实验报告
学生:杨昊元      学号: 250603114      院系:自动化25-1     提交时间: 2026-4-05 22:27

批阅中

实验目的和仪器

实验原理

操作步骤

数据处理

一次性测量数据 

  1 2 3 平均 ΔA
L(mm)          
H(mm)          
D(mm)          
table 722.8 723.2 723.0 723.0 0.5 663.0 663.4 662.6 663.0 1.0 47.20 47.22 47.24 47.22 0.05 HLLIPGEigCS9uQcpleVWHQ== 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 kbs8cUEArIzl90tKMNafbYHxVlWcZSPCSNHpyr79Ghet8cA3zN2g8pM9FKB7U4TQaxwCptcELlCLEqij9G39SnL+KuKO9adY8zD9IqZic8HGZd0xaAtW6TswGU5lqPr7QwLI7k6bdO4eE13xWhxhk3G6B50H48YzYcFoQNYnAUqP13ASPgSs7aAmCs0k3xCeE6WXJ71yTRkI1/KT7foFcixemkFNHTW1ywnVEZ5RgqS2fRhr+J32emRKo0fGe5Ih IQRdJptF5P+YF28Mh9w+o3eO4UY/ozPd6yjucHOcjA4PBM2VhaEJUYZk7eZpFVEUXNO1BjPvLG3E5kGinWJ3joBpTqqkAMS9EuyaddtO33DMTEAf19Encf5kQgCD4yU4KNmHZUwhEgbA1AWeneubFyarh7W3prsK3+weElGaZOn7RJ709aZsyv0Cl+94k29G39QguSSY5avlU4QYPlM1Fw==

 金属丝直径测量数据

序号i 1 2 3 4 5 6 平均值
直径视值d视i(mm)              
table 0.851 0.851 0.852 0.851 0.852 0.851 0.851 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 kbs8cUEArIzl90tKMNafbYHxVlWcZSPCSNHpyr79Ghet8cA3zN2g8pM9FKB7U4TQaxwCptcELlCLEqij9G39SnL+KuKO9adY8zD9IqZic8HGZd0xaAtW6TswGU5lqPr7QwLI7k6bdO4eE13xWhxhk3G6B50H48YzYcFoQNYnAUpbLLGcwOhPEjbEem3SYUF3vmNDm3d7hh47AzLNP/zFchd/9yP70RZoWnaZ12PSwoG99mK5TAGV4mWZthpo7Dq8sDRvMTyzFXGZpiDFyCDJEKtZUcIlsmZ8ri+Cvl+UKJ3NvAotERajAfz3JyV1vNwFwgjMng/iZGnisvL5K2lp+k5lHh7C4r468BDUJBthXEYpc7hlzb30liKP2l8PwnoH42IeWvYsbsu+l+kc1NU9qPufB5EqteQSMsboQdZU17oK2QcfvhKCiZN5yXU20vhcQChqoiqfJ21j7fT38NoWjA== J6ZOrFm48WqOA4scq84Fc9WQJMP5SQSZJkTy24qd+KlwnRBGwhoz3hJoFCPk1Gp11VtIUuXvmpyirVbFgblN3D+fGgQ3WsUm6MxpZ9EbaKeC1ERn09clXwOOqKNb0nHI

螺旋测微器零差d0=(1)mm

datafill 0.002 HLLIPGEigCS9uQcpleVWHQ== HLLIPGEigCS9uQcpleVWHQ== HLLIPGEigCS9uQcpleVWHQ== HLLIPGEigCS9uQcpleVWHQ==

表3 加减力时刻度与对应拉力数据

序号i 1 2 3 4 5 6 7 8 9 10
拉力视值mi(kg)                    
加力时标尺刻度xi+(mm)                    
减力时标尺刻度xi-(mm)                    
平均标尺刻度(mm)xi=(xi++xi-)/2                    
标尺刻度改变量(mm)Δxi=xi+5-xi          

