In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part.
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product.
Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Powder Bed Fusion Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
Learn more about Powder Bed Fusion here
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Answer: All of these choices are correct.
Explanation:
You didn't give the options to the question. The options include:
testing costs prior to placing the equipment into production
transportation costs
installation costs
All of these choices are correct.
Acquisition cost, is the total cost that is recognized by a company on its books for the purchase of an asset. These costs include the transportation cost, installation cost, shipping cost, testing costs, sales taxes, customs fees, etc.
Therefore, based on the explanation, the correct option is All of the choices are correct.
Answer:
The price should be increased to achieve a balance between supply and demand.
Explanation:
If visitors have to wait long for lift, this suggests that the demand is not matching the supply. In fact demand seems to be higher than supply which causes long wait for lift. An increase in price will cause the demand to fall and hence the supply will meet the demand and would result in less waiting for lifts.
Answer:
3. the sampling distribution of the sample mean is normally distributed.
5. the value of the sample mean varies from sample to sample.
Explanation:
We develop confidence interval for population mean because
a. the sampling distribution of the sampling mean is normally distributed. For us to do this we must first ensure that the sample mean is large enough
B. The value of the sample mean is not the same for all samples it varies from sample to sample. Therefore it it is better that an internal is given with the probability that the parameter falls into it.
Answer:
The answer is: Marc´s effective tax rate is 18.29% equivalent to $18,289.50
Explanation:
Marc is a single filer, so his taxable income of $100,000 falls under the fourth tax bracket ($82,501 to $157,500) with a tax rate of 24%. To calculate Marc´s effective tax rate:
Taxes due = $14,089.50 + [24% x ($100,000 - $82,500)]
= $14,089.50 + (24% x $17,500)
= $14,089.50 + $4,200
= $18,289.50
So Marc´s effective tax rate (ETR) = ($18,289.50 / $100,000) x 100 = 18.29%
Note: The $10,000 Marc earned in interest from municipal bonds (City of Birmingham bonds) are tax exempt, so they are not included in these calculations.
