Comparison of Conventional and Non-conventional Manufacturing processes for Production of Motorbike Helmet - A Life Cycle Assessment ApproachAboraas, Mohammed Yousef; Rehman, Ahmed; Gulzar, Saad
doi: 10.1088/1742-6596/2933/1/012028pmid: N/A
This study presents a comparative Life Cycle Assessment (LCA) of helmet manufacturing processes, focusing on Fused Deposition Modelling (FDM) and conventional manufacturing methods. The research evaluates environmental impacts across three key categories: Global Warming Potential (GWP), Non-renewable Energy Consumption, and Ozone Layer Depletion. Results reveal that helmets produced via FDM exhibit significantly higher environmental impacts—13 times greater GWP, seven times higher non-renewable energy use, and fifteen times more severe ozone depletion compared to conventional methods. These disparities stem from FDM’s energy-intensive nature and reliance on non-renewable resources. To mitigate these challenges, recommendations include adopting renewable energy in production, improving energy efficiency in 3D printing technologies, and transitioning to eco-friendly materials. These measures aim to align helmet manufacturing with global sustainability objectives, particularly Sustainable Development Goals (SDGs) 9, 11, and 12, and promote sustainable manufacturing practices. By providing actionable insights, this study contributes to advancing greener industrial methods and informs industry stakeholders and policymakers in their pursuit of environmental sustainability.
Lumped parameter model for 2D Dynamics Vibration AbsorberSaad, Fazidah; Hamid, Muhammad Najib Abdul; Yusuf, Zainal Nazri Mohd; Mazlan, Ahmad Zhafran Ahmad
doi: 10.1088/1742-6596/2933/1/012022pmid: N/A
The Dynamic Vibration Absorber (DVA) is a common and simple solution which can be easily tailored to eliminate resonant vibration of a vibrating system. A DVA is equipped by a spring/mass system that is installed to vibrating system which is usually called as the primary system to lessen the resonant response of it. The DVA is designed to have sufficient weight to transform the resonance frequency of the single degree of freedom primary system into two degree of freedom with two natural frequencies. This happened by properly tuning the DVA mass and stiffness to the system so that the initial resonant peak of the primary system will vastly reduce. Practically, it is absurd to design a DVA without using numerical solutions and intricate calculations to dampen a system. Therefore, the objective of this research is to produce a simple and resolute correlations DVA parameters simulation by using MATLAB Simulink to be used in designing and analysing the DVA system effectiveness. In this research, the mass ratio of 0.25 is considered during the initial design step of the DVA as this is the optimum mass ratio of the primary and DVA. Subsequently, the parameters of DVA system are developed in 2D lumped mass model using MATLAB Simulink for simplicity and the output is obtained in frequency and amplitudes. The results show the effects of vibration reduction vary depending on whether the frequency of the DVA is the same as the primary system’s natural frequency or not. It is proved that when the DVA is operated at the system’s resonance (122.96rad/s) with the highest amplitude, the vibration reduction is great, at 99.99%. When the DVA is applied at frequencies lower or higher than the primary system’s natural frequency, the vibration reduction is acceptable between 64.29% (at 128 rad/s) and 78.56% (at 110 rad/s). These data indicate that the DVA is most effective at the same frequency as the primary system, but when used at other frequencies, it is still capable of reducing the vibration. This has established that all parameters of the system can be employed in the constructed MATLAB Simulink lumped model of the DVA to optimize the DVA design with the aim to reduce the primary system’s resonant frequency.
