卧式马铃薯去皮机设计【卧式土豆去皮机设计】
卧式马铃薯去皮机设计【卧式土豆去皮机设计】,卧式土豆去皮机设计,卧式马铃薯去皮机设计【卧式土豆去皮机设计】,卧式,马铃薯,土豆,去皮,设计,洋芋
UNIVERSITY本 科 毕 业 论 文(设 计) 题目: 卧式马铃薯去皮机设计 学 院: 姓 名: 学 号: 专 业: 机械设计制造及其自动化年 级: 指导教师: 职 称: 讲师 二一二 年 五 月摘要 目前,常见马铃薯去皮机多采用立式转筒型,立式结构多通过下端输入动力,上端悬置,这就使转筒存在运动不平稳的问题,同时,马铃薯主要依靠转筒底部的圆盘来达到去皮效果,而马铃薯依靠自身重力大多堆积在圆盘上,各马铃薯与圆盘接触的不均致使马铃薯去皮不均匀,去皮效果不佳。本文论述了解决以上两大问题的方法,即将转筒制成卧式,采用两端轴承固定的方法提高转筒运动平稳性,同时通过转筒筒壁上的凸起棱部分与马铃薯的摩擦来达到去皮效果,这使得马铃薯的去皮效果达到均匀且较佳。通过对两种去皮机各方面性能的综合对比,采用传统设计方法,获取整机各零件的参数,并且通过计算,论证了该设计方案的可行性,最终结论是卧式去皮机综合性能较立式去皮机更佳。关键词:去皮机;马铃薯去皮机理;卧式马铃薯去皮机Abstract At present, the common potato peeling machine adopts the vertical drum type, vertical structure in the lower end of the input power, the upper suspension, which makes the rotary movement exists unstable problem, at the same time, potato mainly rely on the drum at the bottom of the disk to achieve the peeling effect, and potato on its own gravity mostly accumulates on the disc, the potato contact with the disk does not have led to peel the potatoes peeled uneven, poor effect. This paper discusses the method ,to solve two problems the drum made of horizontal, with both ends of bearing fixing method for improving the rotary motion stability, at the same time through the rotating barrel wall convex edge part and potato friction to achieve the peeling effect, which makes the potato peeling effect to achieve uniform and better. Through to two kind of peeling machine all aspects of the comprehensive comparison, using the traditional design method, acquiring the various parameters of parts, and through calculation, it proves the feasibility of the design, the final conclusion is that the horizontal peeling machine comprehensive performance than vertical peeling machine. Key words : peeling machine for peeling potatoes; mechanism; horizontal potato peeling machine 目录1 绪论1 1.1 马铃薯的主要特点及功用1 1.2 常用薯类去皮方法12 整机的结构设计和工作原理23 马铃薯去皮机零部件的设计3 3.1 机械传动系统总体设计3 3.1.1 传动方案的拟定3 3.1.2 电动机的选择3 3.2 传动装置运动和动力参数的计算4 3.2.1 传动比分配4 3.2.2 各轴的转速计算5 3.2.3 各轴输入功率计算5 3.2.4 各轴输入扭矩计算5 3.3 V 带传动的设计计算5 3.4 齿轮传动的设计计算11 3.5 轴系零件的设计计算11 3.5.1 初算d12 3.5.2 计算轴上载荷12 3.5.3 绘制轴的弯扭矩图,对危险截面进行校核14 3.5.4 轴承的校核15 3.5.5 轴上键连接的选择及校核15 3.5.6 轴上螺母的选择15 3.6 滚筒及其轴系零件的设计、选择与校核15 3.6.1 滚筒的结构设计16 3.6.2 轴承的选择、校核18 3.6.3 齿轮连接键的选择及校核18 3.6.4 轴上螺母选择18 3.7 轴承座、端盖的结构设计18 3.7.1 轴承座的选材18 3.7.2 轴承座的固定方式18 3.7.3 轴承座的结构设计19 3.7.4 轴承座端盖选材、结构设计19 3.8 轴承座联接用螺栓的设计计算19 3.9 机架的结构设计21设计小结22参考文献2341 绪论1.1 马铃薯的主要特点及功用马铃薯为植物的块茎形状为圆形或椭圆形,其结构由表皮层、形成层、外果肉和内果肉四部分。马铃薯品种可分两大类,一类是淀粉含量较高的,适宜于作为生产淀粉的原料,另一类是蛋白质含量较高,适宜作为蔬果或制成多种食品。马铃薯营养丰富,除直接食用外,还可以加工成食品、全粉、淀粉等经济价值较高的食品,通过加工可以大幅度提高鲜薯的商品价值。鉴于马铃薯的很多特点,马铃薯得到了广泛的利用:a.马铃薯可鲜食,鲜食用马铃薯主要用作家庭餐馆烹调,我国主要用来制作菜肴、面点小吃等大众食品。果外除蒸烤鲜马铃薯作主食外,还有咖哩饭、炖薯快以及色拉凉拌菜。马铃薯可制成方便食品、快餐食品、休闲食品,如马铃薯粉、马铃薯全粉、脱水马铃薯片(条)、速冻薯条(薯泥)、蒸薯条、罐装和去皮马铃薯、马铃薯脆片、马铃薯膨化小食品等;b,加工成淀粉及相关产品:由于马铃薯淀粉的优良特性,不仅是制作高级方便面、面类最为理想的添加淀粉,而且还是肉制品、鱼糜制品等的添加剂或原料。马铃薯淀粉也是粉条的优质原料。出马铃薯淀粉外,也可得到相关产品,包括各种变性淀粉、饴糖、葡萄糖、膳食纤维制品等;c,其他制品:马铃薯提取淀粉后的残渣可制成马铃薯发酵饲料、提取蛋白等;1.2 常用薯类去皮方法去皮用于多种水果和蔬菜的加工中以除去不需要或不可食的物质,并改善成品的外观。主要的考虑因素包括通过尽可能减少去掉的部分以及可能降低能源、劳力和物质成本来降低总成本。这里,主要介绍以下四种去皮方法: a.机械切削去皮 是采用锋利的刀片表面皮层。去皮速度较快,但不完全,且果肉损失较多,一般需用手工加以修整,难以实现完全机械作业,适用于果大、皮薄、肉质较硬的果蔬。目前,苹果、梨、柿等常使用机械切削去皮,常用的形式为旋皮机。旋皮机是将待去皮的水果插在能旋转的插轴上,靠近水果一侧安装(或手持)一把刀口弯曲的刀,使刀口贴在果面上。插轴旋转时,刀就从旋转的水果表面将皮车去。旋皮机插轴的转动有手摇、脚踏和电动几种动力形式。在旋车去皮之前应有选果工序,以保证水果大小基本一致。b.机械磨削去皮 是利用覆有磨料的工作面除去表面皮层。可高速作业,易于实现完全机械操作,所得碎皮细小,便于用水或气流清除,但去皮后表面较粗糙,适用于质地坚硬、皮薄、外形整齐的果蔬。胡萝卜、番茄等块根类蔬菜原料去皮大多采用机械磨削去皮机。c.机械摩擦去皮 是利用摩擦因数大、接触面积大的工作构件而产生的摩擦作用使表皮发生撕裂破坏而被去除。所得产品表面质量好,碎皮尺寸大,去皮死角少,但作用强度差,适用于果大、皮薄、皮下组织松散的果蔬,一般需要首先对果蔬进行必要的预处理来弱化皮下组织。常见到的机械摩擦去皮机如采用橡胶板作为工作构件的干法去皮机。d.化学去皮 又称碱液去皮,即将果蔬在一定温度的碱液中处理适当的时间,果皮即被腐蚀,取出后,立即用清水冲洗或搓擦,外皮即脱落,并洗去碱液。此法适用于桃、李、杏、梨、苹果等的去皮及橘瓣脱囊衣。桃、李、苹果等的果皮由角质、半纤维素等组成,果肉由薄壁细胞组成,果皮与果肉之间为中胶层,富含原果胶及果胶,将果皮与果肉连接。当果蔬与碱液接触时,果皮的角质、半纤维素被碱腐蚀而变薄乃至溶解,果胶被碱水溶解而失去胶凝性,果肉薄壁细胞膜较能抗碱。因此,用碱液处理后的果实,不仅果皮容易去除,而且果肉的损伤较少,可以提高原料的利用率。但是,化学去皮用水量较大,去皮过程产生的废水多,尤其是产生大量含有碱液的废水。