Browsing by Author "Pimenov, Danil Yu"

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    Chapter 5: Comprehensive study on tool wear during machining of fiber-reinforced polymeric composites
    (Springer, 2020-12-23) Ismail, Sikiru Oluwarotimi; Sarfraz, Shoaib; Niamat, Misbah; Mia, Mozammel; Gupta, Munish Kumar; Pimenov, Danil Yu; Shehab, Essam
    The use of fiber reinforced polymeric (FRP) composites has increased rapidly, especially in many manufacturing (aerospace, automobile and construction) industries. The machining of composite materials is an important manufacturing process. It has attracted several studies over the last decades. Tool wear is a key factor that contributes to the cost of the machining process annually. It occurs due to sudden geometrical damage, frictional force and temperature rise at the tool-work interaction region. Moreover, tool wear is an inevitable, gradual and complex phenomenon. It often causes machined-induced damage on the workpiece/FRP composite materials. Considering the geometry of drill, tool wear may occur at the flank face, rake face and/or cutting edge. There are several factors affecting the tool wear. These include, but are not limited to, drilling parameters and environments/conditions, drill/tool materials and geometries, FRP composite compositions and machining techniques. Hence this chapter focuses on drilling parameters, tool materials and geometries, drilling environments, types of tool wear, mechanisms of tool wear and methods of measurement of wear, effects of wear on machining of composite materials and preventive measures against rapid drill wear. Conclusively, some future perspectives or outlooks concerning the use of drill tools and their associated wears are elucidated, especially with the advancement in science and technology
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    Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm
    (Springer, 2021-03-06) Valle Tomaz, Italo do; Colaço, Fernando Henrique Gruber; Sarfraz, Shoaib; Pimenov, Danil Yu; Gupta, Munish Kumar; Pintaude, Giuseppe
    Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.65