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Search Results: 1 - 10 of 5115 matches for " machining parameters "
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Characterizing Surface Defects of Solid Wood of Dark Red Meranti (Shorea sp.), Melunak (Pentace sp.) and Rubberwood (Hevea brasiliensis) in Planing Process
S.R. Farrokhpayam,J. Ratnasingam,E.S. Bakar,S.H. Tang
Journal of Applied Sciences , 2010,
Abstract: The machining parameters affect surface quality and usually, wood planing process is heavily influenced by external parameters. External parameters concern mainly the machining process parameters such as: the feeding speed, the depth of cut, the cutting tool diameter and geometry, the cutting geometry, the cutting technique (up or down milling). This study was carried out to determine the planing properties of naturally grown Melunak (Pentace sp.), Dark Red Meranti (Shorea sp.) and Rubberwood (Hevea brasiliensis). Some machining defects such as fuzzy grain, torn grain and chip marks often occur in lumbers at the planing process and it decreases the machining yield. To understand and optimize the planing characteristics of this wood species, a series of experiments were carried out using a weinig Unimate 23E moulder (cutter-head rpm of 6000, cutter diameter 120 mm) to produce machined surface with differing depth of cut ranging from 0.8 to 2.4 mm, by altering the feed rate from 8 to 16 m min-1 according to ASTM D 1666-87. Based on the preliminary results of this study, the best results were obtained at 0.8 mm of depth of cut and feed rate of 8 m min-1 for Dark Red Meranti. While, the poorest results was on Melunak wood at 2.4 mm of depth of cut and 16 m min-1 of feed speed. This research also revealed that the combination of feed rate, depth of cut and wood species used had no significant effect on the surface quality of samples.
Effect Of Different Machining Parameters On Machinability Of Different Materials
International Journal of Engineering Science and Technology , 2011,
Abstract: It is important to view machining, as well as all manufacturing operations, as a system consisting of the workpiece, the tool and the machine. Machinability is the relative susceptibility of a material to the machining process. The ease with which a metal can be machined is one of the principle factors affecting a product's utility, quality cost. The usefulness of a means to predict machinability is obvious. Depending on the application, machinability may be seen in terms of tool wear rate, total power consumption, attainable surface finish or several other benchmarks. In this study ,the effects of variable machining parameters on machinability were investigated.
Development of a Tool Cost Optimization Model for Stochastic Demand of Machined Products  [PDF]
Francisco G. Pantoja, Victor Songmene, Jean-Pierre Kenné, Oluwole A. Olufayo, Michael Ayomoh
Applied Mathematics (AM) , 2018, DOI: 10.4236/am.2018.912091
Cutting tool management in manufacturing firms constitutes an essential element in production cost optimization. In order to optimize the cutting tool stock level while concurrently minimizing production costs, a cost optimization model which considers machining parameters is required. This inclusive modeling consideration is a major step towards achieving effectiveness of cutting tool management policy in manufacturing systems with stochastic driven policies for tool demand. This paper presents a cost optimization model for cutting tools whose utilization level is assumed to be optimized in respect of the machining parameters. The proposed cost model in this research incorporated the effects of diversified machining costs ranging from operational through machining, shortage, holding, material and ordering costs. The machining of parts was assumed to be a single cutting operation. Holt-Winters forecasting technique was used to create a stochastic demand dataset for a test scenario in the production of a high-end automotive part. Some numerical examples used to validate the developed model were implemented to illustrate the optimal machining and tool inventory conditions. Furthermore, a sensitivity analysis was carried out to study the influence of varying production parameters such as: machine uptime, demand and cutting parameters on the overall production cost. The results showed that a desired low level of tool storage and holding costs were obtained at the optimal stock levels. The machining uptime had a significant influence on the total cost while tool life and cutting feed rate were both identified as the most influential cutting variables on the total cost. Furthermore, the cutting speed rate had a marginal effect on both costs and tool life. Other cost variables such as shortage and tool costs had significantly low effect on the overall cost. The output trend showed that the feed rate is the most significant cutting parameter in the machining operation, hence influencing the cost the most. Also, machine uptime and demand significantly influenced the total production cost.
Definición de Condiciones para Taladrado Usando un Enfoque Basado en Lógica Difusa
Durán,Orlando M; Bavestrello,Italo;
Información tecnológica , 2006, DOI: 10.4067/S0718-07642006000500004
Abstract: this study presents a model to optimize the selection of cutting parameters in drilling operations. the model developed, based on fuzzy logic techniques, considers as input data characteristics such as workpiece hardness, cutting tool type, and also the condition of the machine-tool. then the fuzzy sets were specified, in reference to input and output data of the model, type of function applicable, and the appropriate defuzzification method. an adjustment mechanism to handle situations where the recommended ranges of cutting conditions overlap, is proposed. the results showed that the model constructed behaved as expected, and that the fuzzy logic was useful in the definition of the cutting parameters when there was no conclusive information on certain characteristics of the process.