2 整机的结构设计和工作原理 图1 整机结构设计图整机的机构设计如上图所示:1电动机; 2带轮传动; 3齿轮传动;4滚筒马铃薯去皮机工作原理:如上图所示,电动机通过皮带传动和齿轮传动两级降速,将动力传至滚筒,滚筒以一定转速旋转,马铃薯由进料漏斗通过滚筒右侧的空心轴进入滚筒,滚筒转度限制在一定范围内,以使马铃薯在随滚筒旋转时在滚筒的最高点以前与筒壁分离,从而达到去皮效果,同时,滚筒的半径左大右小,具有一定锥度,马铃薯受到向左的轴向分力,在轴向分力的作用下,马铃薯在滚筒内作翻转运动的同时沿轴向运动至滚筒左侧,从滚筒左侧筒壁上开的小口落下,完成去皮运动。马铃薯在滚筒内作翻转运动,为了达到去皮效果,滚筒转速不得大于临转速否则,马铃薯在滚筒内随滚筒一起转动,马铃薯与滚筒没有相对运动速度,起不到去皮效果。 。 图2 马铃薯在滚筒内的运动图 3 马铃薯去皮机零部件的设计3.1 机械传动系统总体设计3.1.1 传动方案的拟定:常用传动机构的一般布置原则如下: (1)带传动承载能力较低,但传动平稳,缓冲吸振能力强,宜布置在高速级。 (2)对于开式齿轮的传动,由于其工作环境较差,润滑不良,为减少磨损,宜布置在低速级。3.1.2 电动机的选择给定去皮机的工作条件:滚筒工作功率=3.2kw,直径D=300mm,稍有震动,在室温下连续运转,工作环境多尘,电源为三相交流,电压为380V。(1)选择电动机类型和结构形式 系统无特殊需求,一般选用Y系列三相交流异步电动机。选用全封闭自扇冷式笼型,电压380V。(2)选择电动机容量 为电动机的功率; 为工作及功率; 为传动装置的总效率; 为滑动轴承的效率,查表取0.97(一对) 为带传动的效率,查表取0.96 为齿轮传动的效率,查表取0.9求解得:=0.960.9 =0.813 kw ,差电动机参数表选取电动机额定功率=4kw。(3)选择电动机转速、确定滚筒转速、总传动比 (a)根据动力源和工作条件,电机的转速选择常用的两种同步转速:1500r/min和1000r/min。选用1000r/min。 查表选用Y132M1-6型号的电机,其参数如表1: 表1 所选电动机的相关参数电机型号额定功率 (kw)同步转速(r/min) 满载转速(r/min)电机重量 (kw)参考价格(元)Y132M1-6 4 1000 960 73 823 (b)先求取滚筒临界转速由临界状态得: m为马铃薯质量,R为滚筒半径R= 令总传动比为i,则滚筒转速为n=,因此: V= 化解得i12.39,取i=14。3.2 传动装置运动和动力参数的计算3.2.1 传动比分配 根据常用传动机构的主要特征及适用范围: 取V带传动的传动比为3.5,则圆柱齿轮传动比3.2.2 各轴的转速计算: =960r/min r/min r/min3.2.3 各轴输入功率计算: 3.2.4 各轴输入扭矩计算 将上述结果列入表2,以供查用。表2 各轴的的运动参数轴号转速n/(r/min)功率Pkw扭矩T(Nm)I960439.8II274.33.7248129.68III68.573.25452.643.3 V带传动的设计计算 选用普通V带传动,动力机位Y系列三相异步电动机,功率P=4kw,转n=960r/min,每天工作16h,中心距小于600mm。计算项目 计算内容 计算结果 定V带型号和带轮直径工作情况系数 由表 =1.2计算功率 选带型号 由图 A型小带轮直径 由表 取大带轮直径 选 (为滑动率,取=2%)大带轮转速 以上所选参数合理 计算带长求 求 = =109mm初取中心距 a=500mm带长 L=+2a+=199+2500+ L=1648.6mm基准长度 由图 =1800mm 求中心距和包角中心距 = a=577.28mm 600mm小轮包角 = = 求带根数带速 v=4.52m/s传动比 i= i=3.5带根数 由表 =0.78kw;0.94 ;1.01; ; Z= = =3.68 取z=4根 求轴上载荷张紧力 =500+q 500+0.10 =148.9N(由表 q=0.10kg/m)轴上载荷 =2z =26148.9 =1752N带轮结构设计 由于带速v30m/s,带轮用HT200制造。小带轮采用整体式结构,大带轮采用轮辐式结构,且D500mm,轮辐数目取为4.具体结构参数见零件图。综上整理带传动参数如表3:表3 带传动的相关参数小带轮直径 大带轮直径 传动比i带基准长度根数Z中心距a90mm308mm3.51800mm4577.28mm3.4 齿轮传动的设计计算 使用要求:预期使用寿命10年,每年300个工作日,在使用期限内,工作时间占20%。工作有中等振动,传动不逆转,齿轮对称布置。传动尺寸无严格限制,无严重过载。传动比i=4,。 因传动尺寸无严格限制,且为开式传动,故小齿轮用45钢,调质处理,硬度241HB286HB,平均取为260HB,大齿轮用球墨铸铁QT500-7,硬度170HB230HB,平均取为200HB。开式传动的齿轮,主要失效形式是弯曲疲劳折断和磨粒磨损,磨损尚无完善的计算方法,故只进行弯曲疲劳强度计算。计算项目 计算内容 计算结果1.初步计算转矩 由前表查得 =129.68齿宽系数 由表,取=1.0 =1.0弯曲疲劳极限 由图 =500Mpa =350Mpa初步计算的许用弯曲应力 =0.7 =0.7500 =350Mpa =0.7 =0.7350 =245Mpa 值 由表,取=1.45 =1.45初取齿轮齿数 取小齿轮齿数=25 =25齿形系数 由图 =2.63 =2.18应力修正系数 由图 =1.58 =1.81初步计算的齿轮模数m m =1.96 m=3初步宽度b b= =75mm =65mm2.校核计算圆周速度v v= v=1.08m/s精度等级 由表 选9级等级齿数z和模数m 由前计算,m=3; =25,=i=100 =25 =100使用系数 由表 =1.25动载系数 由表 =1.1重合度 = =1.88-3.2()=1.72 =1.72重合度系数 =0.25+=0.25+ =0.69齿间载荷分配系数 由表,= =1.4齿向载荷分配系数 =9.63 由图 =1.25载荷系数K =1.251.11.41.25 K=2.4弯曲最小安全系数 由表 =1.2应力循环次数 =60=601274.3 14400 = =60=60168.57 14400 =0.6弯曲寿命系数 由图 =0.92 =0.98尺寸系数 由图 =1.0许用弯曲应力 = =368Mpa = =274.4Mpa验算 = =158.6Mpa =158.6 150.60Mpa 因传动无严重过载,故不作静强度校核齿轮的结构的设计:小齿轮制成实心式,大齿轮制成圆盘式,具体结构参数见零件图。综上整理齿轮传动的参数如表4:表4 齿轮传动的相关参数模数m小齿轮齿数压力角大齿轮齿数传动比32510043.5 轴系零件的设计计算轴材料选用45钢调质,=650Mpa,=360Mpa。轴的设计计算步骤如下:计算项目 计算内容 计算结果3.5.1 初算轴径d 由表,C=112 =112 =26.72mm 取d=40mm3.5.2 初步计算轴上各段长度 轴承选6208,宽度B=18mm; 小齿轮齿宽b=75mm; 由表: 大带轮宽度B=(Z-1)e+2f =(6-1)15+ 210=95mm轴的结构设计如图3:图3 轴II的结构设计图计算轴上载荷:由前计算:带轮作用轴上载荷=1752N,T=129.68Nm齿轮作用在轴上载荷: =3458N,=129.68Nm3.5.3 绘制轴的弯扭矩图,对危险截面进行校核简化轴上载荷如图4:图4 轴II所受的载荷图其中, =1752N,T=129.68Nm, =3458cos=3249.5N =3458=1182.7N画轴的弯矩图,扭矩图图5 轴II的弯矩图、扭矩图由弯矩图、扭矩图可知B点为危险截面。对B点进行校核计算:M=276.64Nm查表得:=215Mpa,=102.5Mpa,=60Mpa 对于不变的转矩,取 =278N.m所以: =43.43Mpa=60Mpa满足强度要求。轴承选用6208,带轮和齿轮结构见零件图。3.5.4 轴承的校核(1)计算轴承的当量动载荷P: 由式:P=X+Y知, 对不承受轴向载荷的深沟球轴承,X=1,Y=0 由力学相关知识解得:=2599.6N; ;=728.46N =3409.6N =5894.3N 得:=3409.6N;=5894.3N (2)校核计算 轴承的计算额定动载荷,它与所选用轴承型号的基本额定载荷C值必须满足下式要求: C; 为轴承的预期使用寿命, 查表,取=6000h 解得=3409.6=15.76KwC=29.5Kw =5894.3=27.24KwC=29.5Kw 综上:轴承满足使用要求,选用合理3.5.5 轴上键连接的选择及校核 因无特殊要求,选用圆头普通平键,键108,通常(1.61.8)d因此,L(1.61.8)34=54.461.2mm,取L=50mm; 校核计算如下:键的接触长度=L-b=50-10=40mm。