Fuzzy Logic-Based Model for Predicting Surface Roughness of Friction Drilled Holes
01 Jan 2020-pp 251-260
TL;DR: Three-dimensional surface plots are developed using this fuzzy model for the prediction of surface roughness of drilled holes in the FD process to reveal the influence of individual process parameters on the surface ambiguities.
Abstract: The nontraditional hole-making process Friction Drilling (FD) receives major attention nowadays because of its operational efficiency in terms of unpolluted, chipless hole making and in fact, the holes are drilled in single step. It is a cumbersome and challenging task to predict surface finish of the work material in the final stages of operation. This difficulty arises because of nonlinear interactions between the process parameters and nonuniform nature of the heat caused by friction which occurred between the conical drill bit rotating at high speed and the workpiece. Since this process is having ambiguities and uncertainties, a model based on fuzzy logic has been developed for the prediction of surface roughness of drilled holes in the FD process. Operating parameters such as rotational speed of the spindle, feed rate, and workpiece temperature are the three membership functions chosen to propose this fuzzy model. These functions are assigned for each input of the model. This fuzzy logic model is verified by two firsthand set of parameter values. The results opine that the established fuzzy model is well in agreement with the investigational data with the maximum deviation of 3.81%. Furthermore, three-dimensional surface plots are developed using this fuzzy model to reveal the influence of individual process parameters on the surface ambiguities. The outcomes of the study attest that the three-dimensional surface plots are much useful for selecting input parameter combinations to achieve the required surface roughness.
Citations
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TL;DR: In this article , the authors used the Smoothed Particle Galerkin (SPG) method for simulation of HX220 sheet metal and found that the results were in high agreement.
Abstract: Abstract As a cost-effective hole production technique, friction drilling is widely used in industrial and automotive manufacturing. Compared with the traditional bolted connection, it enables the fastening of thin metal sheets and thin-walled tubular profiles. Friction drilling results in higher thread length and joint strength, thus better fulfilling the demand for lightweight structures. However, in the numerical simulation of friction drilling, the traditional finite element method encounters difficulties caused by the extreme deformation and complex failure of the material. A large number of elements are usually deleted due to the failure criterion, which significantly reduces the solution accuracy. The development of meshless methods over the past 20 years has alleviated this problem. Especially the Smoothed Particle Galerkin (SPG) method proposed in recent years and incorporating a bond-based failure mechanism has been shown to be advantageous in material separation simulations. It does not require element removal and can continuously evolve each particle's information such as strain and stress after the material failure. Therefore, the SPG method was used in this research for the simulation of frictional drilling of HX220 sheet metal. First the particle distance and the friction coefficient were varied to investigate the applicability of the SPG method to the friction drilling process. Predicted and experimental results were compared and found to be in high agreement. Furthermore, the influence of input parameters, such as sheet thickness, feed rate and rotational speed, on axial force as well as torque of the tool and the surface temperature of the workpiece during friction drilling was investigated numerically.
References
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01 May 1993
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >
15,085Â citations
TL;DR: This paper reviews the application of neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization to four machining processes—turning, milling, drilling, and grinding.
Abstract: Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.
327Â citations
TL;DR: In this article, the microstructures and indentation hardness changes in the friction drilling of carbon steel, alloy steel, aluminum, and titanium were analyzed and shown that materials with different compositions and thermal properties affect the selection of friction drilling process parameters, the surface morphology of the bore, and the development of a highly deformed layer adjacent to the bore surface.
Abstract: Friction drilling, also called thermal drilling, is a novel sheet metal hole-making process. The process involves forcing a rotating, pointed tool through a sheet metal workpiece. The frictional heating at the interface between the tool and workpiece enables the softening, deformation, and displacement of work-material and creates a bushing surrounding the hole without generating chip or waste material. The bushing can be threaded and provides the structural support for joining devices to the sheet metal. The research characterizes the microstructures and indentation hardness changes in the friction drilling of carbon steel, alloy steel, aluminum, and titanium. It is shown that materials with different compositions and thermal properties affect the selection of friction drilling process parameters, the surface morphology of the bore, and the development of a highly deformed layer adjacent to the bore surface.
96Â citations
TL;DR: In this article, tungsten carbide drill with and without coating were employed to make holes in AISI 304 stainless steel, which is known to have high ductility, low thermal conductivity and great hardness.
Abstract: Friction drilling utilizes the heat generated from the friction between the tool and the thin workpiece to form a bush for fixtures such as screw threads in plastic deformation process. This process produces no chip, shortens the time required for hole-making and incurs less tool wear, thus lengthening the service life of the drill. In this study, tungsten carbide drills with and without coating were employed to make holes in AISI 304 stainless steel, which is known to have high ductility, low thermal conductivity and great hardness. TiAIN and AlCrN were coated onto the drill surface by physical vapor deposition (PVD). Performance of coated and uncoated cutting tools was examined for drillings made under different spindle speeds. Changes in relationship between drill surface temperature, tool wear and axial thrust force during machining were also explored. Experimental results reveal that lubricating effect of the coating and low thermal conductivity of AlCrN caused AlCrN-coated drill to produce the highest surface temperature but the lowest axial thrust force with the least tool wear. However, the difference in performance between coated and uncoated drills diminished with increase in number of holes drilled.
92Â citations
TL;DR: In this article, the authors studied the mechanical and thermal aspects of friction drilling and developed two models to predict the distance of tool travel before the workpiece reaches the 250 C threshold temperature that is detectable by an infrared camera.
Abstract: Friction drilling is a nontraditional hole-making process. A rotating conical tool is applied to penetrate a hole and create a bushing in a single step without generating chips. Friction drilling relies on the heat generated from the frictional force between the tool and sheet metal workpiece to soften, penetrate, and deform the work-material into a bushing shape. The mechanical and thermal aspects of friction drilling are studied in this research. Under the constant tool feed rate, the experimentally measured thrust force and torque were analyzed. An infrared camera is applied to measure the temperature of the tool and workpiece. Two models are developed for friction drilling. One is the thermal finite element model to predict the distance of tool travel before the workpiece reaches the 250 C threshold temperature that is detectable by an infrared camera. Another is a force model to predict the thrust force and torque in friction drilling based on the measured temperature, material properties, and estimated area of contact. The results of this study are used to identify research needs and build the foundation for future friction drilling process optimization.
86Â citations