喜歡就充值下載吧。。資源目錄里展示的文件全都有，，請(qǐng)放心下載，，有疑問咨詢QQ:1064457796或者1304139763 ==================== 喜歡就充值下載吧。。資源目錄里展示的文件全都有，，請(qǐng)放心下載，，有疑問咨詢QQ:1064457796或者1304139763 ==================== ----大學(xué)畢業(yè)設(shè)計(jì)（論文）任務(wù)書 畢業(yè)設(shè)計(jì)（論文）題目： 犁刀變速齒輪箱體工藝規(guī)程及夾具設(shè)計(jì) 畢業(yè)設(shè)計(jì)（論文）要求及原始數(shù)據(jù)（資料）： 1. 生產(chǎn)綱領(lǐng)：中批生產(chǎn)； 2. 犁刀變速齒輪箱體零件圖； 3. 設(shè)計(jì)犁刀變速齒輪箱體加工工藝規(guī)程； 4. 設(shè)計(jì)犁刀變速齒輪箱體鉆孔夾具； 5. 設(shè)計(jì)犁刀變速齒輪箱體銑面夾具。 6. 在設(shè)計(jì)方案等環(huán)節(jié)應(yīng)考慮和體現(xiàn)社會(huì)、健康、安全、環(huán)境、法律、文化等因素的影響。 進(jìn)度安排： 第四周～第六周：查閱相關(guān)資料,寫開題報(bào)告，進(jìn)行文獻(xiàn)綜述。做與題目相關(guān)英文資料的中文翻譯； 第七周：對(duì)零件進(jìn)行工藝分析，畫零件圖； 第八周～第九周：選擇加工方案，確定毛坯的制造形式，制訂工藝路線，選擇定位基準(zhǔn)，選擇機(jī)床及工、夾、量、刃具，確定加工余量、工序間尺寸及與公差，確定毛坯尺寸，畫毛坯圖； 第十周：確定各工序的切削用量及基本工時(shí)； 第十一周～第十二周：工藝裝備設(shè)計(jì)，計(jì)算夾緊力，進(jìn)行定位誤差分析，畫總裝圖； 第十三周：畫夾具零件圖； 第十四周～第十五周：編寫設(shè)計(jì)說明書； 第十六周：準(zhǔn)備所有答辯資料，準(zhǔn)備答辯； 第十七周：進(jìn)行畢業(yè)答辯。 畢業(yè)設(shè)計(jì)（論文）主要內(nèi)容： 1.分析零件的工藝性； 2.根據(jù)生產(chǎn)綱領(lǐng)決定生產(chǎn)類型； 3.選擇毛坯的種類和制造方法； 4.擬訂工藝過程； 5.工序設(shè)計(jì)及計(jì)算； 6.編制工藝文件； 7.設(shè)計(jì)鉆、銑夾具。 學(xué)生應(yīng)交出的設(shè)計(jì)文件（論文）： 1. 犁刀變速齒輪箱體的工藝過程綜合卡片； 2. 機(jī)加工工序卡； 3. 鉆、銑夾具裝配圖； 4. 鉆、銑夾具零件圖； 5. 犁刀變速齒輪箱體零件圖和毛坯圖； 6.設(shè)計(jì)說明書一份。 主要參考文獻(xiàn)（資料）： [1] 李洪.機(jī)械加工工藝手冊(cè)[M]. 北京:北京出版社，1900. [2] 李益民. 機(jī)械制造工藝設(shè)計(jì)簡(jiǎn)明手冊(cè) [M]. 北京：機(jī)械工業(yè)出版社，1994. [3] 孫本續(xù)，熊萬武. 機(jī)械加工余量手冊(cè)[M]. 北京：國防工業(yè)出版社，1999. [4] 艾興，肖詩綱. 切削用量簡(jiǎn)明手冊(cè) [M].3版. 北京：機(jī)械工業(yè)出版社，1994. [5] 呂明. 機(jī)械制造技術(shù)基礎(chǔ) [M].2版. 武漢：武漢理工大學(xué)出版社，2010. [6] 廖念釗等. 互換性與技術(shù)測(cè)量[M].6版. 北京：中國質(zhì)檢出版社，2012 [7] 李大磊，王棟. 機(jī)械制造工藝學(xué)課程設(shè)計(jì)指導(dǎo)書[M].2版 北京：機(jī)械工業(yè)出版社，2014 [8] 馬麟等.畫法幾何與機(jī)械制圖[M]. 北京：高等教育出版社，2011 [9] 東北重型機(jī)械學(xué)院等編.機(jī)床夾具設(shè)計(jì)手冊(cè)[M]. 上海:上海科技技術(shù)出版社，1990 [10] 孟少農(nóng). 機(jī)械加工工藝手冊(cè)[M] 第一卷.北京:機(jī)械工業(yè)出版社，1991 [11] P.L.Jacobs.Stereolithograghy and Other RP&M Techologies.ASME Press[M],1996 專業(yè)班級(jí) 學(xué)生 要求設(shè)計(jì)（論文）工作起止日期 指導(dǎo)教師簽字 日期 教研室主任審查簽字 日期 系主任批準(zhǔn)簽字 日期 2351-9789 2015 The Authors.Published by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http:/creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the NAMRI Scientific Committeedoi:10.1016/j.promfg.2015.09.079 Fixture Design Optimisation Considering Production Batch of Compliant Non-Ideal Sheet Metal Parts Abhishek Das,Pasquale Franciosa and Darek Ceglarek WMG,The University of Warwick,Coventry,U.K.abhishek.daswarwick.ac.uk,pasquale.fraciosawarwick.ac.uk,d.j.ceglarekwarwick.ac.uk Abstract Fixtures control the position and orientation of parts in an assembly process and thus significantly contribute to process capability that determines production yield and product quality.As a result,a number of approaches were developed to optimise a single-and multi-fixture assembly system with rigid(3-2-1 fixture layout)to deformable parts(N-2-1 fixture layout).These approaches aim at fixture layout optimisation of single ideal parts(as define by CAD model).However,as production yield and product quality are determined based on a production volume of real(non-ideal)parts.Thus,major challenges involving the design of a fixture layout for assembly of sheet metal parts can be enumerated into three categories:(1)non-ideal part consideration to emulate real part;(2)N-2-1 locating scheme due to compliant nature of sheet metal parts;and,(3)batch of non-ideal parts to consider the production process error at design stage.This paper presents a new approach to improve the probability of joining feasibility index by determining an N-2-1 fixture layout optimised for a production batch of non-ideal sheet metal parts.The proposed methodology is based on:(i)generation of composite parts to model shape variation within given production batch;(ii)selection of composite assembly representing production batch;(iii)parameterisation of fixture locators;and(iv)calculation of analytical surrogate model linking composite assembly model and fixture locators to probability of joining feasibility index.The analytical surrogate model is,then,utilised to maximise the probability of joining feasibility index starting from initial fixture locator layout.An industrial case study involving assembly process of remote laser welded door assembly illustrates and validates the proposed methodology.Keywords:Shape error modelling,Batch of