键与縠的接触高度=4mm;许用挤压应力查表取=150Mpa;所以键连接所能传递的转矩为:T=0.0040.040.034150=408Nm=129.68Nm。所以,以上选择的参数满足强度要求。合理。3.5.6 轴上螺母的选择因螺母只需一般的固定作用,并无特殊要求,所以选用普通六角螺母M30。3.6 滚筒及其轴系零件的设计、选择与校核。3.6.1 滚筒的结构设计考虑到系统结构的简单,及方便安装,将滚筒与其上的轴制成整体式。轴选用45钢,调质处理。滚筒可以采用由圆钢焊接框架并用细薄铁皮包裹。轴与滚筒焊接成一体。其结构如图6:图6 滚筒及相连轴的结构设计图具体参数见零件图。滚筒的内壁制成内径左大右小的圆锥形,马铃薯由右端入口放入,在滚筒内随滚筒翻转的同时,沿滚筒轴向运动,在滚筒内去皮后,由滚筒左端筒壁上的开口落下。即完成马铃薯的去皮过程。其内壁剖面结构如图7: 图7 滚筒剖视图3.6.2 轴承的选择、校核考虑到滚筒的体积、质量较大,并且不受轴向载荷,选用滚动轴承6218,其内径d=90mm。(1) 求解各轴承受力图8 滚筒轴所受载荷图其中,3017.6N;=3017.6=1098.3N由力学相关知识解得:=3436.13N;=418.53N; =1202.9N; =104.6N;(2)计算轴承的当量动载荷P: 由式:知:对不承受轴向载荷的深沟球轴承,X=1,Y=0 =3640.6N =431.4N 得: =3640.6N; =431.4N (3)校核计算 轴承的计算额定动载荷,它与所选用轴承型号的基本额定载荷C值必须满足下式要求: C=; 为轴承的预期使用寿命, 查表,取=6000h 解得=3640.6=10.6KwC=95.8Kw =431.4=1.256Kw=452.64Nm。所以,以上选择的参数满足强度要求。合理。3.6.4 轴上螺母选择因螺母只需一般的固定作用,并无特殊要求,所以选用普通六角螺母M42。3.7 轴承座、端盖的结构设计3.7.1 轴承座的选材 由于机构运转过程中并无较大冲击载荷,且轴承外径较大,考虑到节约成本,故选用灰铸铁HT300,=290Mpa,硬度190240HB。3.7.2 轴承座的固定方式 轴承座与机架用螺栓联接。3.7.3 轴承座的结构设计 具体结构参数见零件图。3.7.4 轴承座端盖选材、结构设计 端盖选用灰铸铁HT300,=290Mpa,硬度190240HB。用螺栓与轴承座联接。端盖用于限制轴承在轴承座内的轴向位移,且在端盖与轴承座之间加用垫圈,通过换用不同厚度的垫圈即可调整轴承在轴承座内的轴向位置,如图9所示: 图9 轴承与内孔及端盖的转配关系图具体结构参数见零件图。3.8 轴承座联接用螺栓的设计计算 螺栓材料选用45钢,材料的许用拉应力=350Mpa。螺栓直径d的设计计算:(1) 轴左右两轴承座受力如图10所示; 图10 轴II上的两轴承座的受力分析图 对于固定左轴承座的螺栓,预紧力只须满足: ; z螺栓个数,z=2; 螺栓预紧力; 接触面间的摩擦系数,查表取=0.135 ; m接合面数目 ,m=1; 考虑摩擦传力的可靠系数,取=1.3 =8023.4N 螺栓直径d=6.16mm 对于固定右轴承座得螺栓,预紧力必须满足: ; 残余预紧力;其余符号含意同上; =3507.4N 同时螺栓所受总拉力F=+=3507.4+5849.1=9356.5N 螺栓直径d=6.65mm综上,轴上轴承座选用螺栓M8. (2)滚筒左右两轴承座受力如图11所示: 图11 滚筒轴上两轴承座的受力分析图 对于固定左轴承座的螺栓,预紧力只须满足: ; z螺栓个数,z=2; 螺栓预紧力; 接触面间的摩擦系数,查表取=0.135 m接合面数目 ,m=1; 考虑摩擦传力的可靠系数,取=1.3 =2355.6N 螺栓直径d=3.34mm 对于固定右轴承座得螺栓,预紧力必须满足: ; 残余预紧力;其余符号含意同上; =503.6N 同时螺栓所受总拉力F=503.6+5849.1=6352.7N 螺栓直径d=6.5mm综上,滚筒轴上轴承座选用螺栓M8.3.9 机架的结构设计 机架材料选用型钢,由型钢焊接成机架。在机架的结构设计中,主要考虑便于轴承座的安装,以及方便机架上零件间相对距离的调整,具体结构参数见零件图。参考文献 1邱宣怀、郭可谦、吴宗泽等.机械设计M.4版.北京:高等教育出版社.2010.2刘混举、赵河明、王春燕.机械可靠性设计M.北京:国防工业出版社.2010.3杨光、席伟光、李波、陈晓岑.机械设计课程设计M.2版.北京:高等教育出版社.2010.4金清肃、范顺成、范晓珂.机械设计课程设计M.武汉:华中科技大学出版社.2006.5王慧、吕宏、王连明.机械设计课程设计M.北京:北京大学出版社.2011.6于永泗、齐民.机械工程材料M.8版.大连:大连理工大学出版社.2010.7郑文纬、吴克坚.机械原理M.7版.北京:高等教育出版社.2010.8刘鸿文.材料力学M.4版.北京:高等教育出版社.2010.9哈尔滨工业大学理论力学教研室.理论力学M.6版.北京:高等教育出版社.2004.10陈于萍、周兆元.互换性与测量技术基础M.2版.北京:机械工业出版社.2009.11何铭新、钱可强.机械制图M.5版.北京:高等教育出版社.2008.12蒋晓、沈培玉、苗青.AutoCAD2008中文版机械设计标准实例教程M.北京:清华大学出版社.2008.设计小结毕业设计是我们专业课程知识综合应用的实践训练,是我们迈向社会,从事职业工作前一个必不少的过程”千里之行始于足下”,通过这次毕业设计,我深深体会到这句千古名言的真正含义我今天认真的进行毕业设计,学会脚踏实地迈开这一步,就是为明天能稳健地在社会大潮中奔跑打下坚实的基础 说实话,毕业设计真的有点累然而,当我一着手清理自己的设计成果,漫漫回味这10周的心路历程,一种少有的成功喜悦即刻使倦意顿消虽然这是我刚学会走完的第一步,也是人生的一点小小的胜利,然而它令我感到自己成熟的许多,另我有了一中”春眠不知晓”的感悟。通过毕业设计,使我深深体会到,干任何事都必须耐心,细致。毕业设计过程中,许多计算有时不免令我感到有些心烦意乱:有2次因为不小心我计算出错,只能毫不情意地重来但想到今后自己应当承担的社会责任,想到世界上因为某些细小失误而出现的令世人无比震惊的事故,我不禁时刻提示自己,一定要养成一种高度负责,认真对待的良好习惯。这次毕业设计使我在工作作风上得到了一次难得的磨练短短10周是毕业设计,使我发现了自己所掌握的知识是真正如此的缺乏,自己综合应用所学的专业知识能力是如此的不足,几年来的学习了那么多的课程,今天才知道自己并不会用。 最后,我要感谢我的老师们,是您严厉批评唤醒了我,是您的敬业精神感动了我,是您的教诲启发了我,是您的期望鼓励了我,我感谢老师您今天又为我增添了一幅坚硬的翅膀由于本人的设计能力有限,在设计过程中难免出现错误,恳请老师们多多指教,我十分乐意接受你们的批评与指正,本人将万分感谢。23systems. assessing the example of three tractors of the same category, which are exploited in climatic and soil conditions 1. Introduction for agricultural agricultural recognized careful technical, predicting ofcropproduction.Nowadays,theexistingmathematicaloptimiza- tion methods, supported by the high-performance computers, can efficiently resolve the optimization problems (Dette Duffy et al., 1994; Mileusnic, 2007; etc.). The formation of an optimal technical system in order to produce cheaper food, highly impacted reliability of tractors, its maintainability, and the functionality of the system. rounding conditions. Although in the same spirit, some authors have defined effectiveness somewhat differently. In (Ebramhimipour maintainabilityascapacityofthe systemforpreventionandfindingfailuresanddamages,forrenewing operating ability and functionality through technical attending