Influence of Surface Modification and MDP-based Primer Treatment on the Shear Bond Strength of Novel Zirconia-Dentine InterfacesAminuddin, Nurul Shayhiera; Jamadon, Nashrah Hani; Hadi, Nurul Hannani Abdul; Amril, Muhammad Sufiyan; Yew, Hsu Zenn
doi: 10.1088/1742-6596/2933/1/012011pmid: N/A
This study investigates the impact of surface modification of zirconia and the application of MDP-based primer on the shear bond strength (SBS) between novel zirconia and dentine. The novel zirconia blocks were produced using a colloidal processing and slip casting method, developed at UKM. The sintered zirconia blocks were divided into three groups based on surface modification: air abrasion, diamond grinding, and a control group with no surface treatment. Surface roughness measurements and morphological observation was conducted. The samples were further subdivided into groups depending on whether an MDP-based primer was applied prior to cementation to the dentine. Bonding was achieved using resin cement light curing. The SBS testing was performed and analysed using two-way ANOVA. Results showed that the control group exhibited the highest surface roughness, followed by the air abrasion and diamond grinding groups. However, no significant difference was found between the control and air abrasion groups. The application of MDP-based primer significantly increased the SBS compared to non-primer-treated subgroups. The highest SBS was observed in the air abrasion group (37.57 ± 1.88 MPa), followed by the diamond grinding group (30.34 ± 1.52 MPa), and the control group (22.65 ± 1.13 MPa). Overall, surface modification altered the zirconia surface, and the application of MDP-based primer significantly improved the SBS between novel zirconia and dentine.
Implementation and Validation of the Cantas Electro’S Vibration Reduction MechanismSaad, Wan Aliff Abdul; Idris, Nurzarifah Zakirah; Ahmad, Zair Asrar; Hasan, Muhammad Danial Abu; Isham, Muhammad Firdaus; Talib, Mat Hussin Ab; Saufi, Mohd Syahril Ramadhan Mohd; Samsuri, Saiful Farhan Mohd
doi: 10.1088/1742-6596/2933/1/012027pmid: N/A
The Malaysian palm oil industry has grown substantially, emphasizing the need for efficient harvesting within a 10–12-day cycle. The CANTAS motorized cutter, introduced in 2007, has enhanced productivity by harvesting fresh fruit bunches from trees up to seven meters high. This research focuses on reducing health risks from Hand-Arm Vibration Syndrome (HAVS) by validating vibration reduction strategies for the CANTAS tool. The study investigates the performance of different design and use materials thermoplastic polyurethane (TPU), used in rubber bushings (RB), webbing bushings (WB), and a combination of rubber and webbing bushings (CRWB). Using triaxial accelerometers, the results demonstrate significant reductions in HAV levels, contributing to improved worker safety and operational efficiency.
Optimizing Railway Safety by Analyzing Human Reliability Techniques - A reviewAliza, M. E. M.; Yusop, A. F.; Hamidi, M. A.; Nor, M. A. M.
doi: 10.1088/1742-6596/2933/1/012014pmid: N/A
Human reliability analysis (HRA) is a critical component in ensuring the safety and efficiency of railway engineering. As railway systems grow more complex, the methodologies used to assess and improve human reliability must also advance. This review provides a comprehensive analysis of the evolution of HRA, from the first-generation techniques to the third-generation approaches currently in use. Through a broad survey of the literature, comparative analysis, and detailed case studies, this review traces the development of HRA methods, showing the evolution from traditional techniques to modern hybrid approaches. The review also emphasizes the significance of hybrid Human Error Assessment and Reduction Technique (HEART) methods, which integrate multiple HRA approaches to provide a more comprehensive and accurate assessment of human reliability. The hybrid technique offers a more accurate estimation than standard methods, as evidenced by the determined Pearson coefficient of 0.9990 between the simulation findings and the HEP values of HEART-related methodologies. It also explores the integration of human factors into railway safety systems, underscoring the importance of considering human-machine interactions and the cognitive and behavioural aspects of railway operations. Key findings indicate that while traditional HRA methods laid the groundwork, there is a growing need for continuous innovation to address the increasing complexity of railway systems. This includes the development of hybrid models that combine insights from various HRA techniques and the incorporation of advanced human-machine interaction paradigms to further minimize human error rates. The objective of this review is to offer recommendations for future research in the field of HRA for railway engineering. It advocates for the development of advanced hybrid models with the use of cutting-edge technology like machine learning and artificial intelligence. By combining historical insights with modern technological advancements, the goal is to create more robust and reliable HRA methods that can better support the safety and efficiency of railway operations.
