6170曲軸加工工藝及其夾具設計【鉆和銑2套夾具】【含7張CAD圖紙】【GJ系列】

喜歡就充值下載吧。資源目錄里展示的全都有，下載后全都有，所見即所得，CAD圖紙均為高清圖可自行編輯，文檔WORD都可以自己編輯的哦，有疑問咨詢QQ:1064457796,課題后的【XX系列】為整理分類用，與內(nèi)容無關，請忽視 畢業(yè)設計中期檢查表系 部： 工程技術系 學生姓名學 號年級專業(yè)及班級指導教師及職稱畢業(yè)論文（設計）題目6170曲軸加工工藝及其夾具設計畢業(yè)論文（設計）工作進度已完成的主要內(nèi)容尚需解決的主要問題1. 關鍵部件的設計和計算基本完成2. 夾具裝配圖，零件圖完成3. 說明書基本完成1. 裝配圖任需修改2. 零件圖任需修改3. 說明書格式需修改指導教師意見 指導教師簽名： 年 月 日檢查（考核）小組意見檢查小組組長簽名： 年 月 日畢業(yè)設計任務書學生姓名學 號年級專業(yè)及班級指導教師及職稱20 年 月 日填 寫 說 明一、畢業(yè)論文（設計）任務書是學校根據(jù)已經(jīng)確定的畢業(yè)論文（設計）題目下達給學生的一種教學文件，是學生在指導教師指導下獨立從事畢業(yè)論文（設計）工作的依據(jù)。此表由指導教師填寫。二、此任務書必需針對每一位學生，不能多人共用。三、選題要恰當，任務要明確，難度要適中，份量要合理，使每個學生在規(guī)定的時限內(nèi)，經(jīng)過自己的努力，可以完成任務書規(guī)定的設計研究內(nèi)容。四、任務書一經(jīng)下達，不得隨意更改。五、各欄填寫基本要求（一）畢業(yè)論文（設計）選題來源、選題性質(zhì)和完成形式：請把合適的對應選項前的“”涂黑，科研課題請注明課題項目和名稱，項目指“國家青年基金”等。（二）主要內(nèi)容和要求：1工程設計類選題明確設計具體任務，設計原始條件及主要技術指標；設計方案的形成（比較與論證）；該生的側重點；應完成的工作量，如圖紙、譯文及計算機應用等要求。2實驗研究類選題明確選題的來源，具體任務與目標，國內(nèi)外相關的研究現(xiàn)狀及其評述；該生的研究重點，研究的實驗內(nèi)容、實驗原理及實驗方案；計算機應用及工作量要求，如論文、文獻綜述報告、譯文等。3文法經(jīng)管類論文明確選題的任務、方向、研究范圍和目標；對相關的研究歷史和研究現(xiàn)狀簡要介紹，明確該生的研究重點；要求完成的工作量，如論文、文獻綜述報告、譯文等。（三）主要參考文獻與外文資料：在確定了畢業(yè)論文（設計）題目和明確了要求后，指導教師應給學生提供一些相關資料和相關信息，或劃定參考資料的范圍，指導學生收集反映當前研究進展的近13年參考資料和文獻。外文資料是指導老師根據(jù)選題情況明確學生需要閱讀或翻譯成中文的外文文獻。（四）畢業(yè)論文（設計）的進度安排1設計類、實驗研究類課題實習、調(diào)研、收集資料、方案制定約占總時間的20%；主體工作，包括設計、計算、繪制圖紙、實驗及結果分析等約占總時間的50%，撰寫初稿、修改、定稿約占總時間的30%。2文法經(jīng)管類論文實習、調(diào)研、資料收集、歸檔整理、形成提綱約占總時間的60%；撰寫論文初稿，修改、定稿約占總時間的40%。六、各欄填寫完整、字跡清楚。應用黑色簽字筆填寫，也可使用打印稿，但簽名欄必須相應責任人親筆簽名。畢業(yè)論文（設計）題目6170曲軸加工工藝及其夾具設計選題來源（ ）結合科研課題 課題名稱： （）生產(chǎn)實際或社會實際 （ ）其他 選題性質(zhì)（ ）基礎研究 （）應用研究 （ ）其他 題目完成形式（ ）畢業(yè)論文 （）畢業(yè)設計 （ ）提交作品，并撰寫論文 主要內(nèi)容和要求曲軸是發(fā)動機的重要零件之一，其加工精度直接影響到發(fā)動機的各項性能。“6170曲軸加工工藝及其夾具設計”選題來源于生產(chǎn)實際現(xiàn)場，具有較強的實際應用價值。該課題需設計人綜合運用大學所學課程去分析研究和解決工程設計中遇到的一些工程技術問題。主要技術參數(shù)：1、加工工藝規(guī)程的制訂符合實際生產(chǎn)情況（生產(chǎn)綱領：成批大量）2、夾具設計滿足工廠加工技術要求，操作方便簡單、安全。3、適當考慮機動操作等。畢業(yè)設計主要內(nèi)容：1、6170曲軸的加工工藝規(guī)程編制2、影響曲軸加工加工質(zhì)量的因素及主要對策（典型工序加以分析、研究）3、粗銑兩端面夾具設計4、鉆斜油孔夾具設計研究方法：工廠實習-收集資料-歸納分析-粗、精基準確定-加工工藝路線擬定-工藝規(guī)程擬定-影響曲軸加工加工質(zhì)量的因素及主要對策（典型工序加以分析、研究設計）-指定工序夾具總裝圖-繪制總裝圖、零件圖-編寫說明書畢業(yè)設計須完成任務：1、3張以上0號圖紙（全部CAD出圖）；2、設計說明書一份（1.2萬字以上）注：此表如不夠填寫，可另加頁。主要參考文獻與外文資料1 王先逵.機械制造工藝學M.北京：機械工業(yè)出版社，2010.2 劉朝儒,彭福蔭，高政機械制圖M北京：高等教育出版社，2008.3 朱張校主編.工程材料M.北京：清華大學出版社，2008.4 郝濱海.鍛造模具簡明設計手冊M.北京：化學工業(yè)出版社，20085 趙如福.金屬機械加工工藝人員手冊M.上海：上?？茖W技術出版公司，2008.6 戴 曙.金屬切削機床設計M.北京：機械工業(yè)出版社，2008.7 薛源順機床夾具手冊M.北京：機械工業(yè)出版社，2008.8 孫桓.機械原理M.北京：高等教育出版社，2008.9 成大先.機械設計手冊 第四卷M.北京：化學工業(yè)出版社，2007.10 劉家仁.機械設計常用元件手冊M.北京：機械工業(yè)出版社，2007.工作進度安排階段起止日期主要工作內(nèi)容12010.9.152010.9.18選題22010.9.192010.9.21下達任務書32010.9.222010.9.25開題42010.9.262011.3.10設計52011.3.112011.3.15中期考核62011.3.162011.4.30完善與總結課題72011.5.12011.5.16提交正稿與預審82011.5.172011.5.24答辯與修改要求完成日期：20 年 月 日 指導教師簽名： 審查日期： 20 年 月 日 專業(yè)負責人簽名： 批準日期： 20 年 月 日 接受任務日期：20 年 月 日 學生本人簽名： 注：簽名欄必須由相應責任人親筆簽名。ORIGINAL ARTICLE Deformation control through fixture layout design and clamping force optimization Weifang Chen 2 jj; :; j C12 C12 C12 C12 ; :; n jj C0C1 s ; j 1; 2; :; n 1 Subject to m F ni jjC21 F 2 ti F 2 hi q 2 F ni C21 0 3 pos i2Vi; i 1; 2; :; p 4 where j refers to the maximum elastic deformation at a machining region in the j-th step of the machining operation, X n j1 j C0 C0 C16C17 2 C30 n v u u t is the average of j F ni is the normal force at the i-th contact point is the static coefficient of friction F ti ; F hi are the tangential forces at the i-th contact point pos(i) is the i-th contact point V(i) is the candidate region of the i-th contact point. The overall process is illustrated in Fig. 1 to design a feasible fixture layout and to optimize the clamping force. The maximal cutting force is calculated in cutting model and the force is sent to finite element analysis (FEA) model. Optimization procedure creates some fixture layout and clamping force which are sent to the FEA model too. In FEA block, machining deformation under the cutting force and the clamping force is calculated using finite element method under a certain fixture layout, and the deformation is then sent to optimization procedure to search for an optimal fixture scheme. 