and repairs; and functionality as the degree of fulfilling the functional requirements, namely the adjustment to environment, or more pre- cisely to the conditions in which the system operates. In the case of monitoring reliability and maintainability it is common to monitor the time picture of state (Fig. 1) according to their working conditions is obtained. The model can be used as cri- teria for decision making related to any procedure in purchasing, operation or maintenance of the system, for prediction of repair and maintenance costs. Quality and functionality of the proposed model is shown in effectiveness determination of agricultural machinery, precisely tractors. R. Miodragovic et al./Expert Systems with Applications 39 (2012) 89408946 8941 which the functions of reliability and maintainability can be deter- mined, as well as the mean time in operation and the mean time in failure. The main problem that occurs in forming the time picture of state is data monitoring and recording. In real conditions the ma- chines should be connected to information system which would precisely record each failure, duration and procedure of repair. This is usually expensive and improvised monitoring of the machine performance, namely of its shut downs, is imprecise. Moreover, statistical data processing provided by the time picture of the state requires that all machines work under equal conditions, which is difficult to achieve. As for the functionality of the technical system, there is no common way for its measuring and quantification. This is the reason why in this paper, in order to assess the effectiveness, expertise judgments of the employed in the working process of the analyzed machines will be used. Application of expertise judgments has been largely used in literature, primarily for data processing and the assessment of the technical systems in terms of: risk (Li Wang, Yang, Tanasijevic, Ivezic, Ignjatovic, Zadeh, 1996). Application of fuzzy sets today represents one of the most frequently used tools for solving the problems in various areas of optimization (Huang, Gu, Liebowitz, 1988) in general is also used for solving the optimizations problems from area of agro machinery. In article (Rohani, Abbaspour-Fard, and fuzzy composition of men- tioned indicators into one synthesized. Fuzzy proposition is pro- cedure for representing the statement that includes linguistic variables based on available information about considered techni- cal system. In that sense it is necessary to define the names of lin- guistic variables that represent different grades of effectiveness of considered technical system and define the fuzzy sets that describe the mentioned variables. Composition is a model that provides structure of indicators influences to the effectiveness performance. 2.1. Fuzzy model of problem solving The first step in the creation of fuzzy model for effectiveness (E) assessment is defining linguistic variables related to itself and to reliability (R), maintainability (M) and functionality (F). Regarding number of linguistic variables, it can be found that seven is the maximal number of rationally recognizable expressions that hu- man can simultaneously identify (Wang et al., 1995). However, for identification of considered characteristics even the smaller number of variables can be useful because flexibility of fuzzy sets to include transition phenomena as experts judgments commonly is (Ivezic et al., 2008). According to the above, five linguistic vari- ables for representing effectiveness performances are included: poor, adequate, average, good and excellent. Form of these linguis- tic variables is given as appropriate triangular fuzzy sets (Klir .;l 5 R ; l M l 1 M ; .;l 5 M ; l F l 1 F ; .;l 5 F 1 In the next step, maxmin composition is