Application of Laser-induced Fluorescence for Thickness and Temperature Measurement in MicrochannelRosli, Nurrina; Zailani, Nina Ainatulmardhiah A
doi: 10.1088/1742-6596/2933/1/012029pmid: N/A
Effective thermal management in microfluidic devices depends on precise temperature measurement, which is crucial for processes such as drug release and food packaging. This study investigates the use of Laser-Induced Fluorescence (LIF) as a non-intrusive method for temperature measurement in microchannels fabricated using micron-sized shims. Initially, the channel thickness was validated using the LIF method with Coumarin 153 (C153) dye, which revealed minor thickness measurement errors that had a negligible impact on temperature sensitivity trends. For temperature measurement within the microchannel, Rhodamine B (RhB), a highly sensitive and water-soluble fluorescent dye was employed. The findings show a temperature sensitivity ranging from -0.1%/°C to -0.4%/°C, which remains reliable despite the use of low laser power output with a wavelength relatively far from the maximum absorption of RhB. Future studies could enhance temperature sensitivity by considering the trade-off between laser power output and dye concentration, further improving this approach for various microfluidic applications.
Drying model of stingless bee pot-pollen using a swirling fluidised bed dryerHalim, Luqman Abdul; Basrawi, Firdaus; Yudin, Ahmmad Shukrie Md; Azman, Nurul Aini Mohd; Ramli, Ahmad Syazwan
doi: 10.1088/1742-6596/2933/1/012030pmid: N/A
Stingless Bees Pot-Pollen (SBPP), a mixture of pollen, nectar and bee enzyme made by stingless bees has high moisture content, making it susceptible to microbes and fungi growth. This can cause spoilage and become toxic to be. Thus, SBPP require suitable preservation method for storage. Current methods available such as sun drying, and oven drying can be unhygienic, inefficient, and hard to control the temperature which leads to nutrient loss. Hence, a swirling fluidized bed dryer (SFBD), a modified fluidised bed dryer with swirling airflow is proposed as it has rapid drying performance at relatively low temperature. Currently, drying models for various materials in a fluidised bed dryer has been previously studied. However, research on drying model of SBPP using a SFBD is still lacking. Therefore, the objective of this paper is to determine the most suitable drying model for drying of SBPP in a SFBD. A lab scale SFBD with a 67° swirling distributor is used to dry 25 g of SBPP at superficial air velocities of 4, 5, and 6 m/s. In this study, 7 drying models obtained from literatures are compared to determine the most suitable model for drying of SBPP in SFBD. It was found that, the Midilli-Kucuk model was the most suitable drying model compared to other drying models tested with highest average R2 (0.999307), lowest X2 (7.2 × 10-5), and lowest RMSE (2.16 × 10-2). The Midilli-Kucuk model closely predict the experimental values for all superficial air velocities tested. The experimental results also shown that increasing the superficial air velocities will lead to increase in drying rate. Thus, it can be concluded that Midilli-Kucuk model is suitable to describe the drying of stingless bee pot-pollen in SFBD.
prefacedoi: 10.1088/1742-6596/2933/1/011001pmid: N/A
It is with great pleasure that we present the proceedings of the 3rd International Postgraduate Conference on Mechanical Engineering 2024 (IPCME 2024), held on 3rd October 2024, at Universiti Malaysia Pahang Al Sultan Abdullah. This annual event has become a prominent platform for postgraduate researchers, academics, and industry professionals to engage in meaningful discussions, share their innovative findings, and explore future directions in the ever-evolving field of mechanical engineering.The theme of IPCME 2024, “Innovation of Sustainable Engineering: Shaping The Future,” underscores the pivotal role that mechanical engineering plays in addressing global challenges, including energy sustainability, climate change, and technological advancements. This theme has inspired a rich array of contributions from researchers across the globe, highlighting cutting-edge developments in areas such as thermal sciences, advanced manufacturing, materials engineering, fluid dynamics, and energy systems.The proceedings encompass a diverse collection of high-quality research papers that underwent rigorous peer review to ensure their relevance, originality, and scientific rigour. These papers reflect the innovative spirit and dedication of the postgraduate community, providing insights into fundamental and applied research that will shape the discipline’s future. We are particularly proud of the collaborative nature of this conference, which has facilitated knowledge exchange and fostered international networks among participants. The discussions and ideas generated during IPCME 2024 are a testament to the strength and vibrancy of the mechanical engineering research community. We extend our heartfelt gratitude to all the authors, reviewers, and session chairs for their invaluable contributions and to the organizing committee and sponsors for their unwavering support. Their efforts have been instrumental in ensuring the success of this conference.We hope that the papers presented in this volume will serve as a valuable resource for researchers, educators, and practitioners and that they will inspire further innovation and collaboration in the field of mechanical engineering. We look forward to seeing the impact of these contributions in advancing knowledge and addressing the pressing challenges of our time.List of Organizing Committee and Scientific Committee are available in this pdf.
Artificial intelligence-Based Detection of Unattended Child Presence in VehiclesJefri, Nur Atikah; Saruchi, Sarah ‘Atifah; Aziz, Radhiyah Abd; Nordin, Muhammad Aqil Hafizzan; Jawi, Zulhaidi Mohd
doi: 10.1088/1742-6596/2933/1/012016pmid: N/A
The issue of unattended children in vehicles is a critical concern due to the significant risks it poses, including fatalities and heat strokes, necessitating immediate attention. Therefore, this study proposes an unattended child presence detection system in vehicles via Convolutional Neural Networks. The methodology begins with the collection of data through images captured using a doll to simulate a child’s presence inside vehicles. These images are used for training the CNN models, which utilize state-of-the-art architectures such as ResNet and MobileNet. Through rigorous simulations, the CNN models achieved a commendable training accuracy exceeding 90%. This high accuracy underscores the effectiveness of the proposed approach in accurately detecting the presence of unattended children in various real-world scenarios. The findings underscore the efficacy of deep learning techniques in addressing critical safety concerns related to unattended children in vehicles. The high accuracy rate indicates that the proposed system can reliably detect a child’s presence, potentially preventing tragic incidents. The research concludes that integrating such deep learning-based detection systems in vehicles can significantly enhance child safety. Future research directions could explore enhancing the system’s robustness to diverse environmental conditions and vehicle types. Additionally, integrating real-time monitoring and alert systems could further enhance the system’s practical application and effectiveness in preventing incidents involving unattended children.
Extreme Learning Machine Optimization based on Hippopotamus Optimization Algorithm for Gear Fault DiagnosisAmirulaminnur, R.; Firdaus Isham, M.; Harith, M. K.; Saufi, M. S. R.; Saad, W. A. A.; Hasan, M. D. A.; Talib, M. H. A.
doi: 10.1088/1742-6596/2933/1/012019pmid: N/A
It is crucial to ensure the dependability, reliability, and sustainability of machines to optimize industrial productivity and efficiency. Any malfunction or breakdown of machine components or mechanical equipment may result in unexpected downtime and financial losses. This study presents a maintenance strategy for mechanical equipment, primarily focusing on a gear failure diagnosis approach using an extreme learning machine optimization based on the hippopotamus optimization algorithm (HO). The proposed method was evaluated using sets of gear vibration signals obtained from an online database, that included both healthy and malfunctioning data. The HO approach was employed to determine an optimal parameter for the ELM method, specifically the number of neurones, input weight, and bias range values. The findings indicate that the proposed approach enhances the classification efficacy of ELM by 12% compared to traditional ELM. The proposed strategy can be applied in any relevant industry to improve the sustainability and dependability of its plants.