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The purpose of this paper is to propose a template‐based framework for nasal prosthesis fabrication using a 3D areal scanner and a CT scanner.

Use of a self‐designed 3D areal scanner enables acquisition of accurate data describing the patient's face. Patients with nasal defects have no organization for reference, but the template‐based model construction method can ensure successful building of the outer surface of the nasal prosthesis. Since the areal scanner has some difficulties acquiring data for concave areas, preoperative CT data are used to provide concave information, enabling construction of the inner surface for the nasal prosthesis. The combined inner and outer surfaces are used to generate the completed nasal prosthesis.

The results showed that the nasal prosthesis fits the patient's appearance well. Clinical applications confirmed that this framework is attractive and has the potential desired nasal prosthesis in daily clinical practice.

The results of this study improve the fabrication accuracy of nasal prostheses. The construction and development technique employs a nasal digital library, 3D areal scanning data and CT scanning data. This technique facilitates fabrication of precise nasal prostheses while helping the patients predict the effect before the prosthesis is manufactured.

This template‐based framework has strong potential for clinical applications because of its advantages over other methods in terms of accuracy, speed, safety, and cost.

To fabricate high performance parts, this paper aims to systematically study the pores characteristics and their formation mechanisms in selective laser melting (SLM) AlSi10Mg.

Cubes of 10 × 10 × 5 mm were manufactured in different laser power, scan speed and scan space. Optical microscope (OM) and scanning electron microscopes (SEM) were used to observe morphology of pores.

Round or irregular pores were found in SLMed AlSi10Mg parts. All the round pores have smooth inner walls and locate in the melt pool. The formation mechanisms of the round pores are contributed to the evaporation of elements in the melt pool, H₂O, high laser energy input and hollow powder. Irregular pores have rough inner walls. Big scan space, unevenness of the upper surface, large layer thickness, spatter and oxide are the main reasons of generating irregular pores which outside the melt pool. Instability of keyhole leads to the irregular pores locate in the bottom of keyhole mode melt pool.

Relationship between pores and melt pool were studied systematically for the first time. Researches of pores characteristics and their formation mechanisms in SLMed AlSi10Mg would be a valuable reference for researchers to obtain an important insight into and control the defect in SLMed Al alloy.

For the laser powder bed fusion (L-PBF) technology, the side surface quality is essentially important for industrial applicated parts, such as the inner flow parts. Contour is generally adopted at the parts’ outline to enhance the side surface quality. However, the side surface roughness (Ra) is still larger than 10 microns even with contour in previous studies. The purpose of this paper is to study the influence of contour process parameters, laser power and scanning velocity on the side surface quality of the AlSi10Mg sample.

Using L-PBF technology to manufacture AlSi10Mg samples under different contour process parameters, use a laser confocal microscope to capture the surface information of the samples, and obtain the surface roughness Ra and the maximum surface height Rz of each sample after analysis and processing.

The results show that the side surface roughness decreases with the increase of the laser power at the fixed scanning velocity of 1,000 mm/s, the side surface roughness Ra stays within the error range as the contour velocity increases. It is found that the Ra increases with the scanning velocity increasing and the greater the laser power with the greater Ra increases when the laser power of contour process parameters is 300 W, 350 W and 400 W. The Rz maintain growth with the contour scanning velocity increasing at constant laser power. The continuous uniform contour covers the pores in the molten pool of the sample edge and thus increase the density of the sample. Two mechanisms named “Active adhesion” and “Passive adhesion” cause sticky powder.

Formation of a uniform and even contour track is key to obtain the good side surface quality. The side surface quality is determined by the uniformity and stability of the contour track when the layer thickness is fixed. These research results can provide helpful guidance to improve the surface quality of L-PBF manufactured parts.

High temperature gradient induces high residual stress, producing an important effect on the part manufacturing during laser powder bed fusion (LPBF). The purpose of this study is to investigate the effect of the molten pool mode on the thermal stress of Ti-6Al-4V alloy during different deposition processes.

A coupled thermal-mechanical finite element model was built. The developed model was validated by comparing the numerical results with the experimental data in the maximum molten pool temperature, the molten pool dimension and the residual stress described in the previous work.

For the single-track process, the keyhole mode caused an increase in both the maximum stress and the high-stress area compared with the conduction mode. For the multitrack process, a lower tensile stress around the scanning track and a higher compressive stress below the scanning track were found in the keyhole mode. For the multilayer process, the stress along the scanning direction at the middle of the part changed from tensile stress to compressive stress with the increase in the deposition layer number. As the powder layer number increased, the stress along the scanning direction near the top surface of the part decreased while the stress along the deposition direction obviously increased, indicating that the stress along the deposition direction became the dominant stress. The keyhole mode can reduce the residual stress near the top of the part, and the conduction mode was more likely to produce a low residual stress near the bottom of the part.

The results provide a systematic understanding of thermal stress during the LPBF process.

The purpose of this study is to investigate the effect of the strut size and tilt angle on the densification behavior, surface roughness and dimensional accuracy of the selective laser melting AlSi10Mg lattice structure was investigated in this study. In this study, the characteristics such as the density, up-skin and down-skin roughness and dimensional accuracy of selective laser melting forming technology manufacturing (SLMed) AlSi10Mg cellular lattice structure were carried. This work reveals the effect of the strut size and tilt angle on the geometric characteristics of SLMed AlSi10Mg and is benefit for controlling the forming performance of the SLMed cellular lattice structure.

Based on AlSi10Mg powder, the influence of the tilt angle changed from 10° to 45° with an increment of 5° were investigated, the influence of the strut size was varied from 0.4 mm to 1.2 mm with an increment of 0.2 mm were investigated. The characteristics such as the density, up-skin and down-skin roughness, dimensional accuracy and mechanical properties of SLM-ed AlSi10Mg cellular lattice structure was carried.

Greater than 99% relative density can be achieved for different strut size when optimal process parameters are used. In the optimized process interval, the struts with a tilt angle of 10° can still be formed well, which is higher than the design limit of the inclined angle given in the related literature. The tilt angle has a significant effect on the surface roughness of the strut. The microhardness reached to 157 ± 3 HV, and the maximum compressive strength was 58.86 MPa, with the optimal process parameters.

In this study, the characteristics such as the density, up-skin and down-skin roughness and dimensional accuracy of SLMed AlSi10Mg cellular lattice structure were carried. With the optimal geometric parameters, the authors tested microhardness and compressive strength of the cellular lattice structure. The results of this study provide theoretical and experimental basis for the realization of high-quality manufacturing and optimization design of aluminum alloy cellular lattice structure, which will meet more diversified industrial needs.

Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.

The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.

As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.

The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.