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A square-thread power screw is used to raise or lower the basketball board in a gym, the weight of which is W = 100kg. See the f

KIM [24]

Answer:

power = 49.95 W

and it is self locking screw

Explanation:

given data

weight W = 100 kg = 1000 N

diameter d = 20mm

pitch p = 2mm

friction coefficient of steel f = 0.1

Gravity constant is g = 10 N/kg

solution

we know T is

T = w tan(α + φ ) $\frac{dm}{2}$ ...................1

here dm is = do - 0.5 P

dm = 20 - 1

dm = 19 mm

and

tan(α) = $\frac{L}{\pi dm}$ ...............2

here lead L = n × p

so tan(α) = $\frac{2\times 2}{\pi 19}$

α = 3.83°

and

f = 0.1

so tanφ = 0.1

so that φ = 5.71°

and now we will put all value in equation 1 we get

T = 1000 × tan(3.83 + 5.71 ) $\frac{19\times 10^{-3}}{2}$

T = 1.59 Nm

so

power = $\frac{2\pi N \ T }{60}$ .................3

put here value

power = $\frac{2\pi \times 300\times 1.59}{60}$

power = 49.95 W

and

as φ > α

so it is self locking screw

8 0

3 years ago

wo companies, Ajax Co. and Boho Inc., were negotiating a merger. In the course of the negotiations, an Ajax representative told

boyakko [2]

Answer:

Yes

Explanation:

If the Ajax representative fails to correct the previous statement this can cause misrepresentation.

4 0

2 years ago

How do you keep the weight evenly distributed on a trailered boat?

Neporo4naja [7]

C because i know what i’m taking about

7 0

3 years ago

2. Ackermann's Function is a recursive mathematical algorithm that can be used to test how well a system optimizes its performan

oee [108]

The python program is an implementation of the Ackermann function that a system optimizes its performance of recursion.

As per the question,

Here is an implementation of the Ackermann function in Python:

def ackermann(m, n):

if m == 0:

return n + 1

elif n == 0:

return ackermann(m - 1, 1)

else:

return ackermann(m - 1, ackermann(m, n - 1))

# get input values for m and n from the user

m = int(input("Enter an integer value for m: "))

n = int(input("Enter an integer value for n: "))

# calculate and print the result of the Ackermann function

result = ackermann(m, n)

print("Ackermann ({},{}) = {}".format(m, n, result))

This implementation follows the logic described in the prompt, using a recursive function to calculate the result of the Ackermann function for the given values of m and n.

To learn more about the Python Program click here:

brainly.com/question/15061326

#SPJ1

5 0

10 months ago

Which of the following is the LEAST-Likely cause of tire wear? A) Underinflation B) Braking C) Acceleration D) Tire rotation

mihalych1998 [28]

Answer:

Tire rotation is the least likely cause of tire wear. So, the option D is correct.

Explanation:

Step1

Under-inflation is the process of tire failure under low pressure. This contributes the wear on tire.

Step2

On breaking, kinetic energy changes to heat energy because of rubbing of tire. So, rubbing action increases the wear on the tire.

Step3

Acceleration on the vehicle increases the rubbing action as well as the wear and tear on the tire. So, acceleration is an also a major cause of tire wear.

Step4

Tire rotation has least amount of wear and tear due to no rubbing action. It has less amount surface contact with the surface in rotation.

Thus, tire rotation is the least likely cause of tire wear. So, the option D is correct.

4 0

2 years ago