performed on them. Max min composition, also called pessimistic, is often used in fuzzy alge- bra as a synthesis model (Ivezic et al., 2008; Tanasijevic et al., 2011; Wang et al., 1995; Wang 2000). The idea is to make overall assess- ment (E) equal to the partial virtual representative assessment. This assessment is identified as the best possible one between the worst partial grades expected (R, M or F). It can be concluded that all elements of (R, M and F) that make the E have equal influence on E, so that maxmin composition will be used, which in parallel way treats the partial ones onto the h time of planned shut down due to preventive maintenance. 1995) and OR R M F If we tions that is (according to Fig. 2): with 39 (2012) 89408946 Further, for each outcome its values are calculated (X c ). The outcome which would suit the combination c, it would be calcu- lated following the equations: X c P R;M;E j hi c 3 3 Finally, all of these outcomes are treated with maxmin composi- tion, as follows: (i) For each outcome search for the MINimum value of l R,M,F in vector E c (2). The minimum which would suit the combina- tion o, it would be calculated following the equations: MIN 0 minfl j1;.;5 R ;l j1;.;5 M .;l j1;.;5 F g;for all o 1toO 4 (ii) Outcomes are grouped according to their values X c (3), namely the size of j. (iii) Find the MAXimum between previously identified mini- mums (i) for each group (ii) of outcomes. The maximum which would suit value of j, would be calculated following the equations: MAX j maxfMIN o g; for every j 5 E assessment of technical system is obtained in the form: l E This expression (Fig. 2 tion of to fuzzy cedure (d) between the E which d i E j ;H take into account only values if l j1;.;5 R;M;F 0, we get combina- are named outcomes (o =1toO, where O # C). in the process of synthesis, are also used. Precisely, if we look at three partial indicators, namely their membership function (1), it is possible to make C = j 3 =5 3 combina- tions of their membership functions. Each of these combinations represents one possible synthesis effectiveness assessment (E). E l j1;.;5 ;l j1;.;5 ; .;l j1;2;.5 hi ; for all c 1toC 2 maxmin compositions which by using operators AND provide an advantage to certain elements over the others synthetic indicator. In literature (Ivezic et al., 2008; Wang et al., Fig. 2. Effectiveness fuzzy sets. 8942 R. Miodragovic et al./Expert Systems MAX j1 ; .;MAX j5 l 1 E ; .;l 5 E 6 (6) is necessary to map back to the E fuzzy sets ). Best-fit (Wang et al., 1995), method is used for transforma- E description (6) to form that defines grade of membership sets: poor, adequate, average, good and excellent. This pro- is recognized as identification. Best-fit method uses distance E obtained by maxmin composition (6) and each of expressions (according to Fig. 2), to represent the degree to E is confirmed to each of fuzzy sets of effectiveness (Fig. 2). i X 5 j1 l j E C0l j H j 2 v u u t ; j 1; .;5;H i fexcellent;goodaverage;adequate;poorg7 E i fb i1 ;poor;b i2 ;adequate;b i3 ;good; b i4 ;average;b i5 ;excellentg 10 3. An illustrative example As an illustrative example of evaluation of agriculture machin- ery effectiveness, the comparative analysis of three tractors A 1 B 2 , and C 2 is given in this article. In tractor A a 7.146 l engine LO4V TCD 2013 is installed. Thanks to the reserves of torque from 35%, the tractor is able to meet all the requirements expected in the worst performing farming oper- ations in agriculture. Total tractor mass is 16,000 kg. According to OECD (CODE II) report maximum power measured at the PTO shaft is 243 kW at 2200 rpm with specific fuel consumption of 198 g/kW h (ECE-R24). Maximum engine torque is 1482 Nm at en- gine regime of 1450 rpm. Transmission gear is vario continious