Browsing by Author "Shafie Kamaruddin"

Radiofrequency (RF) cannula is one of important component in the treatment of chronic pain known as Radiofrequency (RF) procedure. This procedure has become a common and alternative procedure for chronic pain management since it gives pain relief without causing damage to the nerve tissue. The main issue or problem in existing design related to placement of cannula to target nerves which are ability to firmly hold the cannula and adjustment of the cannula to the target nerve. Thus, through conceptual design development, new design of cannula hub may improve placement of cannula to target nerve and gripping ability. In conceptual design and development, product design specification is established from user's requirements and existing cannula. Six design concept sketching is generated according to product design specifications which were evaluated by user of RF cannula. In evaluating these design concepts, several criteria are defined to satisfied user most through details questionnaire. Final concept design is selected through concept screening and concept scoring phase. A design of prototype cannula hub is created using Catia V5 software with improvised features. This study proposes the fabrication prototype cannula hub using Fused Deposition Modeling (FDM) machine. The prototype cannula hub attached with insulated needle was tested on chicken tissue to compare the performance of new prototype cannula hub with existing cannula during radiofrequency procedure. It is found that, the new prototype cannula hub more effective in term of gripping ability and placement to target tissue. Essentially, this study may benefit a lot in providing alternative design of cannula hub while promising a better performance of RF cannula.

Minimizing friction and wear between contacting surfaces is a central focus in tribology, especially in improving lubricants' performance for engine durability and reliability. Low-viscosity lubricants, while beneficial for fuel efficiency, present challenges in reducing friction and wear. The shift to low-viscosity lubricants, such as SAE 5W30 and SAE 0W20, raises concerns about increased surface contact and potential durability challenges, highlighting the need for advanced additives like graphene and fullerene, as well as coatings, to mitigate these issues, while leveraging machine learning to predict tribological performance and reduce resource-intensive experimental trials. Graphene and fullerene have gained attention as potential additives to enhance the tribological properties of lubricants This study investigates the effects of 0.005 wt.% graphene and fullerene in SAE 0W20 lubricant, along with six different coatings (TiN, AlTiN, CrN, TiAlSN, TiCN, and DLC-AlTiN) on steel block. The primary aim is to develop a dataset that explores the relationships between these additives and coatings with critical tribological parameters—coefficient of friction, wear area, and temperature. Using a rod and ring tribometer, this study measures the tribological performance, with SEM and EDX analyses for surface characterization. The findings indicate that graphene and fullerene additives significantly reduce friction and wear, with graphene showing better heat dissipation. The coatings, particularly CrN, reduce wear and friction, although CrN showed higher wear compared to others. Machine learning models, including Support Vector Regression (SVR), Random Forest (RF), and Decision Tree (DT), were used to predict the tribological parameters. RF outperforms SVR and DT in predicting both friction and wear area, while DT excels in predicting temperature. This research contributes to improving lubrication strategies and predictive modeling, offering valuable insights for enhancing engine performance and durability in automotive engineering.