Dr Wahyu Caesarendra



Dr. Wahyu Caesarendra is an Assistant Professor at the Faculty of Integrated Technologies, Universiti Brunei Darussalam since October 2018. He received a Bachelor of Engineering degree from Diponegoro University, Indonesia in 2005. He worked in the automotive and electrical company prior to joining Diponegoro University as a Lecturer in 2007. He received New University for Regional Innovation (NURI) and Brain Korea 21 (BK21) scholarships for Master study in 2008 and obtained his Master of Engineering (M.Eng) degree from Pukyong National University, South Korea in 2010. In 2011, Wahyu Caesarendra was awarded of University Postgraduate Award (UPA) and International Postgraduate Tuition Award (IPTA) from the University of Wollongong. He received a Doctor of Philosophy (Ph.D.) degree from the University of Wollongong in 2015. He has authored more than 60 research articles in Journals and Conference proceedings. He worked as Postdoctoral Research Fellow in Rolls-Royce@NTU Corp Lab, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore from February 2017 to September 2018. He was a Visiting Assistant Professor at the National Taiwan University of Science and Technology from August 5-11, 2019.


- PhD in Mechanical Engineering, University of Wollongong, Australia (2015)
- M.Eng in Mechanical Engineering, Pukyong National University, South Korea (2010)
- B.Eng (honors) in Mechanical Engineering, Diponegoro University (2005)


- Vibration Condition Monitoring
- Fault Diagnosis and Prognosis
- Mechatronics and Robotics
- Machine Learning and Deep Learning
- Mechanical Design and 3D Printing
- Smart Manufacturing and IoT


Solid Modelling of Bat Flapping Wing Kinematics

Recently, a technology-based bio-inspired has shown a gradually impact on human life. This project aims to study a flying behavior of bat as a preliminary study on the unmanned aerial vehicle (UAV) for video aerial monitoring in Brunei. The ultimate goal for this long-term research study e.g. for monitoring bush fire in Brunei. The objective of this project is to model the kinematics of sophisticated bat flapping behavior using SolidWorks. The bat flapping-wing design project starts by investigating bat flying behavior in an indoor tunnel designed by internship Virginia Tech University students. The bat flying behavior will be captured by high-speed cameras installed inside the tunnel. According to this video camera recording, a detail of a bat flapping wing mechanism will be sketched. A four-bar-linkage mechanism will be used to model the bat flapping-wing movement. Once all parts are designed, the part will be printed using a 3D printer and will be building a prototype.

Applications Invited
Bachelor of Engineering

Development of real-time in vivo corrosion monitoring for absorable medical implant materials

Over 2.2 million people per year require surgery to repair critical bone defects resulting from accidents, diseases, and trauma. The people with a bone fracture are usually undergoing with two times surgery, one for bone repair to attach the metal implant and another one is for removing the metal implant from the body after the bone has been recovered. This second surgery may psychologically inconvenient for the patient because of pain or required an additional cost. Due to this reason, an absorbable metal implant is necessary. The implant has certain desired structural/mechanical properties and a desire to minimize the toxicity effect against the human body. With the development of material science engineering, absorbable metal implants are currently being developed in decades. The objective is one the metal implant attached to the body, it is not necessary to remove the metal implant from the body because it can absorb to the body by a chemical reaction and biological process. The research mainly focuses on the measurement of biomedical materials corrosion inside the body. Where this will affect directly for the health for the human. The present invention related to the field of bio-medical implants made of biodegradable material could be advantageous for a temporary application, such as mechanical support during bone healing. After completion of the healing process, the implant should be removed to avoid long-term side effects.

Applications Invited
Doctor of Philosophy

Real-time surface quality monitoring for adaptive manufacturing process parameters with embedded deep learning method

In recent years, the development of advanced manufacturing technology has been continuously pushed towards a higher demand for specification due to a need for better and more consistent product quality, reduced product cost and shorter manufacturing process. The advancement of the manufacturing process equipped with an intelligent method opens possibilities to answer the existing challenges. To date, the surface quality monitoring of abrasive processes such as grinding and polishing relies on a visual inspection and is typically conducted in offline mode once the entire process completed. The surface quality measurements such as thickness, surface roughness, and material removal rate require a considerable amount of time and skilled operators, because of the repetitive process (de-mounting and mounting) of the component/work coupon to the fixture. This research will develop a real-time monitoring method for the abrasive process by connecting sensor, robot and DAQ device in the hardware setup; and embed a deep learning method into the system integration. A convolutional neural network (CNN) will be used in this research to predict the surface quality parameter. The prediction result of CNN will be connected to the robot to provide a decision on whether the current process is operating in normal mode. If the process is running abnormally, the process parameters will adaptively be altered based on the decision input from the CNN method. This real-time monitoring and the adaptive mechanism will maintain the quality of the component with reduced overall manufacturing time.

Applications Invited
Doctor of Philosophy


- In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning. Robotics and Computer-Integrated Manufacturing (2019).
- In-vivo corrosion characterization and assessment of absorbable metal implants. Coatings (2019).
- Comparison of the utilization of 110 °C and 120 °C heat sources in a geothermal energy system using Organic Rankine Cycle (ORC) with R245fa, R123, and mixed-ratio fluids as working fluids. Processes (2019).
- Effect of in-shoe foot orthosis contours on heel pain due to calcaneal spurs. Applied Sciences (2019).
- Observation to building thermal characteristic of green façade model based on various leaves covered area. Buildings (2019).


- In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning. Robotics and Computer-Integrated Manufacturing (2019).
- In-vivo corrosion characterization and assessment of absorbable metal implants. Coatings (2019).
- An AWS machine learning-based indirect monitoring method for deburring in aerospace industries towards industry 4.0. Applied Sciences (2018).
- Adaptive neuro-fuzzy inference system for deburring stage classification and prediction for indirect quality monitoring. Applied Soft Computing (2018).
- Application of relevance vector machine and logistic regression for machine degradation assessment. Mechanical Systems and Signal Processing (2010).




- NSK Bearing Manufacturing
- SHARP Electronics