4 Fixture layout design and clamping force optimization 4.1 A genetic algorithm Genetic algorithms (GA) are robust, stochastic and heuristic optimization methods based on biological reproduction processes. The basic idea behind GA is to simulate “survival of the fittest” phenomena. Each individual candidate in the population is assigned a fitness value through a fitness function tailored to the specific problem. The GA then conducts reproduction, crossover and mutation processes to eliminate unfit individuals and the population evolves to the next generation. Sufficient number of evolutions of the population based on these operators lead to an increase in the global fitness of the population and the fittest individual represents the best solution. The GA procedure to optimize fixture design takes fixture layout and clamping force as design variables to generate strings which represent different layouts. The strings are compared to the chromosomes of natural evolution, and the string, which GA find optimal, is mapped to the optimal fixture design scheme. In this study, the genetic algorithm and direct search toolbox of MATLAB are employed. The convergence of GA is controlled by the population size (P s ), the probability of crossover (P c )andthe probability of mutations (P m ). Only when no change in the best value of fitness function in a population, N chg , reaches a pre-defined value NC max , or the number of generations, N, reaches the specified maximum number of evolutions, N max ., did the GA stop. There are five main factors in GA, encoding, fitness function, genetic operators, control parameters and con- straints. In this paper, these factors are selected as what is listed in Table 1. Since GA is likely to generate fixture design strings that do not completely restrain the fixture when subjected to machining loads. These solutions are considered infeasible and the penalty method is used to drive the GA to a feasible solution. A fixture design scheme is considered infeasible or unconstrained if the reactions at the locators are negative, in other words, it does not satisfy the constraints in equations (2)and(3). The penalty method essentially involves Machining Process Model FEA Optimization procedure cutting forces fitness Optimization result Fixture layout and clamping force Fig. 1 Fixture layout and clamp- ing force optimization process Table 1 Selection of GAs parameters Factors Description Encoding Real Scaling Rank Selection Remainder Crossover Intermediate Mutation Uniform Control parameter Self-adapting Int J Adv Manuf Technol assigning a high objective function value to the scheme that is infeasible, thus driving it to the feasible region in successive iterations of GA. For constraint (4), when new individuals are generated by genetic operators or the initial generation is generated, it is necessary to check up whether they satisfy the conditions. The genuine candidate regions are those excluding invalid regions. In order to simplify the checking, polygons are used to represent the candidate regions and invalid regions. The vertex of the polygons are used for the checking. The “inpolygon” function in MATLAB could be used to help the checking. 