transmision. Linkage mechanism is a Category II/III with lifting force of 11,800 daN. In tractors B 2 and C 2 8.134 l engine 6081HRW37 JD is installed, with reserve torque of 40%, and this tractor was able to meet all the requirements expected in the worst performance of the farming operations in agriculture. Total tractor weight is 14,000 kg. Accord- ing to OECD (CODE II) report maximum power measured at the PTO shaft is 217 kW at 2002 rpm with specific fuel consumption of 193 g/kW h (ECE-R24). Maximum torque is 1320 Nm at engine revs of 1400 rpm. Transmission is AutoPower. Linkage mechanism is a Category II/III with lifting force of 10,790 daN. Both models have electronically controlled tractor engine and fuel supply system that meets the regulations on emissions. From the submitted technical characteristics of the tractor A, B and C it is seen that all three tractors are fully functional for l exc. = (0,0,0,0.25,1); l good = (0,0,0.25,1,0.25); l aver. = (0,0.25,1,0.25,0); l adeq. = (0.25,1,0.25,0,0); l poor = (1,0.25,0,0,0). The closer l E (6) is to the ith linguistic variable, the smaller d i is. Distance d i is equal to zero, if l E (6) is just the same as the ith expression in terms of the membership functions. In such a case, E should not be evaluated to other expressions at all, due to the exclusiveness of these expressions. Suppose d imin (i =1,.,5) is the smallest among the obtained distances for E j and leta 1 ,.,a 5 represent the reciprocals of the rel- ative distances (which is calculated as the ratio between corres- ponding distance d i (7) and the mentioned values d imin ). Then, a i can be defined as follows: a i 1 d i =d imin ; i 1; .;5 8 If d i = 0 it follows that a i = 1 and the others are equal to zero. Then, a i can be normalized by: b i a j P 5 m1 a im ; i 1; .;5 X 5 i1 b i 1 9 Each b i represents the extent to which E belongs to the ith defined E expressions. It can be noted that if E i completely belongs to the ith expression then b i is equal to 1 and the others are equal to 0. Thus b j could be viewed as a degree of confidence that E i belongs to the ith E expressions. Final expression for E performance at the level of tech- nical system, have been obtained in the form (10) where Applications 1 Tractor Fendt Vario 936. 2 Tractor John Deere 8520. performing difficult operations for different technologies of agri- cultural production. Tractors B and C have the same technical char- acteristics, and practice is the same type and model, except that the tractor B entered into operation in May 2007, a tractor C in June 2007. A tractor on the experimental farm, which is the technical documentation for the base model, comes into operation in July 2009. The main task of maintaining agricultural techniques is to provide functionality and reliability of machines. Maintenance of all three tractors is done by machine shop owned by the user up- grade option. Ten engineers (analysts) working on maintenance and opera- tion of tractors were interviewed. Their evaluation of R, D and F are given in Table 1. First, the effectiveness of tractor A is calculated. It can be seen that the reliability was assessed as excellent by six out of ten ana- lysts (6/10 = 0.6), as average by three (0.3) and as good by one (0.1). In this way the assessment R is obtained in the form (11): R 0:6=exc; 0:3=good; 0:1=aver; 0=adeq; 0=poor11 In the same way the assessments for M and F are obtained: M 0:4=exc; 0:4=good; 0:2=aver; 0=adeq; 0=poor F 0:5=exc; 0:5=good; 0=aver; 0=adeq; 0=poor In the next step, these assessments are mapped on fuzzy sets (Fig. 1) in order to obtain assessment in the form (1). For example, Reliabil- ity in this example is determined as (11), where it is to linguistic variable excellent joined weight 0.6. Thereby, fuzzy set excellent is