The science of the total environment

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Progress in additive manufacturing on new materials: A review

Fused deposition modelling (FDM) is one of the fastest-growing additive manufacturing methods used in printing fibre-reinforced composites (FRC). The performances of the resulting printed parts are limited compared to those by other manufacturing methods due to their inherent defects. Hence, the effort to develop treatment methods to overcome these drawbacks has accelerated during the past few years. The main focus of this study is to review the impact of those defects on the mechanical performance of FRC and therefore to discuss the available treatment methods to eliminate or minimize them in order to enhance the functional properties of the printed parts. As FRC is a combination of polymer matrix material and continuous or short reinforcing fibres, this review will thoroughly discuss both thermoplastic polymers and FRCs printed via FDM technology, including the effect of printing parameters such as layer thickness, infill pattern, raster angle and fibre orientation. The most common defects on printed parts, in particular, the void formation, surface roughness and poor bonding between fibre and matrix, are explored. An inclusive discussion on the effectiveness of chemical, laser, heat and ultrasound treatments to minimize these drawbacks is provided by this review.

FDM-Based 3D Printing of Polymer and Associated Composite: A Review on Mechanical Properties, Defects and Treatments.

Stereolithography is a solid freeform technique (SFF) that was introduced in the late 1980s Although many other techniques have been developed since then, stereolithography remains one of the most powerful and versatile of all SFF techniques It has the highest fabrication accuracy and an increasing number of materials that can be processed is becoming available In this paper we discuss the characteristic features of the stereolithography technique and compare it to other SFF techniques The biomedical applications of stereolithography are reviewed, as well as the biodegradable resin materials that have been developed for use with stereolithography Finally, an overview of the application of stereolithography in preparing porous structures for tissue engineering is given

A review on stereolithography and its applications in biomedical engineering

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

https://www.mdpi.com/2223-7747/8/11/468/pdf

Plant Disease Detection and Classification by Deep Learning.

A comparison of traditional manufacturing vs additive manufacturing, the best method for the job

Omni-directional is used to describe the ability of a system to move instantaneously in any direction from any configuration. Omnidirectional robotic platforms have vast advantages over a conventional design in terms of mobility in congested environments. They are capable of easily performing tasks in environments congested with static and dynamic obstacles and narrow aisles. These environments are commonly found in factory workshops offices, warehouses, hospitals and elderly care facilities.

/pdf/improved-mecanum-wheel-design-for-omni-directional-robots-q1mh2cat9t.pdf

Improved Mecanum Wheel Design for Omni-directional Robots

Additive manufacturing (AM) is rapidly evolving as the most comprehensive tool to manufacture products ranging from prototypes to various end-user applications. Fused filament fabrication (FFF) is the most widely used AM technique due to its ability to manufacture complex and relatively high strength parts from many low-cost materials. Generally, the high strength of the printed parts in FFF is attributed to the research in materials and respective process factors (process variables, physical setup, and ambient temperature). However, these factors have not been rigorously reviewed for analyzing their effects on the strength and ductility of different classes of materials. This review systematically elaborates the relationship between materials and the corresponding process factors. The main focus is on the strength and ductility. A hierarchical approach is used to analyze the materials, process parameters, and void control before identifying existing research gaps and future research directions.

https://www.mdpi.com/1996-1944/12/10/1664/pdf

Effect of Material and Process Specific Factors on the Strength of Printed Parts in Fused Filament Fabrication: A Review of Recent Developments.

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

Johan Potgieter

Papers

Plant Disease Detection and Classification by Deep Learning.

A comparison of traditional manufacturing vs additive manufacturing, the best method for the job

Improved Mecanum Wheel Design for Omni-directional Robots

Effect of Material and Process Specific Factors on the Strength of Printed Parts in Fused Filament Fabrication: A Review of Recent Developments.

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers