Simulation of fault detection system of automotive coil spring by using acoustic method

• Main Authors: M. Haiqal, Hamdan, Mohd Zuhaifi, Zainol, Zubair, Khalil
• Format: Article
• Language: English
• Published: Penerbit UMP 2022
• Subjects: TJ Mechanical engineering and machinery; TS Manufactures
• Online Access: http://umpir.ump.edu.my/id/eprint/35766/1/Simulation%20of%20fault%20detection%20system%20of%20automotive%20coil%20spring.pdf; http://umpir.ump.edu.my/id/eprint/35766/; https://doi.org/10.15282/jmmst.v6i2.8565; https://doi.org/10.15282/jmmst.v6i2.8565
• Institution: Universiti Malaysia Pahang

Due to rising client demand and accessibility to financing, local automotive manufacturer must become cost-competitive against well-known imported brand. As a result, manufacturer is facing more challengers in cost-effectiveness, manufacturing time, as well as quality of their production. Each product that reaches consumer are expected to be excellent in quality, and reliability. However, this could be a problem when quality check (QC) inspections are done using batch sampling method. This method only scans several samples due to complexity of structure and hard to detect fault occurred on the sample. Thus, this study is proposed to find a solution using acoustic method fault detection system to enable 100% automated inspection. This study focusses on automotive coil spring for its sample. Previous study has shown that each different sample conditions has its own distinctive vibration pattern when forced vibrated using same frequency of vibration. The study is done using coil spring model on Ansys simulation software platform that then verified against reference experimental data. Next, the model is used to simulate various fault conditions in order to recognize each different distinctive vibration pattern to reduce consumed cost and time to study the pattern trend. Results to this study shown that healthy and faulted coil spring’s vibration pattern were distinctive clear and easily recognizable. Thus, it is concluded that it is possible to automate 100 % inspection within manufacturing line.