بهينه سازي كيفيت ماشينكاري فولاد6 AISID، با طراحي آزمايش مناسب تجزيه و تحليل تاثير پارامترهاي ماشينكاري

چكيده انگليسي :

Optimization of Machining Quality of AISI D6 with Proper Conducting Test and Analyze the Effect of Machining Parameter Amir Beig Mohammadi a mohammadi@me iut ac ir Date of Submission 2010 3 7 Department of Mechanical Engineering Isfahan University of Technology Isfahan 84156 83111 IranDegree M Sc Language FarsiMohsen Safavi mosafavi@cc iut ac irAbstract AISI D6 is a cold work tool steel that is widely used in cutting drawing deep drawing tools and other similarapplications This steel has a very high wear resistance and very poor machinability With regard to wide usage ofthis steel it is necessary to economize AISI D6 machining process The goal of this research work is to optimizeAISI D6 machining process with regard to machining forces and surface roughness The aim of this optimization isto introduce cutting conditions under which minimum machining forces and surface roughness are achieved Severalexperiments were planned based on full factorial design FFD with using analysis of variance ANOVA and theeffect of machining parameters on surface roughness and machining forces is determined Full factorial experimentis an experiment whose design consists of two or more factors each with discrete possible values or levels andwhose experimental units take on all possible combinations of these levels across all such factors A full factorialdesign may also be called a fully crossed design Such an experiment allows studying the effect of each factor on theresponse variable as well as the effects of interactions between factors on the response variable Then the attempthas been made to model the machining forces and surface roughness through the response surface methodology RSM Response surface methodology RSM explores the relationships between several explanatory variables andone or more response variables The main idea of RSM is to use a sequence of designed experiments to obtain anoptimal response Researchers suggest using a second degree polynomial model to do this They acknowledge thatthis model is only an approximation but use it because such a model is easy to estimate and apply even when littleis known about the process An easy way to estimate a first degree polynomial model is to use a factorial experimentor a fractional factorial designs This is sufficient to determine which explanatory variables have an impact on theresponse variable s of interest Once it is suspected that only significant explanatory variables are left Then a morecomplicated design such as a full factorial design can be implemented to estimate a second degree polynomialmodel which is still only an approximation at best However the second degree model can be used to optimize maximize minimize or attain a specific target for a response At last the optimum cutting condition has beenintroduced with using genetic algorithm GA Genetic algorithm GA is a search technique used in computing tofind exact or approximate solutions to optimization and search problems Genetic algorithms are categorized asglobal search heuristics Genetic algorithms are a particular class of evolutionary algorithms EA that usetechniques inspired by evolutionary biology such as inheritance mutation selection and crossover Furthermore machining process has been simulated using finite element method In this simulation effect of tool nose radius anddepth of cut on radial and cutting forces has been investigated Key wordsAISI D6 cold work tool steel analysis of variance genetic algorithm response surfacemethodology

بيگ محمدي، امير

بهينه سازي كيفيت ماشينكاري فولاد6 AISID، با طراحي آزمايش مناسب تجزيه و تحليل تاثير پارامترهاي ماشينكاري

آناليز واريانس , الگوريتم ژنتيك , روش رويه ي پاسخ