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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The answer is C- The entire economy
Answer: $267,300
Explanation:
The cash received will be:
= Net book value + gain
Net book value:
Annual depreciation = (1,260,000 - 84,000) / 5 years
= $235,200 per year.
Depreciation up until January 1, 2021 = 235,200 * 4
= $940,800
Depreciation up until May 1, 2021 = 940,800 + ( (235,200 / 12 months) * 4 months )
= $1,019,200
Net book value = 1,260,000 - 1,019,200
= $240,800
Cash received = 240,800 + 26,500
= $267,300
Answer: 1,425.2 units
Explanation:
Recorder point
:
Lead time = 4 weeks
Expected demand during this time is
= No. of weeks × Weekly demand
= 4 × 273
= 1,092 units
Standard Deviation = 95 units
Standard Deviation for the 4 week period is:
= 170 units
At the 95% probability level, the z-score is 1.96 (From the Z- table)
Safety Stock = Z-value × Standard Deviation for the 4 week
= 1.96 × 170 units
= 333.2 units
Recorder point = Safety stock + expected demand during the time period so,
= 333.2 units + 1,092 units
= 1,425.2 units
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Hope this helps!
