Advanced Measurement Systems And Data Analysis Assignment Sample

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Advanced Measurement Systems And Data Analysis Assignment Sample

Introduction of Advanced Measurement Systems And Data Analysis Assignment

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Task 1

The term “Automation” has been described as the process of enriching the mechanism of some machines which can incorporate innovative concepts to maintain the quality as well as consistency of the machines. In the last few years, this company has been working by investing in the “automated machine tools” by evaluating several components by invoking several measuring techniques. 

Aim and objective


This project aims to explore the comprehensive skills of the measuring techniques of the automated machines along with the features of the components, commercial as well as industrial constraints, and the batch size of the machines.


  • To explore the errors on the “CMMS (Coordinate Measuring Machines)”.
  • To comprehend the knowledge of the Surface Measuring machines.
  • To explore the surface parameters.

Discussion on equipment

This company has manufactured the components of several automated machines with a range of dimensions 400mm maximum. The dimensional tolerance of the components has been measured with the value “25 µm” which can play a crucial role in the manufacturing process. In the surface measuring automated machines, the ranges of the surface roughness are 12.5-0.025 µm. In the CMMs, the positions of the components are said the operators have been controlled by the displacement of the components based on the “Cartesian coordinate system”. As illustrated by Lu et al. (2020), the utilization of the CMMs can have the ability to measure the size error of the components. The errors exist in CMMs because of incorporating the “parts machining, calibrating and other drivers”.

There are various techniques to evaluate the errors in CMMs along with flourishing the error detection principles. As per the view of Jin et al. (2020), measuring coordinates can be a great technique to extract errors. The errors in CMMs can be described as "three-position errors, 6 straightness, 9 Angle error, and 3 squareness".


From the above illustration, it has been elaborated that the automated machines developed by this company have been structured with the power operators and the control components. The new automation solution has been explored in order to reduce the pressure of cost and enhance the performance of machines for commercial purposes. The factors of the several components in the equipment have been discussed in this task in order to provide an overview of the measurement techniques. The measurement of the components has been included based on the batch size of the components of the automated machines. The errors of the CMMs have been discussed here to provide comprehensive knowledge on the principles of the errors.

Task 2

In the regression analysis, the least squared value has been evaluated based on the statistics of regression which can define the linear relationship between the variables. In this task, the measurement has been obtained from the 2D microscope by incorporating the values of the dependent (Y) variables and the independent variable (X). The best first of the linear regression line can be measured by the least-squares method which can reduce the vertical distance of the regression line. In this task, the concept of the least square method has been invoked to estimate the center along with the radius of the microscope.


The measurement phase has shed light on the application of regression analysis to evaluate the measurement of least squares (Hu et al., 2019). Based on the perception of the least square method, it has been depicted that the least square algorithm has aided the analysis to evaluate the best fit for the collection of data of the microscope to minimize the sum of the residual point from the mapped curve.

Regression statistics

The above figure has highlighted several parameters of the regression statistics to expose the values of the R squared, Adjusted R squared with the standard error (Desboulets, 2018). The ANOVA table has illustrated the degree of freedom (df) along with the sum of squares and means squares with the appropriate statistics.

Residual output and probability output

The above figure has elaborated the desired outcome of the residual as well as the probability output based on the values of the regression variables. The residual output has been evaluated in 12 observations by defining the difference between the predicted output of the regression model and the measured outcome from the validation. The probability output has been evaluated based on the percentile.

Scatter plot

The above plot has shed light on the mathematical diagram by incorporating the Cartesian coordinates in order to display the values of the data point of the two variables. As per the view of Mohseni and Mokhtarzade (2020), it has been depicted that the data point has been mapped with the circle structure based on the values of the variables by defining the radius by the line.

Line fit plot

The above line fit plot has illustrated the best fit by the line in order to explore the relationship between the data points. It has been found from the above plot that the best fit has been predicted as 75.048 based on the observations.

Normal probability plot

The above plot has shed light on the probability based on the sample percentile along with the prediction in order to extract the “substantive departures” of the normality.

Residual plot

The above residual plot has showcased the residual values on the vertical axis along with the values of the independent variable on the “horizontal axis.”


From the above discussion, it has been depicted that the center, as well as radius, has been exposed in the above analysis beautifully by flourishing the scatter plot with the line. Several plots like the residual plot, probability plot, and the line fit plot have been structured based on the regression statistics. The measurement of the residual values along with the probability has been extracted in this analysis in order to predict the regression statistics. The appropriate equation of the least-squares method has been incorporated in the scatter plot and based on this equation the values of the least-squares have been evaluated in excel. The features of the microscope along with the proper measurement analysis have been enhanced in this task.


Desboulets, L.D.D., 2018. A review on variable selection in regression analysis. Econometrics6(4), p.45.

Hu, Y., Chen, Q., Feng, S., Tao, T., Asundi, A. and Zuo, C., 2019. A new microscopic telecentric stereo vision system-calibration, rectification, and three-dimensional reconstruction. Optics and Lasers in Engineering113, pp.14-22.

Jin, Z., Zhang, Z. and Gu, G.X., 2020. Automated real?time detection and prediction of interlayer imperfections in additive manufacturing processes using artificial intelligence. Advanced Intelligent Systems2(1), p.1900130.

Lu, Y., Xu, X. and Wang, L., 2020. Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems56, pp.312-325.

Mohseni, F. and Mokhtarzade, M., 2020. A new soil moisture index driven from an adapted long-term temperature-vegetation scatter plot using MODIS data. Journal of Hydrology581, p.124420.


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