4.2 Finite element analysis The software package of ANSYS is used for FEA calculations in this study. The finite element model is a semi-elastic contact model considering friction effect, where the materials are assumed linearly elastic. As shown in Fig. 2, each locator or support is represented by three orthogonal springs that provide restrains in the X, Y and Z directions and each clamp is similar to locator but clamping force in normal direction. The spring in normal direction is called normal spring and the other two springs are called tangential springs. The contact spring stiffness can be calculated according to the Herz contact theory 8 as follows k iz 16R C3 i E C32 i 9 C16C171 3 f iz 1 3 k iz k iy 6 E C3 i 2C0v fi G fi 2C0v wi G wi C16C17 C01 C1 k iz 8 : 5 where k iz , k ix , k iy are the tangential and normal contact stiffness, 1 R C3 i 1 R wi 1 R fi is the nominal contact radius, 1 E C3 i 1C0 V 2 wi E wi 1C0 V 2 fi E fi is the nominal contact elastic modulus, R wi , R fi are radius of the i-th workpiece and fixture element, E wi , E fi are Youngs moduli for the i-th workpiece and fixture materials, wi , fi are Poisson ratios for the i-th workpiece and fixture materials, G wi , G fi are shear moduli for the i-th workpiece and fixture materials and f iz is the reaction force at the i-th contact point in the Z direction. Contact stiffness varies with the change of clamping force and fixture layout. A reasonable linear approximation of the contact stiffness can be obtained from a least-squares fit to the above equation. The continuous interpolation, which is used to apply boundary conditions to the workpiece FEA model, is Fig. 2 Semi-elastic contact model taking friction into account Spring position Fixture element position 1234567 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Fig. 3 Continuous interpolation Fig. 4 A hollow workpiece Table 2 Machining parameters and conditions Parameter Description Type of operation End milling Cutter diameter 25.4 mm Number of flutes 4 Cutter RPM 500 Feed 0.1016 mm/tooth Radial depth of cut 2.54 mm Axial depth of cut 25.4 mm Radial rake angle 10 Helix angle 30 Projection length 92.07 mm Int J Adv Manuf Technol illustrated in Fig. 3. Three fixture element locations are shown as black circles. Each element location is surrounded by its four or six nearest neighboring nodes. These sets of nodes, which are illustrated by black squares, are 37, 38, 31 and 30, 9, 10, 11, 18, 17 and 16 and 26, 27, 34, 41, 40 and 33. A set of spring elements are attached to each of these nodes. For any set of nodes, the spring constant is k ij d ij P k2h i d ik k i 6 where k ij is the spring stiffness at the j-th node surrounding the i-th fixture element, d ij is the distance between the i-th fixture element and the j-th node surrounding it, k i is the spring stiffness at the i-th fixture element location. i is the number of nodes surrounding the i-th fixture element location. For each machining load step, appropriate boundary conditions have to be applied to the finite element model of the workpiece. In this work, the normal springs are constrained in the three directions (X, Y, Z)andthe tangential springs are constrained in the tangential direc- tions (X, Y). Clamping forces are applied in the normal direction (Z) at the clamp nodes. The entire tool path is simulated for each fixture design scheme generated by the GA by applying the peak X, Y, Z cutting forces sequentially to the element surfaces over which the cutter passes 23. In this work, chip removal from the tool path is taken into account. The removal of the material during machining alters the geometry, so does the structural stiffness of the workpiece. Thus, it is necessary to consider chip removal affects. The FEA model is analyzed with respect to tool movement and chip removal using the element death technique. In order to calculate the fitness value for a given fixture design scheme, displacements are stored for each load step. Then the maximum displacement is selected as fitness value for this fixture design scheme. The interaction between GA procedure and ANSYS is implemented as follows. Both the positions of locators and clamps, and the clamping force are extracted from real strings. These parameters are written to a