Last updated on December 17th, 2024 at 05:49 pm
Fdaytalk Homework Help: Questions and Answers: Which kind of Al is used for maintenance and quality checks in digital manufacturing?
a) diagnostic
b) predictive
c) functional
d) generative
e) I don’t know this yet
Answer:
First, let’s understand what maintenance and quality checks in digital manufacturing involve:
- Monitoring equipment performance
- Detecting potential issues before they occur
- Ensuring consistent product quality
- Optimizing production processes
Now, let’s check each given option to determine which kind of AI is used for maintenance and quality checks in digital manufacturing.
Given Options: Step by Step Answering
a) Diagnostic AI
- Diagnostic AI is primarily used to identify and diagnose issues. It analyzes data to understand why a problem occurred. It’s often used in maintenance to pinpoint faults and failures after they have occurred.
b) Predictive AI
- Predictive AI uses data, statistical algorithms, and machine learning techniques to forecast future outcomes based on historical data.
- This type of AI is commonly used for predictive maintenance and quality checks. It can predict when equipment is likely to fail, allowing for proactive maintenance, and it can also predict potential quality issues before they occur.
c) Functional AI
- Functional AI refers to AI systems that carry out specific functions or tasks. While functional AI can be used in many applications, it’s not specifically known for maintenance and quality checks.
d) Generative AI
- Generative AI creates new data instances that resemble the training data. It can generate designs, text, images, etc.
- Generative AI is more often used in creative fields, such as design and content creation, rather than maintenance and quality checks.
e) I don’t know this yet
- We’ll consider this if none of the other options seem correct.
Conclusion
Based on the above analysis, the correct answer is:
b) predictive AI
Predictive AI is the best choice because it can:
- Analyze current and historical data to predict when machinery might fail
- Forecast potential quality issues before they occur
- Help schedule maintenance proactively
- Optimize processes to maintain consistent quality
Application: Predictive AI is used to foresee when a machine might fail or when a quality issue might occur, allowing for preventive measures to be taken before a problem arises.
Example: Predictive AI can analyze patterns and predict when a machine part is likely to fail, enabling maintenance to be scheduled before an actual breakdown.
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