Electrophoretic deposition and capacitive characteristics of composite electrode materials based on CNTs and ruthenium oxide for planar supercapacitors

In this work, a method for the formation of planar supercapacitors, which combines electrophoretic deposition of composite electrode materials based on CNTs and ruthenium oxide, and laser engraving, is proposed. The features of electrophoretic deposition are considered and the influence of the main technological modes on the morphology and composition of the formed layers of electrode materials is determined. The conducted studies of the electrophysical characteristics of the formed samples confirmed the possibility of producing planar capacitors with a high capacity and their potential applicability for a number of applications in microelectronics.

Kinematic Analysis and Parameter Measurement for Multi-Axis Laser Engraving Machine Tools

Multi-axis Laser Engraving Machine Tools (LEMT) are widely used in precision processing of parts with complex surface. The accuracy of kinematic model and parameter measurement are the key factors determining the processing quality of LEMT. In this paper, a kinematic model of multi-axis LEMT was established based on Homogeneous Transformation Matrix (HTM). Two types of unknown parameters, linkage parameters and positioning parameters, were measured in the presented model. Taking advantage of the characteristics of laser processing, this paper proposed a rapid measurement method of linkage parameters by combining the machine tool motion with the laser marking action. For positioning parameters, this study proposed a non-contact measurement method based on structured light scanner, which can obtain the translation values and the rotation values from the Workpiece Coordinate System (WCS) to the Basic Coordinate System (BCS) simultaneously. After the measurement of two kinds of parameters of a multi-axis LEMT was completed, the processing of a spatial curve was performed and the average contour error was controlled at 15.1 µm, which is sufficient to meet the project requirements.

Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks

Cable-driven parallel robots offer significant advantages in terms of workspace dimensions and payload capability. Their mechanical structure and transmission system consist of light and extendable cables that can withstand high tensile loads. Cables are wound and unwound by a set of motorized winches, so that the robot workspace dimensions mainly depend on the amount of cable that each drum can store. For this reason, these manipulators are attractive for many industrial tasks to be performed on a large scale, such as handling, pick-and-place, and manufacturing, without a substantial increase in costs and mechanical complexity with respect to a small-scale application. This paper presents the design of a planar overconstrained cable-driven parallel robot for quasi-static non-contact operations on planar vertical surfaces, such as laser engraving, inspection and thermal treatment. The overall mechanical structure of the robot is shown, by focusing on the actuation and guidance systems. A novel concept of the cable guidance system is outlined, which allows for a simple kinematic model to control the manipulator. As an application example, a laser diode is mounted onto the end-effector of a prototype to perform laser engraving on a paper sheet. Observations on the experiments are reported and discussed.

ВИЗНАЧЕННЯ ВПЛИВУ ЛАЗЕРНОГО ГРАВІРУВАННЯ НА ГІГІЄНІЧНІ ВЛАСТИВОСТІ НАТУРАЛЬНИХ ШКІР ДЛЯ ВЕРХУ ВЗУТТЯ ТА ШКІРГАЛАНТЕРЕЙНИХ ВИРОБІВ

Purpose. Investigation of the effect of laser engraving on the hygienic properties of nature leather for uppers and leather goods, namely leather Krast, Flotar and Nappa. Methodology. To achieve this goal, the method of scanning electron microscopy (SEM) was used. Hygienic properties of Krast, Flotar and Nappa hides was studied in accordance with ISO 14268: 2008 by determining vapor permeability. Findings. According to the results of the study of the effect of laser radiation on the hygienic properties of nature leather for uppers and leather goods, the nature of changes in the micro- and macrostructure of the dermis under laser engraving and allowable depth and area of laser ablation that do not impair the hygienic properties of these skins and products. With increasing ablation depth to 0,7 mm (50% of the total thickness of the sample), the relative vapor permeability increases in all samples of the studied skins, while the relative vapor permeability for the skin of Krast increased by 5%, Flotar by 13,5% and 9,5% for Nappa. The optimal value of the depth of laser ablation, which does not impair the operational and hygienic characteristics of the products is 25–30% of the skin thickness. On the front surface revealed features of the structure, which are characteristic only for the area of direct action of the laser beam, namely the skin samples Flotar and Nappa have obvious signs of welding collagen fibers. In the area of laser ablation, an increase in the interstructural distances between the bundles of collagen fibers was also detected, which was the result of thermophysical processes of laser radiation. Originality. The determined effect affects the depths and flat laser ablations on the morphological structure of the dermis and the hygienic properties of natural leather for shoe uppers and leather goods. Practical value. The vapor permeability of genuine leather for the uppers of shoes and leather goods under the action of laser radiation was determined. The value of the depth and area of laser ablation for finishing leather products, taking into account the impact on the hygienic properties of products in general.

Technical study of the effect of laser engraving using uArm swift pro robot

<p>Laser engraving is the most non - traditional and efficient working method in the machining of materials of different geometry as compared to conventional methods. The main objective of this study is to determine the impact of uArm swift pro robot operated laser engraving process on a wooden pitch board piece. However, the robot was connected with uArm Studio 1.1.22 software to perform laser engraving operation. For this purpose the effect of process parameters like spot diameter and depth of penetration were investigated with different working length of the robot end effector, measured from wooden pitch board base. Experimental observation method was used to investigate the formation of deep and light engraving pattern on the pitch board surface by measuring penetration depth and spot diameter in suitable condition. The result obtained from the experiment and statistical parameters showed a new dimension to find a suitable working length of the robot assisted laser nozzle where the laser penetration effect was clearly perceptible for the wooden material.</p>

Classification of Microscopic Laser Engraving Surface Defect Images Based on Transfer Learning Method

Microscopic laser engraving surface defect classification plays an important role in the industrial quality inspection field. The key challenges of accurate surface defect classification are the complete description of the defect and the correct distinction into categories in the feature space. Traditional classification methods focus on the terms of feature extraction and independent classification; therefore, feed handcrafted features may result in useful feature loss. In recent years, convolutional neural networks (CNNs) have achieved excellent results in image classification tasks with the development of deep learning. Deep convolutional networks integrate feature extraction and classification into self-learning, but require large datasets. The training datasets for microscopic laser engraving image classification are small; therefore, we used pre-trained CNN models and applied two fine-tuning strategies. Transfer learning proved to perform well even on small future datasets. The proposed method was evaluated on the datasets consisting of 1986 laser engraving images captured by a metallographic microscope and annotated by experienced staff. Because handcrafted features were not used, our method is more robust and achieves better results than traditional classification methods. Under five-fold-validation, the average accuracy of the best model based on DenseNet121 is 96.72%.