text file. The input batch file of ANSYS could read these parameters and calculate the deformation of machined surfaces. Thus the fitness values in GA procedure can also be written to a text file for current fixture design scheme. It is costly to compute the fitness value when there are a largenumberofnodesinanFEMmodel.Thusitisnecessary to speed up the computation for GA procedure. As the generation goes by, chromosomes in the population are getting similar. In this work, calculated fitness values are stored in a SQL Server database with the chromosomes and fitness values. GA procedure first checks if current chromosomes fitness value has been calculated before, if not, fixture design scheme are sent to ANSYS, otherwise fitness values are directly taken from the database. The meshing of workpiece FEA model keeps same in every calculating time. The difference among every calculating model is the boundary conditions. Thus, the meshed workpiece FEA model could be used repeatedly by the “resume” command in ANSYS. 5 Case study An example of milling fixture design optimization problem for a low rigidity workpiece displayed in previous research papers 16, 18, 22 is presented in the following sections. Fig. 5 Candidate regions for the locators and clamps Table 3 Bound of design variables Minimum Maximum X /mm Z /mm X /mm Z /mm L 1 0 0 76.2 38.1 L 2 76.2 0 152.4 38.1 L 3 0 38.1 76.2 76.2 L 4 76.2 38.1 152.4 76.2 C 1 0 0 76.2 76.2 C 2 76.2 0 152.4 76.2 F 1 /N 0 6673.2 F 2 /N 0 6673.2 Int J Adv Manuf Technol 5.1 Workpiece geometry and properties The geometry and features of the workpiece are shown in Fig. 4. The material of the hollow workpiece is aluminum 390 with a Poisson ration of 0.3 and Youngs modulus of 71 Gpa. The outline dimensions are 152.4 mm127 mm 76.2 mm. The one third top inner wall of the workpiece is undergoing an end-milling process and its cutter path is also shown in Fig. 4. The material of the employed fixture elements is alloy steel with a Poisson ration of 0.3 and Youngs modulus of 220 Gpa. 5.2 Simulating and machining operation A peripheral end milling operation is carried out on the example workpiece. The machining parameters of the operation are given in Table 2. Based on these parameters, the maximum values of cutting forces that are calculated and applied as element surface loads on the inner wall of the workpiece at the cutter position are 330.94 N (tangential), 398.11 N (radial) and 22.84 N (axial). The entire tool path is discretized into 26 load steps and cutting force directions are determined by the cutter position. 5.3 Fixture design plan The fixture plan for holding the workpiece in the machining operation is shown in Fig. 5.Generally,the321 locator principleisusedinfixturedesign.Thebasecontrols3degrees. One side controls two degrees, and another orthogonal side controlsonedegree.Here,itusesfourlocators(L1,L2,L3and L4) on the Y=0 mm face to locate the workpiece controlling two degrees, and two clamps (C1, C2) on the opposite face where Y=127 mm, to hold it. On the orthogonal side, one locator is needed to control the remaining degree, which is neglectedintheoptimalmodel.Thecoordinateboundsforthe locating/clamping regions are given in Table 3. Since there is no simple rule-of-thumb procedure for determining the clamping force, a large value of the clamping force of 6673.2 N was initially assumed to act at each clamp, and the normal and tangential contact stiffness obtained from a least-squares fit to Eq. (5) are 4.4310 7 N/m and 5.4710 7 N/m separately. 