sheet metal parts,N-2-1 fixture design optimisation,Surrogate model 1 Introduction Assembly fixture plays a significant role to achieve desired dimensional and joining qualities(Key Product Characteristics-KPCs)of assembled product where fixture design parameters act as Key Control Characteristics(KCCs).Fixtures are being used to provide accurate locating scheme to the Procedia ManufacturingVolume 1,2015,Pages 15716843rd Proceedings of the North American Manufacturing ResearchInstitution of SME http:/www.sme.org/namrc parts or subassemblies being assembled as well as to avoid shape variation in the assembly.It has been demonstrated that fixtures have large impact on product dimensional and geometric/shape variation and,subsequently,on product yield(Phoomboplab and Ceglarek,2008;Das et al.,2014).This is especially true for assembly processes of sheet metal parts produced by plastic deformation processes which lead to significant shape variations(also called non-ideal part)due to mainly spring-back,forming process parameters variations,tooling errors.Additionally,due to the compliance of sheet metals,parts can get deformed and cause variation in assembly processes(Li et al.,2001).For example,excessive variations in automotive enclosure panels may cause fundamental problems such as unnecessary closing effort,improper fit causing vibration and noise,air leakage as well as poor aesthetic appearance due to misalignment(Ceglarek et al.,2004;Camelio et al.,2004a;Huang et al.,2014).Subsequently,the shape variation management is a key issue in current industrial manufacturing and assembly process as it has direct impact on the product quality,cost and time-to-market.To be competitive in the market,proper shape and part management through robust fixture design is inevitable prerequisite to minimize the defects caused by variation during manufacturing and product usage.The locating principle 3-2-1 is widely used in industries to locate rigid body parts quite uniquely without creating locator interferences(Lowell,1982;Shirinzadeh,2002).Variety of research literature exists in field of fixture design considering 3-2-1 part locating scheme which are mainly focused on designing and optimising fixtures for machining operations(Youcef-Toumi et al.,1988;Menassa and DeVries,1991).Further,Rearick et al.(1993)introduced deformable sheet metal parts and they proposed a technique combining the nonlinear programming and FEM for determining the best fixture locations.Beyond the first requirement of part placement and constraining the rigid body motion,the fixture should also be able to limit any part deformation.Unfortunately,compliant sheet metal parts cannot be controlled through 3-2-1 scheme which require increased number of locators to N-2-1 to minimise geometric deviation(N3).For compliant part fixturing,Cai et al.(1996)proposed N-2-1 locating principle which allows to prevent excessive deformation of sheet metal parts by defining N locators on the primary datum.Camelio et al.(2004a)presented a new fixture design methodology for sheet metal assembly processes focusing on the impact of fixture position on the dimensional quality of sheet metal parts after assembly by considering the effect of part variation,tooling variation and assembly spring-back.A number of research focuses on joining process considering resistance spot welding and single part errors(Cai,2008;Li et al.,2008a;Li et al.,2010;Liu and Hu,1997).In case of laser welding,fixture plays a vital role by providing the degree of metal fit-up required to join the mating parts together.Li et al.(2001)proposed a prediction and correction methodology integrated with FEM for fixture design for laser welding where the objective function is to minimise the degree of Metal Fit-up(DMF),which is the maximum distance between mating nodes in weld joints.Few attempts have been made over the years to optimise fixture design considering the metal fit-up problem of compliant sheet metal assembly and the parts shape variation(Li et al.,2001).Undoubtedly,a batch of sheet metal parts produced through metal forming process may be affected by within batch or batch-to-batch variation which leads to quality loss of the final assembly.For example,some assembly joining processes,such as Remote Laser Welding(RLW),part variation strongly affects the final product performance which is