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This study develops a cost model for the additive manufacturing (AM)-produced spare parts supply chain in the automotive industry. Moreover, we evaluate the economic feasibility of AM for slow-moving automotive spare parts by comparing the costs of the traditional manufacturing (TM) spare parts supply chain (SPSC) with centralized, outsourced AM SPSC. Data from a multiple case study of an OEM in the automotive industry regarding SPSC is utilized. The supply chain costs of 14 individual spare parts were analyzed, and the total SPSC cost for the AM and TM, were compared. Three of the fourteen parts showed potential for cost-savings, if they were produced with AM instead of TM. In this context, AM polymer parts showed greater potential than metal to replace TM as the more economical option of manufacturing from a total supply chain cost perspective. This study shows that the AM competitiveness to TM, from a financial perspective, increases for spare parts with low demand, high minimum order quantity, and high TM production price. The SPSC cost model included: cost of production, transport, warehousing, and service costs. This study contributes to the emerging field of part identification for AM and the existing literature regarding cost modeling in SPSCs.

To address the challenge of recognizing and estimating the position of untextured stacked parts, which are common in industrial environments, this study proposes an integrated approach that incorporates the YOLOv7 target detection algorithm and point cloud alignment techniques. First, the YOLOv7 algorithm is utilized to quickly identify and locate the 2D position of the part, followed by a mapping technique to transform the 2D region of interest (ROI) into the corresponding 3D point cloud region. In the point cloud processing stage, depth threshold segmentation and Euclidean clustering segmentation methods are used to separate the target part from the background and other interfering objects. The pose estimation stage uses the SAC-IA algorithm for coarse alignment, followed by an improved ICP algorithm that introduces an adaptive weighting mechanism and a global optimization strategy for fine alignment to obtain the final 6D pose of the part. The improved strategy significantly optimizes the point-pair selection and alignment process and enhances the robustness and accuracy of the algorithm. Through experimental validation on publicly available part piece datasets, the results show that the part identification and pose estimation method proposed in this study can realize fast and accurate identification and pose estimation of different shapes, non-textured, and scattered stacked parts, where the position error can reach up to 1mm and the angular error within 1°, which meets the requirements of practical applications.

Identifying relevant plant parts is one of the most significant tasks in the pharmaceutical industry. Correct identification minimizes the risk of mis-identification, which might have unfavorable effects, and it ensures that plants are used medicinally. Traditional methods for plant part identification are often time-consuming and require specific expertise. This study proposed a Depth-wise Separable Convolutional Neural Network (DWS-CNN) to enhance the accuracy of medicinal plant part identification. Furthermore, we incorporated the tuned pre-trained model s such as VGG16, Res Net-50, and Inception V3 which are designed by Standard convolutional neural network (S-CNN) for comparative purposes. We trained variants of the Standard convolutional neural network (S-CNN) model with high-resolution images of medicinal plant leaves which contains 15,100 leaf images. The study used supervised learning by which leaf images are used as an identity for the other parts of the plants. We used transfer learning to tune training and model parameters. Experimental results showed that our DWS-CNN model achieved better performance compared to S- CNN models, with an accuracy of 99.84% for training data, 99.44% for F1-score and 99.44% for testing data, which improves in both accuracy and training speed. The presence of depth-wise separable convolution and batch normalization at the fully connected layer of the model made the model achieved a good classification performance.

Unique identification of machine parts is critical to production and maintenance, repair and overhaul (MRO) processes in the aerospace industry. Despite recent advances in automating these identification processes, many are still performed manually. This is time-consuming, labour-intensive and prone to error, particularly when dealing with visually similar objects that lack distinctive features or markings or when dealing with parts that lack readable identifiers due to factors such as dirt, wear and discolouration. Automation of these processes has the potential to alleviate these problems. However, due to the high visual similarity of components in the aerospace industry, commonly used object identifiers are not directly transferable to this domain. This work focuses on the challenging component spectrum engine tubes and aims to understand which identification method using only object-inherent properties can be applied to such problems. Therefore, this work investigates and proposes a comprehensive set of methods using 2D image or 3D point cloud data, incorporating digital image processing and deep learning approaches. Each of these methods is implemented to address the identification problem. A comprehensive benchmark problem is presented, consisting of a set of visually similar demonstrator tubes, which lack distinctive visual features or markers and pose a challenge to the different methods. We evaluate the performance of each algorithm to determine its potential applicability to the target domain and problem statement. Our results indicate a clear superiority of 3D approaches over 2D image analysis approaches, with PointNet and point cloud alignment achieving the best results in the benchmark.

