Evaluation of the auto surfacing methods to create a surface body of the mandible model

Designing an anatomical structure for a surgical procedure is not a simple task. It is especially true of the craniofacial area, which consists of bone tissues with very complex geometry. CAD modelers need appropriate knowledge and skills in medicine and technical sciences to fully use the currently available tools in related processes with the reconstruction of the craniofacial areas. The presented preliminary studies are based on four patients treated at the Department of Maxillofacial Surgery. The segmentation process of the mandible model was performed in the ITK SNAP software. The process of generating surface body models was performed in the Auto Surfacing module in Geomagic software using two different methods: organic and mechanical. Then compare both methods for the accuracy of generating a CAD model of the mandible based on a triangle mesh structure in the Focus Inspection and the GOM Inspect software.

Landscape Perspective Distance-Included Angle Shape Distribution Analysis Based on 3D CAD Model Retrieval Algorithm

In view of the results obtained in the retrieval process of the 3D CAD model, which can show the differences in the local feature details of the model, the 3D CAD model retrieval algorithm is introduced into the analysis of the perspective distance-angle shape distribution of the garden landscape in this paper. Random sampling is performed on the surface of the constructed 3D CAD model, combined with the test distance between the sampling point and the neighboring points, and the corresponding garden landscape perspective distance-angle shape distribution characteristics in this area are calculated in order to achieve the similarity of the CAD model high-speed retrieval. Finally, experimental research shows that the algorithm proposed in this paper is better than the overall shape distribution algorithm and the spherical harmonic algorithm in the search performance of the CAD model, and it can effectively improve the recognition ability of the local detailed features of the 3D CAD model.

Identification of particular hydrodynamic parameters for a modular type 4 DOF underwater vehicle by means of CFD method

Purpose The development of robust control algorithms for the position, velocity and trajectory control of unmanned underwater vehicles (UUVs) depends on the accuracy of their mathematical models. Accuracy of the model is determined by precise estimation of the UUV hydrodynamic parameters. The purpose of this study is to determine the hydrodynamic forces and moments acting on an underwater vehicle with complex body geometry and moving at low speeds and to achieve the accurate coefficients associated with them. Design/methodology/approach A three-dimensional (3D) computer-aided design (CAD) model of UUV is designed with one-to-one dimensions. 3D fluid flow simulations are conducted using computational fluid dynamics (CFD) software programme in the solution of Navier Stokes equations for laminar and turbulent flow analysis. The coefficients depending on the hydrodynamic forces and moments are determined by the external flow analysis using the CFD programme. The Flow Simulation k-ε turbulence model is used for the transition from laminar flow to turbulent flow. Hydrodynamic properties such as lift and drag coefficients and roll and yaw moment coefficients are calculated. The parameters are compared with the coefficient values found by experimental methods. Findings Although the modular type UUV has a complex body geometry, the comparative results of the experiments and simulations confirm that the defined model parameters are accurate and close to the actual experimental values. In the proposed k-ε method, the percentage error in the estimation of drag and lifting coefficients is decreased to 4.2% and 8.39%, respectively. Practical implications The model coefficients determined in this study can be used in high-level control simulations which leads to the development of robust real-time controllers for complex-shaped modular UUVs. Originality/value The Lucky Fin UUV with 4 degrees of freedom is a specific design and its CAD model is first extracted. Verification of simulation results by experiments is generally less referenced in studies. However, it provides more precise parameter identification of the model. Proposed study offers a simple and low-cost experimental measurement method for verification of the hydrodynamic parameters. The extracted model and coefficients are worthwhile references for the analysis of modular type UUVs.

Comparative modal analysis for a twisted beam axle for a hybrid vehicle

In this article it is presented a comparative analysis of natural mode frequencies for a non-powered rear axle used to fit mass production vehicle and for a similar rear axle derived from the first one to be used to fit a hybrid powered vehicle. The CAD model of the axle and the computed natural mode frequencies were realised using CATIA V5. For calculation, finit element method was used.

