Answer:
Hello your question is incomplete attached below is the complete question
Answer : Factor of safety for point A :
i) using MSS
(Fos)MSS = 3.22
ii) using DE
(Fos)DE = 3.27
Factor of safety for point B
i) using MSS
(Fos)MSS = 3.04
ii) using DE
(Fos)DE = 3.604
Explanation:
Factor of safety for point A :
i) using MSS
(Fos)MSS = 3.22
ii) using DE
(Fos)DE = 3.27
Factor of safety for point B
i) using MSS
(Fos)MSS = 3.04
ii) using DE
(Fos)DE = 3.604
Attached below is the detailed solution
Answer:
This band gap also allows semiconductors to convert light into electricity in photovoltaic cells and to emit light as LEDs when made into certain types of diodes. Both these processes rely on the energy absorbed or released by electrons moving between the conduction and valence bands.
Explanation:
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This question is incomplete, the complete question is;
For a steel alloy it has been determined that a carburizing heat treatment of 11.3 h duration at Temperature T1 will raise the carbon concentration to 0.44 wt% at a point 1.8 mm from the surface. A separate experiment is performed at T2 that doubles the diffusion coefficient for carbon in steel.
Estimate the time necessary to achieve the same concentration at a 4.9 mm position for an identical steel and at the same carburizing temperature T2.
Answer:
the required time to achieve the same concentration at a 4.9 is 83.733 hrs
Explanation:
Given the data in the question;
treatment time t₁ = 11.3 hours
Carbon concentration = 0.444 wt%
thickness at surface x₁ = 1.8 mm = 0.0018 m
thickness at identical steel x₂ = 4.9 mm = 0.0049 m
Now, Using Fick's second law inform of diffusion
/ Dt = constant
where D is constant
then
/ t = constant
/ t₁ =
/ t₂
t₂ = t₁
t₂ = t₁
/ 
t₂ = (
/
)t₁
t₂ =
/
× t₁
so we substitute
t₂ =
0.0049 / 0.0018
× 11.3 hrs
t₂ = 7.41 × 11.3 hrs
t₂ = 83.733 hrs
Therefore, the required time to achieve the same concentration at a 4.9 is 83.733 hrs
Answer: double click at the top of the page. Or you can also go to home file and click add heading.
Explanation:
Answer:
It is a type of ANOVA that can analyze several independent variables at the same time.
Explanation:
This is the statement that correctly describes the n-way analysis of variance (ANOVA). ANOVA is a type of analysis of variance that can analyze several independent variables at the same time. In this type of analysis, a dependent variable is measured by different levels of independent variables. When the results are obtained, these are assumed to be the consequence of the different levels of the independent variables, plus random error. The computation necessary for this analysis can be done in most types of statistical software.