Machine vision inspection systems are often used for part classification applications to confirm that correct parts are available in manufacturing or assembly operations. Support vector machines (SVMs) and artificial neural networks (ANNs) are popular choices for classifiers. These supervised classifiers perform well when developed for specific applications and trained with known class images. Their drawback is that they cannot be easily applied to different applications without extensive retuning. Moreover, for the same application, they do not perform well if there are unknown class images. This paper proposes a novel solution to the above limitations of SVMs and ANNs, with the development of a hybrid approach that combines supervised and semi-supervised layers. To illustrate its performance, the system is applied to three different small part identification and sorting applications: (1) solid plastic gears, (2) clear plastic wire connectors and (3) metallic Indian coins. The ability of the system to work with different applications with minimal tuning and user inputs illustrates its flexibility. The robustness of the system is demonstrated by its ability to reject unknown class images. Four hybrid classification methods were developed and tested: (1) SSVM–USVM, (2) USVM–SSVM, (3) USVM–SANN and (4) SANN–USVM. It was found that SANN–USVM gave the best results with an accuracy of over 95% for all three applications. A software package known as FlexMVS for flexible machine vision system was written to illustrate the hybrid approach that enabled easy execution of the image conditioning, feature extraction and classification steps. The image library and database used in this study is available at http://my.me.queensu.ca/People/Surgenor/Laboratory/Database.html.

The article is aimed at solving the problem of aerospace parts identification. A neural network model for part identification was developed. The proposed model consists of three modules: object detection using the YOLO3 model, preprocessing of the selected fragment, and classification of the processed fragment using the VGG19 model. A distinctive feature of the developed model is the use of STL objects for training the VGG19 neural network. To increase the reliability of the classification for each object we used photos made from three angles. The developed model has been tested on the parts of the rotor of a small gas turbine engine. The test was conducted on 100 test cases including 300 photos of parts. To train the neural network, 13,200 images were simulated using STL models. The loss function (categorical cross-entropy) for the training sample was 0.0004, and the classification accuracy was 100%. The accuracy of identification of real photos using the developed model was 97%.

The accurate identification of highly similar sheet metal parts remains a challenging issue in sheet metal production. To solve this problem, this paper proposes an effective mean square differences (EMSD) algorithm that can effectively distinguish highly similar parts with high accuracy. First, multi-level downsampling and rotation searching are adopted to construct an image pyramid. Then, non-maximum suppression is utilised to determine the optimal rotation for each layer. In the matching, by re-evaluating the contribution of the difference between the corresponding pixels, the matching weight is determined according to the correlation between the grey value information of the matching pixels, and then the effective matching coefficient is determined. Finally, the proposed effective matching coefficient is adopted to obtain the final matching result. The results illustrate that this algorithm exhibits a strong discriminative ability for highly similar parts, with an accuracy of 97.1%, which is 11.5% higher than that of the traditional methods. It has excellent potential for application and can significantly improve sheet metal production efficiency.

Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional underground actors; however previous studies overlooked such information as a feature in detecting and classifying malware and in attributing malware to creators. Guided by this insight, we propose a method to improve the performance of Android malware detection by incorporating the creator’s information as a feature and classify malicious applications into similar groups. We developed a system that implements this method in practice. Our system enables fast detection of malware by using creator information such as serial number of certificate. Additionally, it analyzes malicious behaviors and permissions to increase detection accuracy. The system also can classify malware based on similarity scoring. Finally, we showed detection and classification performance with 98% and 90% accuracy, respectively.

Blade serial number identification is one of the key issues in blade tip-timing vibration measurement without once-per-revolution (OPR) sensor. In order to overcome the shortcomings of the existing blade serial number identification methods without OPR sensor, a new identification method of blade serial number based on blade tip clearance is proposed in this paper. The relationship between blade tip-timing data and blade serial number can be identified by the matching relationship between blade tip clearance under static state and dynamic state. According to the finite element simulation and experimental data, the accuracy of the blade serial number identification method based on blade tip clearance is verified by using the OPR sensor method. The results show that in the nonresonant rotation speed region, the method can identify the blade serial number, and the identification result is consistent with the result of the OPR sensor method. In the resonance rotation speed region, when the blade tip clearance change caused by the blade circumferential bending vibration is less than the dispersion of initial blade tip clearance, the method in this paper can accurately identify the blade serial number. Otherwise, the inference method can be used. It provides theoretical support and technical basis for the engineering application of blade tip-timing vibration measurement technology without OPR sensor.

Bend tubes have been widely used in aerospace equipment due to their excellent mechanical properties and chemical properties. However, elliptic deformation will occur in the process of bending, which will reduce the pressure bearing capacity and service life of the bend tube. In order to ensure the production quality of the bend tube, a method of detecting elliptic deformation based on the spine line of the tube is proposed in this article. Firstly, the spine line reconstruction method, which employs scanning points of the tube is proposed. Specifically, a voting points inspired spine line initialization method, and a bend part identification preferred spine line shape analysis method are designed. Secondly, the contour extraction method based on the natural coordinate system of the spine line is studied. Thirdly, the contour integrity assessment method based on the maximum center angle of the missing contour line is proposed. Contours satisfying the integrity will be used for ellipse fitting to determine the ovality of the tube. Finally, various bend tubes and standard elliptical cylinders are used to illustrate the effectiveness of the proposed method. Extensive experimental results show that our proposed method can extract the contour of the bend tube and analyze its deformation in only 7 s, and the ovality error is about 0.039%. Therefore, the method proposed in this paper can quickly and effectively analyze the elliptic deformation of the bend tube and can be used for online detection.