defined as: R exc = (1/0, 2/0, 3/0, 4/0.25, 5/1.0) (according to Fig. 1). In this way the specific values of fuzzy set excellent R exc0.6 = (1/(0 C2 0.6), 2/(0 C2 0.6), 3/(0 C2 0.6), 4/(0.25 C2 0.6), 5/(1.0 C2 0.6) are obtained. The remaining four linguistic variables are treated in the same way. In the end for each j =1,.,5 specific membership functions (last row, Table 2) are added into the final fuzzy form (1) of tractor A reliability: l RA 0;0:025;0:175;0:475;0:675 In the same way, based on the questionnaire (Table 1) values for maintainability and functionality are obtained: l MA 0;0:05;0:3;0:55;0:5; l FA 0;0;0:125;0:625;0:62512 These fuzzificated assessments (11) and (12) are necessary to syn- thesize into assessment of effectiveness, using maxmin logics. In this case it is possible to make C =5 3 = 125 combinations, out of which the 48 outcomes. First outcome would be for combination 2-2-3: E 2-2-3 = 0.025,0.05,0.125, where is X 2-2-3 = (2 + 2 + 3)/3 = 2 (rounded as integer). Smallest value among the membership func- tions of this outcome is 0.025. Other outcomes and corresponding values of X c are shown in Table 3. All these outcomes can be grouped around sizes X = 2, 3, 4 and 5. For example, for outcome X = 5 it can be written: E 4C05C05 0:475;0:5;0:625C138;E 5C04C05 0:675;0:55;0:625C138;E 5C05C04 0:675;0:5;0:625C138;E 5C05C05 0:675;0:5;0:625C138 Further, for each of them, minimum between membership function is sought: Table 1 Results of questionnaire. Average x x xx x xx x R. Miodragovic et al./Expert Systems with Applications 39 (2012) 89408946 8943 Analyst Linguistic variables Tractor A Tractor B Excellent Good Average Adequate Poor Excellent Good 1R x x Mx x Fxxx 2R x Mx x Fx 3R x x Mx Fx 4R x x Mx Fx x 5R x x Mx Fxxx 6R x x Mx Fx x 7R x Mx Fx 8R x x Mx x Fx x 9R x x Mx x Fx x 10 R x x Mx x Fx x Tractor C Adequate Poor Excellent Good Average Adequate Poor x x x x x x x x x x x xx x x x x x x x x x with Table 2 Calculation of specific values of fuzzy sets. 12345 0.6/exc. 0 C2 0.6 0 C2 0.6 0 C2 0.6 0.25 C2 0.6 1.0 C2 0.6 0.3/good 0 C2 0.3 0 C2 0.3 0.25 C2 0.3 1.0 C2 0.3 0.25 C2 0.3 8944 R. Miodragovic et al./Expert Systems MINE 4C05C05 minf0:475;0:5;0:625g0:475;MINE 5C04C05 0:55;MINE 5C05C04 0:5;MINE 5C05C05 0:5 Between these minimums, in the end it seeks maximum: MAXX d5 maxf0:475;0:55;0:5;0:5g0:55 Also for other values: X: MAX X =2 = 0.025; MAX X =3 = 0.175; MAX X =4 = 0.55 (Table 1.) 0.1/aver. 0 C2 0.1 0.25 C2 0.1 1.0 C2 0.1 0.25 C2 0.1 0 C2 0.1 0/adeq. 0.25 C2 0 1.0 C2 0 0.25 C2 00C2 00C2 0 0/poor 1.0 C2 0 0.25 C2 00C2 C2 C2 0 P R 0 0.025 0.175 0.475 0.675 Table 3 Structure of MAXMIN composition. Comb. X l MIN 2345 2-2-3 2 0.025,0.05,0.125 0.025 2-2-4 3 0.025,0.05,0.625 0.025 2-2-5 3 0.025,0.05,0.625 0.025 2-3-3 3 0.025,0.3,0.125 0.025 2-3-4 3 0.025,0.3,0.625 0.025 2-3-5 3 0.025,0.3,0.625 0.025 2-4-3 3 0.025,0.55,0.125 0.025 2-4-4 3 0.025,0.55,0.625 0.025 2-4-5 4 0.025,0.55,0.625 0.025 2-5-3 3 0.025,0.5,0.125 0.025 2-5-4 4 0.025,0.5,0.625 0.025 2-5-5 4 0.025,0.5,0.625 0.025 3-2-3 3 0.175,0.05,0.125 0.05 3-2-4 3 0.175,0.05,0.625 0.05 3-2-5 3 0.175,0.05,0.625 0.05 3-3-3 3 0.175,0.3,0.125 0.125 3-3-4 3 0.175,0.3,0.625 0.175 3-3-5 4 0.175,0.3,0.625 0 0.175 3-4-3 3 0.175,0.55,0.125 0.125 3-4-4 4 0.175,0.55,0.625 0.175 3-4-5 4 0.175,0.55,0.625 0.175 3-5-3 4 0.175,0.5,0.125 0.125 3-5-4 4 0.175,0.5,0.625 0.175 3-5-5 4 0.175,0.5,0.625 0.175 4-2-3 3 0.475,0.05,0.125 0.05 4-2-4 3 0.475,0.05,0.625 0.05 4-2-5 4 0.475,0.05,0.625 0.05 4-3-3 3 0.475,0.3,0.125 0.125 4-3-4 4 0.475,0.3,0.625 0.3 4-3-5 4 0.475,0.3,0.625 0.3 4-4-3 4 0.475,0.55,0.125 0.125 4-4-4 4 0.475,0.55,0.625 0.475 4-4-5 4 0.475,0.55,0.625 0.475 4-5-3 4 0.475,0.5,0.125 0.125 4-5-4 4 0.475,0.5,0.625 0.475 4-5-5 5 0.475,0.5,0.625 0.475 5-2-3 3 0.675,0.05,0.125 0.05 5-2-4 4 