 

table 0.00 1.00 1.99 3.00 3.98 4.98 5.99 6.99 8.00 9.00 15.0 18.6 22.1 25.0 28.0 30.9 34.2 37.6 40.7 43.8 14.8 18.1 21.2 24.6 27.7 30.8 34.0 37.1 40.1 43.3 14.9 18.4 21.7 24.8 27.9 30.9 34.1 37.4 40.4 43.6 16 15.7 15.7 15.6 15.7 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E=8mgLHπd2D·1Δx=(1)x1011N/m2

datafill 2.02 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 IQRdJptF5P+YF28Mh9w+o0xnyqffY+cpq5ok/zWkudnGtUil1O7UApaNmph42AmRwhc+GQXLMdzAC0qJu2IUA2BX2/sVnkU8G1gEAFeQFy49kEdlsxZxQMS2P6iLHnE35CoUPfo8YOIMX0XwR4rATeI4o2rBhab7s4QuEUfaYl4XZWQ+J5KCT3H09pvNjVPHUBfEI3/qxoa2zjyGKsVddMzjidIQWXx8H5aXlLT1CMA=

ΔEE=(ΔLL)2+(ΔHH)2+(ΔDD)2+(2Δdd)2+(Δ(Δx)Δx)2+(Δ(m)m)2 =(1)

datafill 0.019 HLLIPGEigCS9uQcpleVWHQ== HLLIPGEigCS9uQcpleVWHQ== HLLIPGEigCS9uQcpleVWHQ== HLLIPGEigCS9uQcpleVWHQ==

ΔE=ΔEE·E=(1)x1011(N·m-2

datafill 0.04 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 kbs8cUEArIzl90tKMNafbYHxVlWcZSPCSNHpyr79Ghet8cA3zN2g8pM9FKB7U4TQaxwCptcELlCLEqij9G39SnL+KuKO9adY8zD9IqZic8HGZd0xaAtW6TswGU5lqPr7QwLI7k6bdO4eE13xWhxhk3G6B50H48YzYcFoQNYnAUq7HWaxeF6waweGMODVPlHXvF8J+91uCR5Z2hZhx1omzNzMR95Pan8IEFG4hIDv91VEph+xEElTS6ssKg9KupUJ5CXflgKARLuDEYCg1qOh5SN5J61woCPulpp2ruWZsHPfwLUMuHcmzuDxvnlU561+M22VjYyYkWmzidu4Fe/I8CMNVqgl+nJmcVTjPQfq7iMxNvaME3U8AHlntxu5jPrg IQRdJptF5P+YF28Mh9w+o1tR/wj40O9xBbxoGlMB9uINlCshX2CP4iXPtzW2Mp52Mw4wQ0wnplnkG2DiCD53j0hJXM5ivGZl221/qVlFIc/j5GceEkuWG49oWMbjeQVx

E=E±ΔE=((1)±(2))x1011(N·m-2

datafill 2.02 0.04 aOb7hPnQYJYnMSTxiIcTqh9o3Ddl7mnk0Hhz2R6Ld+ujWwnT1Emy6doEx9rd9i1KYzMfeCE2KLlIDLwAOVirtmJE9AzyWTyI+kwKBtXydEQ19av/OLc/8QANVKmyEBzO2RKknDskx0AFVzByALoZsmLA0RiwzCRVaudbPrazILEOYJUuBDvKxIFsiN5f5ebby9LZDOEEkUeS3G/miMC8PURRG3fAcndleMagj4ceIYTpG0QTPAoGyJhvjWrvc421NXf69hlftz+U6MngNzwhds925/czkAQL/a0Qe9CXibRSowzkkal+nXzdj86UYljpm66Nz5HOxJ9fMHpugptktKHO2LBn/wKqqf4ygH//IJI= 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 kbs8cUEArIzl90tKMNafbYHxVlWcZSPCSNHpyr79Ghet8cA3zN2g8pM9FKB7U4TQaxwCptcELlCLEqij9G39SnL+KuKO9adY8zD9IqZic8HGZd0xaAtW6TswGU5lqPr7QwLI7k6bdO4eE13xWhxhk3G6B50H48YzYcFoQNYnAUrZ5j0UJ5aJWND8xTCNlNtUWBC2MsZiG8aOutHuDZcW0A== IQRdJptF5P+YF28Mh9w+o+E0os0xB8nExsDfQfqyeRgB5tdnidExai/DmnQUQ3nv

思考题

1.光杠杆有什么特点、怎样提高光杠杆测量微小长度变化的灵敏度?

2.是否可用作图法求杨氏弹性模量?如果胁强为横轴,胁变为纵轴作图,图线应是什么形状?

原始数据