imputed to part-to-part gap(Ceglarek,2011).Therefore,a systematic fixture design approach is demanded to mitigate the part-to-part variation as coming from the real manufacturing process.Existing methods(Li et al.,2007;Li et al.,2003;Cai,2006;Cai et al.,2005)for fixture design optimisation are based on single ideal/non-ideal compliant assembly models which are not sufficient to mitigate the error components associated with batch of assemblies.Robust fixture design is to make the output results insensitive to shape variation considering batch of parts to improve the product and process performance.The objective of this paper is to develop a novel robust methodology for fixture design optimisation by addressing a batch of non-ideal compliant assemblies.The proposed methodology is based on the concept of composite part(Das et al.,2015)which mainly quantifies the main shape error patterns/modes into composite parts coming from a Fixture Design OptimisationDas,Franciosa and Ceglarek158 batch of parts.Composite part can be defined as the part composed of all the major significant shape error components present in the population.In reality,the composite part may not exist but it reduces the efforts required for assembly process simulation as it composed of all the major shape error components.The composite parts and initial fixture locator strategies are taken as input for fixture modelling.The methodology involves selection of composite assemblies and optimisation to obtain the robust layout of the fixturing elements(i.e.,location of clamps).Therefore,it allows to optimise not only single assembly but batch of assemblies which presumably represents the production population and identifies robust fixture design parameters through optimisation to maximize the probability of joining feasibility index.A significant gap in the literature has been identified to optimise fixture design of non-ideal compliant parts.Table 1 reviews the state of art of the existing methods for fixture design optimisation.The paper has been arranged with the following sections:Section 3 describes the methodology which includes the overview of the shape error quantification for batch of parts,composite assembly selection strategy and optimisation formulation.Section 4 demonstrates the applicability through industrial cases with remote laser welding.Further,section 5 summarises the conclusions.Fixturing Scheme 3-2-1 fixture N-2-1 fixture Single part error based assembly Rearick et al.(1993);Ceglarek(1998);Li et al.(2008b)Cai et al.(1996);Cai(2008);Camelio et al.(2004a);Li et al.(2001);Li et al.(2008a);Li et al.(2010);Yu et al.(2008);Franciosa et al.(2011)Batch of parts error based assembly-Proposed in this paper Table 1:Review of fixture design methods with current research gap 2 Fixture Optimisation Methodology Overview The proposed methodology is composed of three stages.Firstly,part shape variation is determined using part measurement data for batch of parts through quantifying the shape errors into few composite parts;and initial process configuration,i.e.,joint locations,initial fixture locations(clamps,support blocks,locators etc.)are as initial process input.Thereafter,the finite element modelling for fixture simulation has been performed considering composite parts,fixture elements and contact pairs using Variation Response Method(VRM)software which is a Matlab based finite element modelling software toolkit with capabilities of fast modelling specific features required by assembly process(Franciosa et al.,2015).VRM is a new comprehensive methodology for dimensional management of assembly processes with compliant non-ideal parts which allows to analytically model the product-to-process interaction.At this stage,fewer composite assemblies have been Figure 1:Overview of fixture design optimisation methodology.Initial Process Information(CAD specs,Locator Strategy)Part Measurement(Batch of Parts)2.1 Batch of Parts Modelling Statistical Geometric Modal Analysis(SGMA)Composite PartsOptimum Layout2.2 Composite Assembly Selection Composite Assemblies with Map Index(MI)Eq.(3)Correlation Criteria Based Clustering Eq.(5)Entropy Based Assembly Selection Eq.(8)2.3 Optimisation Strategy Formulation Analytical Surrogate model development Maximise Joining Feasibility Index Eq.