Abstract

Magnetic abrasive finishing (MAF) is one of the advanced finishing processes, which produces a high level of surface quality and is primarily controlled by a magnetic field. This paper study the effect of the magnetic abrasive finishing system on the material removal rate (MRR) and surface roughness (Ra) in terms of magnetic abrasive finishing system for eight of input parameters, and three levels according to Taguchi array (L27) and using the regression model to analysis the output (results). These parameters are the (Poles geometry angle, Gap between the two magnetic poles, Grain size powder, Doze of the ferromagnetic abrasive powder, DC current, Workpiece velocity, Magnetic poles velocity, and Finishi

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

   In the present work, tetracycline (TC) was removed from a simulated wastewater through a new photo-anodic oxidation process with a rotating graphite cylinder anode. The effects of current density, pH, rotation speed, and NaCl addition were evaluated. The results confirmed that increasing the current density results in improving the removal of TC. However, increasing the current density beyond 5 mA/cm2 had little effect on TC removal. Results revealed that TC removal using photoanodic oxidation can be achieved at high performance with an initial pH of 5. Increasing or decreasing pH beyond this value has a negative effect on TC removal. Increasing rotation speed gave better performance for TC removal due to the increase in mass t

We report a method of converting non-conductive plastic surfaces into conductive by plating either copper electroless or copper electroplating -carbon black containing bending Agent onto Perspex plastics . Various approaches have been studied in order to comparing properties of the plated copper for two methods such as scanning electron microscopy (SEM),thickness, roughness, porosity, tensile Strength and elongation. The results show that the surface of electroplating was uniform, compact, and continuous and it had an obvious metallic sheen, while the surface of plated copper for electroless for it had many pores. Also observed that the coating was composed of small cells. Thes

Background: Bone augmentation techniques are commonly employed in medical fields. This biomaterial system must be readily available, easily applicable by minimally-invasive technique and able to release an osteoinductive growth factor. Such a system will be able to engineer new bone formation locally at the site of injection. Hyaluronic acid has osteogenic potential that can be exploited not only for repairing bone defects but also for providing transplantable bone for the reconstruction of a variety of bone defects. The aims of this study were to evaluate the effects of Hyaluronic acid gel on bone healing by immunohistochemical estimation of transforming growth factor -beta 3 in experimental and control groups. Materials and methods: Thirt

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed