Optimization Process By Using Design Of Experiment (DOE) Method To Increase Performance Of Fiber Optic Glucose Sensor

Muhammad Akmal, Zohizam (2015) Optimization Process By Using Design Of Experiment (DOE) Method To Increase Performance Of Fiber Optic Glucose Sensor. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text (24 Pages) Optimization Process By Using Design Of Experiment (DOE) Method To Increase Performance Of Fiber Optic Glucose Sensor.pdf - Submitted Version Download (554kB)

Abstract

Basically, Design of Experiment (DOE) is a statistical technique used in quality control for planning, analysing and interpreting sets of experiments aimed at making wise decisions without incurring a high cost and consuming much time. The problems occurred can be analyzed and an improvement action can be done immediately. There are a lot of advantages of using DOE technique and for example the number of experiments need to be carried out can be define and stated clearly according to the methods that involved in this technique such as Full Factorial, Fractional Factorial, Taguchi Method and etc. Amongst the various types of DOE which is Factorial Design of Experiment is one of the methods in which my research design will be focusing on. Factorial design is planned to study non-direct functions and connections between factors influencing reaction. Through the factorial design method, I am able to determine the optimum factor which will contribute most to the fiber optic glucose sensor and reduce the variation involve. Ultimately, we could obtain a more precise and better result by repeating using several type of factorial. Thus, in order to perform the factorial design of experiment method, we will be assisted by tabulated parameters in generating outputs based on the variation of different combinations of factors. The optical fiber glucose sensor experiment is carried out within the university compound. After collecting the data needed, we are able to plot graphs and observe the output so that we could have a clearer understanding of the significance of each factor that we used in order to get an optimum output.

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