(10)VRM Modelling EnvironmentFixture Design OptimisationDas,Franciosa and Ceglarek159 selected which quantifies the batch errors.Finally,the nonlinear optimisation has been carried out on the defined KPCs to obtain the optimised fixture layout by varying the KCCs(clamp locations).Optimiser updates the variables that are KCCs of the process to maximise the joining feasibility index.Figure 1 illustrates the fixture design optimisation methodology considering batch of parts and initial process information under the VRM modelling environment.2.1 Batch of Parts Modelling Overview To characterise and quantify the part shape variation associated with a batch of parts,Das et al.(2015)developed Statistical Geometric Modal Analysis(SGMA)methodology which identifies the main shape error patterns present in the individual parts and merge them together using different criteria to create composite parts.The main objective of SGMA method is statistical characterisation of a batch of parts which are representative of production population.The individual part error modes are parameterised by means of its amplitude.The shape error modes are statistically characterised using non-parametric Kernel Density Estimator(KDE)which provide more accurate depiction of the shape variation.Data dimensional reduction approach,such as,Principal Component Analysis(PCA)has been utilized to extract deformation patterns from production data(Camelio et al.,2004b).However,PCA based decomposition is not suitable for shape error characterisation as it is incapable for detection of process shift in primary data set or presence of different shape errors in the data(Matuszyk et al.,2010).Unfortunately,real process of part stamping clearly exhibit different grouping of shape errors in within-run production and process shift in batch-to-batch production.Therefore,the measured part errors need to be decomposed independently to provide more accurate estimation of underlying shape errors.The SGMA method eliminates the challenges and model batch of parts error more accurately.The proposed SGMA methodology involves significant modes identification from a batch of parts,statistical characterisation of extracted modal signatures.The quantification of shape variation engraved with a batch of parts has been achieved through synthesising composite parts which are composed of major error components from the batch.Relying on the energy compaction criteria,a number of composite parts can be created where the composite parts contain the major shape errors present in the batch of parts.The overview of the SGMA method for composite part creation has been shown in Figure 2.Further,depending upon the type of shape error modes present in the batch of parts,using K-means clustering process,the parts are grouped in few clusters exhibit similar type of errors.Thereafter,energy compaction criteria have been applied to obtain the composite parts for each cluster.Therefore,using maximum,minimum and average energy compaction criteria,three composite parts created for each cluster.These composite parts behave differently in assembly system due to the part-to-part interaction.The proposed SGMA method has been applied to model and quantify part shape variation of a batch of sheet metal parts produced by stamping process and these composite parts are used for fixture design optimisation.Figure 2:Overview of the SGMA method(i)batch of parts measurement,(ii)SGMA method and statistical characterisation,and(iii)synthesis of composite part using SGMA.Original Deviation(Batch of Parts)SGMA Method&Statistical CharacterisationComposite Parts420-2-4Dev mm0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0-2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2-4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4420-2-4Dev mm-4420-2-4Dev mm1.41.00.60.2x 10-2-100 -50 0 50 100 1501.20.80.4x 10-30 1000 2000 30001.00.80.60.40.2x 10-3-2000 -1000 0 500Dev mm3210-1-2-3Main Error PatternsStatistical Distribution of Error Patterns(i)(ii)(iii)Fixture Design OptimisationDas,Franciosa and Ceglarek160 2.2 Composite Assembly Selection Relying on the creation of composite parts and number of parts present in an assembly,several different composite assemblies can be created by considering the exhaustive combination of all composite parts.For example,in an assembly operation M number of parts(?)are to be joined which is consist of?number of KPCs?,where?represents the part id and?represents the ith KPC in the assembly.The assembly consists of L number of KCCs.Therefore,depending upon the types of shape error present in a batch,parts may be grouped into?number of clusters.For each cluster,a total three composite parts can be created depending on maximum,minimum and average energy compaction criteria,i.e.,?.Therefore,the assembly system can be written as?,max,min,max,min,:,1,2,:,1,2,:,1,2,:,:istmlmmm avgMAXmgMINmgKPCsKPCiNPartsPTmMKCCsKCClLCompositePartsCPTCPTCPTMaximumCompositeParts CPTCPTMinimumCompositeParts CPTCPTAverageCompositeParts?,1,2,;1,2,AVGm avg gmCPTCPTwheremM gN?