Stable automatic envelope estimation for noisy doppler ultrasound

Doppler ultrasound technology is widespread in clinical applications and is principally used for blood flow measurements in the heart, arteries and veins. A commonly extracted parameter is the maximum velocity envelope. However, current methods of extracting it cannot produce stable envelopes in high noise conditions. This can limit clinical and research applications using the technology. In this article, a new method of automatic envelope estimation is presented. The method can handle challenging signals with high levels of noise and variable envelope shapes. Envelopes are extracted from a Doppler spectrogram image generated directly from the Doppler audio signal, making it less device-dependent than existing imageprocessing methods. The method’s performance is assessed using simulated pulsatile flow, a flow phantom and in-vivo ascending aortic flow measurements and is compared with three state-of-the-art methods. The proposed method is the most accurate in noisy conditions, achieving on average for phantom data with SNRs below 10 dB, a bias and standard deviation 0.7% and 3.3% lower than the next-best performing method. In addition, a new method for beat segmentation is proposed. When combined, the two proposed methods exhibited the best performance using invivo data, producing the least number of incorrectly segmented beats and 8.2% more correctly segmented beats than the next best performing method. The ability of the proposed methods to reliably extract timing indices for cardiac cycles across a range of signal quality is of particular significance for research and monitoring applications

Effect of process parameters on the microstructure and mechanical properties of AA2024 fabricated using selective laser melting

Selective laser melting (SLM) offers significant benefits, including geometric freedom and rapid production, when compared with traditional manufacturing techniques. However, the materials available for SLM production remain limited, restricting the industrial adoption of the technology. The mechanical properties and microstructure of many aluminium alloys have not been fully explored, as their manufacturability using SLM is extremely challenging. This study investigates the effect of laser power, hatch spacing and scanning speed on the mechanical and microstructural properties of as-fabricated aluminium 2024 alloy (AA2024) manufactured using SLM. The results reveal that almost crack-free structures with high relative density (99.9%) and Archimedes density (99.7%) have been achieved. It is shown that when using low energy density (ED) levels, large cracks and porosities are a major problem, owing to incomplete fusion; however, small gas pores are prevalent at high-energy densities due to the dissolved gas particles in the melt pool. An inversely proportional relationship between ED and microhardness has also been observed. Lower ED decreases the melt pool size and temperature gradients but increases the cooling rate, creating a fine-grained microstructure, which restricts dislocation movement, therefore increasing the microhardness. The highest microhardness (116 HV0.2), which was obtained from one of the lowest EDs used (100 J/mm3), is 45% higher than as-cast AA2024-0, but 17% lower than wrought AA2024-T6 alloy

Heterogeneous microstructure and mechanical behaviour of Al-8.3Fe-1.3V-1.8Si alloy produced by laser powder bed fusion

The relationship between processing parameters, microstructure, and mechanical properties of Al-8.3Fe-1.3V-1.8Si alloy processed by laser powder bed fusion is seldom studied. Therefore, fully dense alloys with two parameters were selected to investigate this key issue. The results show that the alloy with low power and scanning speed (S200) shows fan-shell-shaped melt pools and laser tracks while another (S350) shows a deeper and wider melt pool. Both alloys obtain a heterogeneous microstructure without a secondary phase in melt pool (MP) and a nano-sized phase in melt pool boundary (MPB). The difference between solid-solution strengthening and Orowan strengthening in MP and MPB contributes to the difference in compressive yield strength (S200: 380 ± 14 MPa and S350: 705 ± 16 MPa), and heterogeneous nano-hardness results in different crack behaviours and failure strains. This work indicates that adjusting processing parameters is an effective method to control microstructure and mechanical properties of this alloy

A hybrid method for feature recognition in computer-aided design models

Automatic feature recognition (AFR) techniques applied to three-dimensional (3D) solid models are an important tool for achieving a true integration of computer-aided design (CAD) and computer-aided manufacturing (CAM) processes. In particular, AFR systems allow the identification in CAD models of high-level geometrical entities: features that are semantically significant for manufacturing operations. However, the recognition performances of most of the existing AFR systems are limited to the requirements of specific manufacturing applications. This paper presents a new hybrid method that facilitates the deployment of AFR systems in different application domains. In particular, the method includes two main processing stages: learning and feature recognition. During the learning stage, knowledge acquisition techniques are applied for generating feature-recognition rules and feature hints automatically from training data. Then, these hints and rule bases are utilized in the feature-recognition stage to analyse boundary representation (B-Rep) part models and identify their feature-based internal structure. The proposed AFR method is implemented within a prototype feature-recognition system and its capabilities are verified on two benchmarking parts