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natka813 [3]
3 years ago
6

What is a robot’s work envelope?

Engineering
1 answer:
Dmitry_Shevchenko [17]3 years ago
3 0

Answer:

B

Explanation:

A robot's work envelope is its range of movement. It is the shape created when a manipulator reaches forward, backward, up and down. These distances are determined by the length of a robot's arm and the design of its axes. ... A robot can only perform within the confines of this work envelope.

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atroni [7]

why is life I hate life I hate school I hate everything

5 0
3 years ago
Air is compressed by a 40-kW compressor from P1 to P2. The air temperature is maintained constant at 25°C during this process a
AlexFokin [52]

Answer:

the rate of entropy change of the air is -0.1342 kW/K

the assumptions made in solving this problem

- Air is an ideal gas.

- the process is isothermal ( internally reversible process ). the change in internal energy is 0.

- It is a steady flow process

- Potential and Kinetic energy changes are negligible.

Explanation:

Given the data in the question;

From the first law of thermodynamics;

dQ = dU + dW ------ let this be equation 1

where dQ is the heat transfer, dU is internal energy and dW is the work done.

from the question, the process is isothermal ( internally reversible process )

Thus, the change in internal energy is 0

dU = 0

given that; Air is compressed by a 40-kW compressor from P1 to P2

since it is compressed, dW = -40 kW

we substitute into equation 1

dQ = 0 + ( -40 kW )

dQ = -40 kW

Now, change in entropy of air is;

ΔS_{air = dQ / T

given that T = 25 °C = ( 25 + 273.15 ) K = 298.15 K

so we substitute

ΔS_{air =  -40 kW / 298.15 K

ΔS_{air =  -0.13416 ≈ -0.1342 kW/K

Therefore, the rate of entropy change of the air is -0.1342 kW/K

the assumptions made in solving this problem

- Air is an ideal gas.

- the process is isothermal ( internally reversible process ). the change in internal energy is 0.

- It is a steady flow process

- Potential and Kinetic energy changes are negligible.

7 0
3 years ago
A non-inductive load takes a current of 15A at 125V. An inductor is then connected in series in order that the same current shal
Norma-Jean [14]

Answer:

The inductance of the inductor is 0.051H

Explanation:

From Ohm's law;

  V = IR .................. 1

The inductor has its internal resistance referred to as the inductive reactance, X_{L}, which is the resistance to the flow of current through the inductor.

From equation 1;

V = IX_{L}

X_{L} = \frac{V}{I} ................ 2

Given that; V = 240V, f = 50Hz, \pi = \frac{22}{7}, I = 15A, so that;

From equation 2,

X_{L}= \frac{240}{15}

    = 16Ω

To determine the inductance of the inductor,

X_{L} = 2\pifL

L = \frac{X_{L} }{2 \pi f}

  = \frac{16}{2*\frac{22}{7}*50 }

 = 0.05091

The inductance of the inductor is 0.051H.

4 0
4 years ago
Let's model this housing price data! Before we can do this, however, we need to split the data into training and test sets. Reme
Lilit [14]

The program reads in a dataset into a pandas dataframe, and uses the train_test_split function in the sklearn library to split the data into <em>training and test sets</em>. The code goes thus :

import pandas as pd

<em>#import</em><em> </em><em>the</em><em> </em><em>pandas</em><em> </em><em>dataframe</em><em> </em><em>and</em><em> </em><em>alias</em><em> </em><em>it</em><em> </em><em>as</em><em> </em><em>pd</em>

from sklearn.model_selection import train_test_split

<em>#import</em><em> </em><em>the</em><em> </em><em>train_test_split</em><em> </em><em>function</em><em> </em>

housing_df = pd.read_csv('housing price.csv')

<em>#read</em><em> </em><em>in</em><em> </em><em>the</em><em> </em><em>housing</em><em> </em><em>data</em><em> </em>

features_df = df.iloc[:,1:]

<em>#seperate</em><em> </em><em>the</em><em> </em><em>features</em><em> </em><em>from</em><em> </em><em>the</em><em> </em><em>label</em><em> </em><em>;</em>

target_df = df.iloc[:,0]

<em>#put</em><em> </em><em>the</em><em> </em><em>label</em><em> </em><em>into</em><em> </em><em>a</em><em> </em><em>seperate</em><em> </em><em>dataframe</em><em> </em><em>as</em><em> </em><em>well</em><em>.</em><em> </em>

X_train, X_test, Y_train, Y_test = train_test_split(features_df, target_df, test_size = 0.1, random_state = 1)

<em>#uses</em><em> </em><em>tuple</em><em> </em><em>unpacking</em><em> </em><em>to</em><em> </em><em>randomly</em><em> </em><em>assign</em><em> </em><em>the</em><em> </em><em>data</em><em> </em><em>each</em><em> </em><em>of</em><em> </em><em>the</em><em> </em><em>4</em><em> </em><em>variables</em><em>.</em><em> </em>

<em>#</em><em>Test</em><em> </em><em>size</em><em> </em><em>is</em><em> </em><em>test</em><em> </em><em>percent</em><em> </em><em>of</em><em> </em><em>the</em><em> </em><em>entire</em><em> </em><em>dataset</em><em> </em>

Learn more :brainly.com/question/4257657?referrer=searchResults

3 0
3 years ago
How to find Catenary length in sag
Gnom [1K]

Answer:

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6 0
2 years ago
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