(1)Therefore,depending upon the number of clusters modelled for all the parts present in the assembly,the combination of composite assemblies also increases.The number of obtained composite assemblies can be formulated as?:MAXMINAVGCompositeAssembly CACPTCPTCPT?(2)As the each fixture simulation is time expensive,optimisation based on all composite assembly combination becomes computationally inefficient.Therefore,it emphasises on selection of few composite assemblies which are representative of all other assemblies.In order to reduce the assembly number for optimisation,two different criteria have been proposed:(i)Correlation Criteria Based Clustering and(ii)Entropy Based Assembly Selection.2.2.1.Correlation Criteria Based Clustering All combinations of composite parts are determined as per equation(2)to create complete set of composite assemblies.In order to achieve reduced number of composite assemblies for optimisation,a correlation threshold based clustering criteria introduced.It involves clustering of composite assemblies based on similar KPC Map Index(MI).MI depends on the type of KPCs selected such as point deviation,part-to-part gap distribution,surface area deformation etc.Considering the initial locator strategy(KCCs),such as given clamp layout and NC blocks,an initial fixture simulation provide part-to-part KPC map index for all the composite assemblies,?.A map index of a given iih KPC(?)of jth composite assembly can define as a function,(,)i ji jMIf CAKCC?(3)where the function f denotes the fixture simulation process composed of part-to-part interaction,boundary constraints,contact pair detection and part/assembly flexibility.Equation(3)represents the fixture simulation process with map index as an outcome.Subsequently,considering all the defined KPCs in the assembly,a total MI for the jth assembly can be evaluated as,Fixture Design OptimisationDas,Franciosa and Ceglarek161 ,1stNji jiTMIMI?(4)Similar error contained assemblies are expected to exhibit similar MI as all other parameters are kept constant.The correlation coefficient(?)between two assemblies(j and k)can be estimated as,?,22cov,jkj kjkTMITMI?(5)where,?and?,?represent the standard deviations of the total map index of?and?assembly respectively.Therefore,the correlation matrix has been determined for all composite assemblies and a user defined correlation threshold,?,has been applied to group the assemblies having the similar KPC map index.The composite assemblies can be clustered into fewer groups consist of similar type of map index distribution.This implies that one assembly from the specific cluster can be chosen for the optimisation and the obtained result should be optimum for all the assemblies belong to that cluster.2.2.2.Entropy Based Assembly Selection To select one representative assembly from each cluster for optimisation,entropy based selection criteria has been introduced.The analysis of the MIs content can be performed by borrowing tools that have been developed in the field of information theory.In particular,it is proposed to determine the Information(I)contained on MI,calculated for the?MI of?assembly(?)as(Suh,2005),2,logi ji jIp?(6)where?represents the probability of satisfying the joining requirements of?.This can be estimated as the ratio between the numbers of points in a MI satisfying the joining requirements over the total number of points of the MI.The closer?is to zero,the more likely that the parts can be joined in that particular surface.The entropy(?)for a complete assembly having?number of KPCs can be calculated,following Shannons definition involving the quantification of information by measuring the uncertainty in a MI,as(Cover and Thomas,2006),1stNji ji jiHpI?(7)The