting material. The copper substrate has the smallest melt pool with a high angle, followed by aluminium, steel and titanium alloy. The incorporation times for powder grains in the melt pools vary based on the substrate materials. Its dependency on material properties, including surface tension forces, melting temperatures and material density, is discussed. Oxide skins present on melt pools can affect powder incorporation, most significantly on the aluminium substrate. The lower limits of the fraction of power irradiated on the surface used purely for melting were calculated to be 0.73%, 2.94%, 5.95% and 9.78% for the copper, aluminium, steel and titanium alloy substrates, respectively, showing a strong dependence on thermal conductivity of the substrate material. For a copper wall built, the fraction was 2.66%, much higher than a single clad on a copper substrate, due to reduced workpiece heating. The results of this paper can be transferred to other metals with low absorptivity such as gold.

For the purpose of improving the productivity of laser metal deposition (LMD), the focus of current research is set on increasing the deposition rate, in order to develop high-deposition-rate LMD (HDR-LMD). The presented work studies the effects of the powder stream on HDR-LMD with Inconel 718. Experiments have been designed and conducted by using different powder feeding nozzles—a three-jet and a coaxial powder feeding nozzle—since the powder stream is mainly determined by the geometry of the powder feeding nozzle. After the deposition trials, metallographic analysis of the samples has been performed. The laser intensity distribution (LID) and the powder stream intensity distribution (PID) have been characterized, based on which the processes have been simulated. Finally, for verifying and correcting the used models for the simulation, the simulated results have been compared with the experimental results. Through the conducted work, suitable boundary conditions for simulating the process with different powder streams has been determined, and the effects of the powder stream on the process have also been determined. For a LMD process with a three-jet nozzle a substantial part of the powder particles that hit the melt pool surface are rebounded; for a LMD process with a coaxial nozzle almost all the particles are caught in the melt pool. This is due to the different particle velocities achieved with the two different nozzles. Moreover, the powder stream affects the heat exchange between the heated particles and the melt pool: a surface boundary condition applies for a powder stream with lower particle velocities, in the experiment provided by a three-jet nozzle, and a volumetric boundary condition applies for a powder stream with higher particle velocities, provided by a coaxial nozzle.

Background: Qianhu is a traditional Chinese medicine. It is thought that Qianhu roots will harden after bolting and not be suitable for medicinal purposes. Bolting Qianhu and unbolting Qianhu are referred to as “Xiong Qianhu” and “Ci Qianhu,” respectively. In this study, the properties, microscopic and chemical characteristics of Ci Qianhu and Xiong Qianhu roots were compared using fluorescence microscopy, laser microdissection coupled with ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry, and high-performance liquid chromatography with diode-array detection. Results: Microscopy results showed that the area of secondary xylem in the root increased after bolting, with the cork and secretory canals showing strong fluorescence intensity. A total of 34 peaks, mostly pyranocoumarins, were identified in the tissues of Ci Qianhu and Xiong Qianhu. The secretory canals contained the highest variability of coumarins, whereas the secondary xylem contained the least coumarins. Moreover, seven coumarins, especially the pyran- coumarin, decreased after bolting. Generally, both before and after bolting, coumarin level was the highest in the bark, followed by the middle part, and the lowest in the inner part. Conclusion: Thus, it was indicated that the area of secondary xylem increased after bolting, however the coumarin variant and content decreased in the secondary xylem of Qianhu. The result shows that the quality of Qianhu decreases after bolting, which supports the viewpoint that Xiong Qianhu is not suitable for medicinal use.

The global navigation satellite system–interferometric reflectometry (GNSS-IR) technique has emerged as an effective coastal sea-level monitoring solution. However, the accuracy and stability of GNSS-IR sea-level estimation based on quadratic fitting are limited by the retrieval range of reflector height (RH range) and satellite-elevation range, reducing the flexibility of this technology. This study introduces a new GNSS-IR sea-level estimation model that combines local mean decomposition (LMD) and Lomb–Scargle periodogram (LSP). LMD can decompose the signal-to-noise ratio (SNR) arc into a series of signal components with different frequencies. The signal components containing information from the sea surface are selected to construct the oscillation term, and its frequency is extracted by LSP. To this end, observational data from SC02 sites in the United States are used to evaluate the accuracy level of the model. Then, the performance of LMD and the influence of noise on retrieval results are analyzed from two aspects: RH ranges and satellite-elevation ranges. Finally, the sea-level variation for one consecutive year is estimated to verify the stability of the model in long-term monitoring. The results show that the oscillation term obtained by LMD has a lower noise level than other signal separation methods, effectively improving the accuracy of retrieval results and avoiding abnormal values. Moreover, it still performs well under loose constraints (a wide RH range and a high-elevation range). In one consecutive year of retrieval results, the new model based on LMD has a significant improvement effect over quadratic fitting, and the root mean square error and mean absolute error of retrieval results obtained in each month on average are improved by 8.34% and 8.87%, respectively.

Diffusiophoresis of a liquid metal droplet (LMD) in a cylindrical pore is investigated theoretically in this study. A patched pseudo-spectral method based on Chebyshev polynomials combined with a geometric mapping technique is adopted to solve the resulting governing electrokinetic equations in irregular geometries. Several interesting phenomena are found which provide useful guidelines in practical applications involving liquid metal droplets (LMDs) such as drug delivery. In particular, the severe boundary confinement effect brings about unique features of droplet motion, leading to mobility reversal and a “stagnation phenomenon” where droplets cease to move regardless of their surface charge densities in a narrow cylindrical pore. An overwhelming exterior vortex flow nearly enclosing the entire droplet is found to be responsible for this. This finds various practical applications in droplet microfluidics and drug delivery. For instance, a cylindrical pore or blood vessel may be clogged by a droplet much smaller than its radius. In addition, the “solidification phenomenon”, where all droplets move with identical speed regardless of their viscosities like rigid particles with no interior recirculating vortex flows, is also discovered. The electrokinetic mechanism behind it and its potential applications are discussed. Overall, the geometric configuration considered here is a classic one, with many other possible applications yet to be found by experimental researchers and engineers in the field of colloid industry and operations.