5.4 Genetic control parameters and penalty function The control parameters of the GA are determined empiri- cally. For this example, the following parameter values are Fig. 6 Convergence of GA for fixture layout and clamping force optimization procedure Fig. 7 Convergence of the first function values Fig. 8 Convergence of the second function values Table 4 Result of the multi-objective optimization model Multi-objective optimization X /mm Z /mm L 1 17.102 30.641 L 2 108.169 25.855 L 3 21.315 56.948 L 4 127.846 60.202 C 1 22.989 62.659 C 2 117.615 25.360 F 1 /N 167.614 F 2 /N 382.435 f 1 /mm 0.006568 /mm 0.002683 Int J Adv Manuf Technol used: P s =30, P c =0.85, P m =0.01, N max =100 and N cmax = 20. The penalty function for f 1 and is f v f v 50 Here f v can be represented by f 1 or . When N chg reaches 6 the probability of crossover and mutation will be change into 0.6 and 0.1 separately. 5.5 Optimization result The convergence behavior for the successive optimization steps is shown in Fig. 6, and the convergence behaviors of corresponding functions (1) and (2) are shown in Fig. 7 and Fig. 8. The optimal design scheme is given in Table 4. 5.6 Comparison of the results The design variables and objective function values of fixture plans obtained from single objective optimization and from that designed by experience are shown in Table 5. The single objective optimization result in the paper 22is quoted for comparison. The single objective optimization method has its preponderance comparing with that designed by experience in this example case. The maximum deformation has reduced by 57.5%, the uniformity of the deformation has enhanced by 60.4% and the maximum clamping force value has degraded by 49.4%. What could be drawn from the comparison between the multi-objective optimization method and the single objective optimization method is that the maximum deformation has reduced by 50.2%, the uniformity of the deformation has enhanced by 52.9% and the maximum clamping force value has degraded by 69.6%.The deformation distribution of the machined surfaces along cutter path is shown in Fig. 9. Obviously, the deformation from that of multi-objective optimization method distributes most uniformly in the deformations among three methods. With the result of comparison, we are sure to apply the optimal locators distribution and the optimal clamping force to reduce the deformation of workpiece. Figure 10 shows the configuration of a real-case fixture. 6 Conclusions This paper presented a fixture layout design and clamping force optimization procedure based on the GA and FEM. The optimization procedure is multi-objective: minimizing the maximum deformation of the machined surfaces and maximizing the uniformity of the deformation. The ANSYS software package has been used for FEM calculation of fitness values. The combinationof GAand FEM isproven to be a powerful approach for fixture design optimization problems. In this study, both friction effects and chip removal effects are considered. In order to reduce the computation time, a database is established for the chromosomes and fitness values, and the meshed workpiece FEA model is repeatedly used in the optimization process. Table 5 Comparison of the results of various fixture design schemes Experimental optimization Single objective optimization X/mm Z/mm X/mm Z/mm L 1 12.700 12.700 16.720 34.070 L 2 139.7 12.700 145.360 17.070 L 3 12.700 63.500 18.400 57.120 L 4 139.700 63.500 146.260 58.590 C 1 12.700 38.100 5.830 56.010 C 2 139.700 38.100 104.400 22.740 F 1 /N 2482 444.88 F 2 /N 2482 1256.13 f 1 /mm 0.031012 0.013178 /mm 0.014377 0.005696 Fig. 9 Distribution of the deformation along cutter path Fig. 10 A real case fixture configuration Int J Adv Manuf Technol Thetraditionalfixturedesignmethodsaresingleobjective optimization method or by experience. The results of this study show that the multi-objective optimization method is more effective in minimizing the deformation and uniform- ing the deformation than other two methods. It is meaningful for machining deformation control in NC machining. References 1. 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