Question 2: Problem Statement: In the automotive manufacturing industry, ensuring consistent quality in production processes is crucial to meet customer expectations and maintain competitiveness. Your task is to perform Statistical Process Control (SPC) analysis on a dataset of dimensional measurements taken from automotive components and draw conclusions based on your analysis. Data: Below is a dataset of dimensional measurements (in millimeters, mm) collected from 30 automotive components produced in a manufacturing facility: 32.1, 32.2, 32.0, 32.3, 32.1, 32.2, 32.4, 32.3, 32.2, 32.1, 32.0, 32.1, 32.2, 32.3, 32.4, 32.2, 32.1, 32.0, 32.3, 32.1, 32.2, 32.1, 32.0, 32.3, 32.4, 32.2, 32.1, 32.0, 32.3, 32.2 1. Calculate Control Limits: - Compute the mean (X-bar) and standard deviation (σ) of the dimensional measurements. Determine the Upper Control Limit (UCL) and Lower Control Limit (LCL) using the mean and standard deviation. 2. Construct X-bar Chart: - Plot the dimensional measurements on a time-ordered X-bar chart. - Add the UCL and LCL lines to the chart. 3. Analyze the Chart: - Interpret the X-bar chart by examining the plotted data points relative to the control limits. - Identify any points that fall outside the control limits or exhibit unusual patterns. 4. Draw Conclusions: - Based on your analysis, make a decision regarding the stability of the manufacturing process. - Provide recommendations for further action if necessary.
Question 2: Problem Statement: In the automotive manufacturing industry, ensuring consistent quality in production processes is crucial to meet customer expectations and maintain competitiveness. Your task is to perform Statistical Process Control (SPC) analysis on a dataset of dimensional measurements taken from automotive components and draw conclusions based on your analysis. Data: Below is a dataset of dimensional measurements (in millimeters, mm) collected from 30 automotive components produced in a manufacturing facility: 32.1, 32.2, 32.0, 32.3, 32.1, 32.2, 32.4, 32.3, 32.2, 32.1, 32.0, 32.1, 32.2, 32.3, 32.4, 32.2, 32.1, 32.0, 32.3, 32.1, 32.2, 32.1, 32.0, 32.3, 32.4, 32.2, 32.1, 32.0, 32.3, 32.2 1. Calculate Control Limits: - Compute the mean (X-bar) and standard deviation (σ) of the dimensional measurements. Determine the Upper Control Limit (UCL) and Lower Control Limit (LCL) using the mean and standard deviation. 2. Construct X-bar Chart: - Plot the dimensional measurements on a time-ordered X-bar chart. - Add the UCL and LCL lines to the chart. 3. Analyze the Chart: - Interpret the X-bar chart by examining the plotted data points relative to the control limits. - Identify any points that fall outside the control limits or exhibit unusual patterns. 4. Draw Conclusions: - Based on your analysis, make a decision regarding the stability of the manufacturing process. - Provide recommendations for further action if necessary.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:Question 2:
Problem Statement:
In the automotive manufacturing industry, ensuring consistent quality in production processes is
crucial to meet customer expectations and maintain competitiveness. Your task is to perform
Statistical Process Control (SPC) analysis on a dataset of dimensional measurements taken from
automotive components and draw conclusions based on your analysis.
Data:
Below is a dataset of dimensional measurements (in millimeters, mm) collected from 30
automotive components produced in a manufacturing facility:
32.1, 32.2, 32.0, 32.3, 32.1, 32.2, 32.4, 32.3, 32.2, 32.1, 32.0, 32.1, 32.2, 32.3, 32.4, 32.2, 32.1,
32.0, 32.3, 32.1, 32.2, 32.1, 32.0, 32.3, 32.4, 32.2, 32.1, 32.0, 32.3, 32.2
1. Calculate Control Limits:
- Compute the mean (X-bar) and standard deviation (σ) of the dimensional measurements.
- Determine the Upper Control Limit (UCL) and Lower Control Limit (LCL) using the mean
and standard deviation.
2. Construct X-bar Chart:
- Plot the dimensional measurements on a time-ordered X-bar chart.
- Add the UCL and LCL lines to the chart.
3. Analyze the Chart:
- Interpret the X-bar chart by examining the plotted data points relative to the control limits.
- Identify any points that fall outside the control limits or exhibit unusual patterns.
4. Draw Conclusions:
- Based on your analysis, make a decision regarding the stability of the manufacturing process.
- Provide recommendations for further action if necessary.
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VIEWStep 2: Compute the mean and standard deviation(σ) of the dimensional measurements.
VIEWStep 3: Determine upper control limit(UCL) and lower control limit(LCL) using mean and standard deviation.
VIEWStep 4: Construct X-bar chart for the obtained UCL and LCL lines.
VIEWStep 5: Interpret the X-bar chart by examining the plotted data points relative to the control limits.
VIEWStep 6: Make a decision regarding stability of the manufacturing process.
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