Modeling of the number of stubble stuck elements after abrasive jet machining-processing
K. Pietnicki,E. Wo?owiec,L. Klimek
Archives of Foundry Engineering , 2011,
Abstract: Sticking of abrasive grains into the surface is the effect of abrasive jet machining processing. For prosthetic works burnt the clay, it may deteriorate the quality of metal-clay connection. Parameters of sandblasting have the significant influence on the amount of stubble elements stuck into the base. The aim of the research is examining possibilities of applying neuron networks for modeling this process. Examples were sandblasted using alternating parameters (pressure of working factor, angle of sandblasting, size of grains). As a next the amount of stubble elements stuck into the base were calculated. Study results allowed to work out a set of 180 standardized teaching patterns. Next, a group of 150 randomly selected neuron networks was submitted to teaching process. The aim was to select net architecture with the highest adjustment for discussed issue. Finally, a MLP (Multi Layer Perception) was chosen. Training process of the net involved presentation of process entrance parameters to the net, followed by presentation of researched proportional partaking of stuck elements. In following training cycles the error was decreasing systematically. After finishing the training, the average medium error between the real measurement and the one anticipated by the net was about 5 per cent. Additional number of abrasive jet machining processing was done to verification experiments. As the study shows, designed artificial neuron net stimulates real dependencies appearing during abrasive jet machining-processing in a proper way and can be used for estimating proportional part of grains stuck into base after this processing. It turns out that this phenomenon can be modeled, and its model can be used in a practical way without using mathematical or physical equations.
Influence of machining parameters on fatigue endurance limit of AISI 4140 steel
Lopes, Karina S. S.;Sales, Wisley Falco;Palma, Ernani S.;
Journal of the Brazilian Society of Mechanical Sciences and Engineering , 2008, DOI: 10.1590/S1678-58782008000100011
Abstract: the general purpose of this research is to study the influence of commercial machining parameters on fatigue limits of steels. specifically in this work, the influence of cutting speeds, depth of cut, feed rate and residual stresses of turned surfaces of aisi 4140 steel specimens on fatigue strength were analyzed. in some specimens, the residual stress was eliminated by heat treatment. the fatigue experiments were carried out at room temperature, applying a cyclical frequency of 58hz, with mean stress equal to zero (r=-1), on a rotating-bending fatigue testing machine of the constant bending moment type. it was used the staircase (or up-and-down) method to determine the fatigue limit of the specimens.
Die-sinking electrical discharge machining of a high-strength copper-based alloy for injection molds
Amorim, F. L.;Weingaertner, W. L.;
Journal of the Brazilian Society of Mechanical Sciences and Engineering , 2004, DOI: 10.1590/S1678-58782004000200004
Abstract: high-strength copper alloys are used as materials for injection molding tools or as cores and inserts in steel molds because of their high thermal conductivity, corrosion and wear resistance. unfortunately, there is little technological knowledge on the electrical discharge machining (edm) of copper-beryllium astm c17200 alloy. in this work, rough and finish machining conditions were tested using copper and tungsten-copper as materials for the electrodes. cross-sectional micrographic and hardness examinations as well as surface roughness measurements were also carried out on workpieces after machining in order to study the thermally affected zones. appropriate parameters settings for edm of the investigated alloy are suggested.
International Journal of Engineering Science and Technology , 2010,
Abstract: Electrical discharge machining (EDM) is widely used process in the production of mould / dies, aerospace, automobile and electronics industries where intricate complex shapes need to be machined in very hard materials. The selection of the AISI 304 stainless steel was made taking into account its use in almost all industrial applicationsfor approximately 50% of the world’s stainless steel production and consumption. In this work, a study has been carried out on the influence of four design factors: current, open-circuit voltage, servo and dutycycle over material removal rate, tool wear rate, surface roughness and hardness on the die-sinking electrical discharge machining of AISI 304 stainless steel. This has been done using design of experiments (DOE), which allows us to carry out theabove-mentioned analysis performing a relatively small number of experiments. In this case, a 3*24-1 mixed level factorial design, whose resolution is V, has been selected due to the number of factors considered in the study. The resolution of this mixed factorial design allows us to estimate all the main effects, two-factor interactions and pure quadratic effects of the four design factors selected to perform this study.
ВЛИЯНИЕ УЛЬТРАМЕЛКОЗЕРНИСТОЙ СТРУКТУРЫ ВОЛЬФРАМА НА ВЫСОКОСКОРОСТНОЕ АНОДНОЕ РАСТВОРЕНИЕ В СРАВНЕНИИ С КРУПНОЗЕРНИСТЫМ СОСТОЯНИЕМ Influence of ultrafine-grained structure of tungsten on high-speed anode dissolution in comparison with the coarse-grained condition
Н. А. Амирханова,П. А. Белов,А. Р. Ганеев
Vestnik UGATU , 2012,
Abstract: Исследованы закономерности высокоскоростного анодного растворения ультра мелкозернистого и крупнозернистого вольфра-ма применительно к ЭХРО. Определены выходные параметры ЭХО: линейная скорость растворения, выход по току, коэффи-циент локализации и высота микронеровностей для крупнозернистого и мелкозернистого состояний вольфрама. Regularity of high-speed anodic dissolution of ultrafine-grained and coarse-grained tungsten with reference to electrochemical dimensional machining was investigated. Target parameters of electrochemical machining are spotted: peripheral speed of dissolution, a current efficiency, coefficient of localization and altitude of micro unevenness for coarse-grained and compact-grained states of tungsten.
GA based CNC turning center exploitation process parameters optimization
Z. Car,B. Barisic,M. Ikonic
Metalurgija , 2009,
Abstract: This paper presents machining parameters (turning process) optimization based on the use of artificial intelligence. To obtain greater efficiency and productivity of the machine tool, optimal cutting parameters have to be obtained. In order to find optimal cutting parameters, the genetic algorithm (GA) has been used as an optimal solution finder. Optimization has to yield minimum machining time and minimum production cost, while considering technological and material constrains.
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