Automating Aircraft Scanning For Inspection With A UAV And Reinforcement Learning Technique

Visual inspections of aircraft exterior surface are usually required in aircraft maintenance routine. It becomes a trend to use mobile robots equipped with sensors to perform automatic inspections as a replacement of manual inspections which are time-consuming and error-prone. The sensed data such as images and point cloud can be used for further defect characterization leveraging the power of machine learning and data science. In such a robotic inspection procedure, a precise digital model of the aircraft is required for planning the inspection path, however, the original CAD model of the aircraft is often inaccessible to aircraft maintenance shops. Thus, sensors such as 3D Laser scanners and RGB-D (Red, Green, Blue, and Depth) cameras are used because of their capability of generating a 3D model of an interested object in an efficient manner. This paper presents a two-stage approach of automating aircraft scanning with a UAV (Unmanned Aerial Vehicle) equipped with an RGB-D camera for reconstructing a digital replica of the aircraft when its original CAD model is not available. In the first stage, the UAVcamera system follows a predefined path to quickly scan the aircraft and generate a coarse model of the aircraft. Then, a full-coverage scanning path is computed based on the coarse model of the aircraft. In the second stage, the UAV-Camera system follows the computed path to closely scan the aircraft for generating a dense and precise model of the aircraft. We solved the Coverage Path Planning (CPP) problem for the aircraft scanning using Monte Carlo Tree Search (MCTS) which is a reinforcement learning technique. We also implemented the Max-Min Ant System (MMAS) strategy, a population-based optimization algorithm, to solve the CPP problem and demonstrate the effectiveness of our approach.

Optimal Design of the Six-Wheel Steering System with Multiple Steering Modes

Vehicles will face different working conditions during the use, and different working conditions have different requirements for vehicle functions, which results in many subdivided models. An off-road vehicle is a subdivision model produced to adapt to complex road conditions. In order to adapt to complex road conditions, vehicles should have a good passing ability, small size, and good flexibility. The six-wheeled vehicle has both good passing ability and small volume, which is the best choice for off-road vehicles. The design of the steering system becomes the key step to improve the flexibility of the six-wheeled vehicle. This paper mainly designs an independent steering system for a six-wheel electric vehicle with higher flexibility. The system is designed for six-wheel electric vehicles driven by six in-wheel motors. It mainly includes mechanical steering system and electronic control steering system. Both mechanical steering systems and electronic control steering system have multiple steering modes. Firstly, this paper introduces the various steering modes of the mechanical steering system and the electronic control steering system. Secondly, a CAD model is established by using the software Solidworks, and the system structure is introduced in detail combined with the CAD model. Finally, a kinematics model is established and calculated. The calculation results showed that the steering system can significantly improve the flexibility of the vehicle, so that the vehicle can complete the steering stably and quickly on complex road sections.

The Establishment of Transformer-Based Computer-Aided Diagnosis Model to Improve the Classification Consistency of BI-RADS-US3–5 Nodules Among Radiologists: A Multi-Center Study

Background: Significant differences exist in classification outcomes for radiologists using ultrasonography-based breast imaging-reporting and data systems for diagnosing category 3–5 (BI-RADS-US 3–5) breast nodules, due to a lack of clear and distinguishing image features. As such, this study investigates the use of a transformer-based computer-aided diagnosis (CAD) model for improved BI-RADS-US 3–5 classification consistency.Methods: Five radiologists independently performed BI-RADS-US annotations on a breast ultrasonography image set collected from 20 hospitals in China. The data were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity, specificity, and accuracy were calculated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS-US classification results for the sampling test set, provided by CAD, to determine whether classification consistency (the kappa value)，sensitivity, specificity, and accuracy had improved.Results: Classification accuracy for the CAD model applied to the test set was 95.7% for category 3 nodules, 97.6% for category 4A nodules, 95.60% for category 4B nodules, 94.2% for category 4C nodules, and 97.5% for category 5 nodules. Adjustments were made to 1,583 nodules, as 905 were classified to a higher category and 678 to a lower category in the sampling test set. As a result, the accuracy, sensitivity, and specificity of classification by each radiologist improved, with the consistency (kappa values) for all radiologists increasing to >0.60.Conclusions: The proposed transformer-based CAD model improved BI-RADS-US 3–5 nodule classification by individual radiologists and increased diagnostic consistency.

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study

Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists’ diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.