Alternative weight determination of mango (Mangifera indica L.) by natural frequency analysis

Date

6-2016

Degree

Bachelor of Science in Agricultural and Biosystems Engineering

Major Course

Major in Agricultural Power and Machinery Engineering

College

College of Engineering and Agro-Industrial Technology (CEAT)

Adviser/Committee Chair

Delfin C. Suministrado

Restrictions

Restricted: Not available to the general public. Access is available only after consultation with author/thesis adviser and only to those bound by the confidentiality agreement.

Abstract

A cantilever beam system experiencing simple harmonic motion (SHM) was fabricated and formulated to determine the weight of mango using natural frequency analysis. The system is composed of an Arduino UNO board, ADXL345 accelerometer, connecting wires, breadboard, and stainless steel sheet metal. Two programs, Frequency Detector and Frequency and Weight Detector, were created using National Instruments LabVIEW 2014. Thirty carabao mango fruits of varying sizes were used during testing. The actual weights of the samples were obtained using a digital weighing scale. The size classification of each mango is based on its actual weight and the predicted weight were also determined. The percent error using natural frequency analysis was 2.89%. Moreover, the accuracy of natural frequency analysis in size classification was determined and found that 29 out of the 30 samples were correctly classified. A verification run was performed to check whether the three frequencies are similar or close to one another. A t-test analysis was performed to check the level of significance and it was found out that the predicted frequency and detected frequency have no significant differencce, the detected frequency and theoretical frequency have a highly significant difference, and the same was true for the predicted and theoretical frequency. The average percent error between the predicted and detected frequency was computed to be 1.10%

Language

English

Location

UPLB College of Engineering and Agro-Industrial Technology (CEAT)

Call Number

LG 993.5 2016 A2 /M46

Document Type

Thesis

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