0.675,0.05,0.625 0.05 5-2-5 4 0.675,0.05,0.625 0.05 5-3-3 4 0.675,0.3,0.125 0.125 5-3-4 4 0.675,0.3,0.625 0.3 5-3-5 4 0.675,0.3,0.625 0.3 5-4-3 4 0.675,0.55,0.125 0.125 5-4-4 4 0.675,0.55,0.625 0.55 5-4-5 5 0.675,0.55,0.625 0.55 5-5-3 4 0.675,0.5,0.125 0.125 5-5-4 5 0.675,0.5,0.625 0.5 5-5-5 5 0.675,0.5,0.625 0.5 MAX 0.025 0.175 0.55 0.55 Finally, we get expression for membership function of effective- ness of tractor A: l EA 0;0:025;0:175;0:55;0:55 Best-fit method (79) and proposed E fuzzy set (Fig. 1) give the final effectiveness assessment for the tractor A: d 1 E;exc X 5 j1 l j E C0l j exc 2 v u u t 0C00 2 0:025C00 2 0:175C00 2 0:55C00:25 2 0:55C01 2 q 0:56899 where is : l E 0;0:025;0:175;0:55;0:55 l exc 0;0;0;0:25;1 For other fuzzy sets: d 2 (E, good) = 0.54658, d 3 (E, aver) = 1.06007, d 4 (E, adeq) = 1.27426, d 5 (E, poor) = 1.29856. for d min d 2 : a 1 1 d 1 =d 2 1 0:56899=0:54658 0:96061; a 2 1:00000;a 3 0:51561;a 4 0:42894;a 5 0:42091: b 1 a 1 P 5 i1 a i 0:96901 0:96901 1 0:51561 0:42894 0:42091 0:28881; b 2 0:30065;b 3 0:15502;b 4 0:12896;b 5 0:12655: Finally, we get the assessment of effectiveness of tractor A, in form (10): E A =(b 1 , excellent), (b 2 , good), (b 3 , average), (b 4 , ade- quate), (b 5 , poor) = (0.28881, excellent), (0.30065, good), (0.15502, average), (0.12896, adequate), (0.12655, poor) In the same way, we get the assessments for other two tractors B and C: E B = (0.23793, excellent), (0.27538, good), (0.20635, aver- age), (0.14693, adequate), (0.13342, poor) E C = (0.17507, excellent), (0.25092, good), (0.25468, aver- age), (0.17633, adequate), (0.14300, poor). Tractor A is in great extent of 0.30065 (in relation to 30 %) as- sessed as good, tractor B in great extent of 0.27538 (27.5%) as- Applications 39 (2012) 89408946 sessed as good, while tractor C is in great extent of 0.25468 (25.5%) assessed as average. It can be concluded that C is the worst, while tractor A is only somewhat better than B, especially if we see with that A is assessed as excellent in the extent of 28.8% while B in the extent of 23.8%. Effectiveness of analyzed tractors can be presented as in Fig. 3., where it can be more clearly seen that tractor A has the biggest effectiveness. If this assessment (E A , E B , E C ) is defuzzificated by center of mass point calculation Z (Bowles if calculated on 10,000 moto-hours, Fig. 3. Relationship of effectiveness of observed tractors. R. Miodragovic et al./Expert Systems it would spend in work 9244 moto-hours. As of the tractor B, out of 10,004 available moto-hours, it spent 9069 moto-hours in work, and tractor C out of 9981 available moto-hours spent 9045 in work. The experiment showed that the more reliable and efficient tractors are the less frequent are delays. In part, this initial advan- tage wiped out worse logistics of delivery of spare parts when it comes to tractor A. in 1100 moto-hours work of the tractor, due to poor logistics in maintaining hoped to eight working days, and it greatly influenced the decline in benefits of maintainability of a given tractor and thus the decline in total exploitation of the same efficiency (Internal technical documentation PKB). 4. Conclusion This paper presents a model for effectiveness assessment of technical systems, precisely agricultural machinery, based on fuzzy sets theory. Effectiveness performance has been adopted as overall indicator of systems quality of service, i.e. as entire measure of technical system availability. Reliability, maintainability and func- tionality performances have been recognized as effectiveness parameters or indicators. Linguistic form can be appointed as the References Bowles, J. B., & Pelaez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203213. Cai, K. Y. (1996).
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