ORCID Profile
0000-0002-5390-8202
Current Organisation
Universiti Putra Malaysia
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Publisher: MDPI AG
Date: 07-08-2021
DOI: 10.3390/APP11167272
Abstract: Fused deposition modeling (FDM) is a capable technology based on a wide range of parameters. The goal of this study is to make a comparison between infill pattern and infill density generated by computer-aided design (CAD) and FDM. Grid, triangle, zigzag, and concentric patterns with various densities following the same structure of the FDM machine were designed by CAD software (CATIA V5®). Polylactic acid (PLA) material was assigned for both procedures. Surface roughness (SR) and tensile strength analysis were conducted to examine their effects on dog-bone s les. Also, a finite element analysis (FEA) was done on CAD specimens to find out the differences between printing and simulation processes. Results illustrated that CAD specimens had a better surface texture compared to the FDM machine while tensile tests showed patterns generated by FDM were stronger in terms of strength and stiffness. In this study, s les with concentric patterns had the lowest average SR (Ra) while zigzag was the worst with the value of 6.27 µm. Also, the highest strength was obtained for concentric and grid s les in both CAD and FDM procedures. These techniques can be useful in producing highly complex sandwich structures, bone scaffolds, and various combined patterns to achieve an optimal condition.
Publisher: MDPI AG
Date: 22-02-2023
DOI: 10.3390/MATH11051085
Abstract: A fundamental issue in manufacturing systems is moving a local manufacturer into a supply chain network including wholesalers and retailers. In this research, a 3-phase framework is proposed to determine the food consumption pattern in food supply chains. In the first stage of this research, the consumer, availability and society factors for product classification according to the features of populations in Malaysia are identified (phase 1). Then, using statistical analysis, the effective factors are recognised (phase 2). In the third phase, the product clusters are recognised using a hybrid PCA and agglomerative clustering method. For this purpose, different clusters for the training step are used. The outcomes indicated that Age (0.94), City (0.79), Health Benefit Awareness (0.76) and Education (0.75) are the most effective factors in product consumption patterns, respectively. Moreover, the efficiency of the outcomes is evaluated using the Silhouette Coefficient, indicating that the proposed algorithm could provide solutions with a 68% score. Moreover, using Calinski-Harabasz Index, it was found that the algorithm provided more logic scores while the number of product patterns was 3 for the studied region (707.54).
Publisher: MDPI AG
Date: 14-12-2021
DOI: 10.3390/MATH9243225
Abstract: In this paper, a new hybrid AHP and Dempster—Shafer theory of evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, a three-phase framework is proposed. In the first phase, quantitative research was conducted to identify the risk factors that can influence a project. Then, a hybrid PCA-agglomerative unsupervised machine learning algorithm is proposed to classify the projects in terms of Properties, Operational and Technological, Financial, and Strategic risk factors. In the third step, a hybrid AHP and Dempster—Shafer theory of evidence is presented to select the best alternative with the lowest level of overall risks. As a result, four groups of risk factors, including Properties, Operational and Technological, Financial, and Strategic risk factors, are considered. Afterward, using an L2^4 Taguchi method, several experiments with various dimensions have been designed which are then solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index, Reduced Risk Indicator, and Solving Time. The findings indicated that, compared to classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.
Publisher: MDPI AG
Date: 03-12-2021
DOI: 10.3390/MATH9233114
Abstract: Transferring a local manufacturing company to a national-wide supply chain network with wholesalers and retailers is a significant problem in manufacturing systems. In this research, a hybrid PCA-K-means is used to transfer a local chocolate manufacturing firm near Kuala Lumpur into a national-wide supply chain. For this purpose, the appropriate locations of the wholesaler’s center points were found according to the geographical and population features of the markets in Malaysia. To this end, four wholesalers on the left island of Malaysia are recognized, which were located in the north area, right area, middle area, and south area. Similarly, two wholesalers were identified on the right island, which were in Sarawak and WP Labuan. In order to evaluate the performance of the proposed method, its outcomes are compared with other unsupervised-learning methods such as the WARD and CLINK methods. The outcomes indicated that K-means could successfully determine the best locations for the wholesalers in the supply chain network with a higher score (0.812).
Publisher: MDPI AG
Date: 16-11-2021
DOI: 10.3390/MATH9222919
Abstract: In manufacturing firms, there are many factors that can affect product completion time in production lines. However, in a real production environment, such factors are uncertain and increase the adverse effects on product completion time. This research focuses on the role of internal factors in small- and medium-scale supply chains in developing countries, enhancing product completion time during the manufacturing process in fuzzy conditions. In the first step of this research, a list of factors was found clustered into six main groups: technology, human resources, machinery, material, facility design, and social factors. In the next step, fuzzy weights of each group factor were determined by a fuzzy inference system to reflect the uncertainty of the factors in utilizing product completion time. Then, a hybrid fuzzy–TOPSIS-based heuristic is proposed to generate and select the best production alternative. The outcomes showed that the proposed method could generate and select the alternative with a 10.13% lower product completion time. The findings also indicated that using the proposed fuzzy method will cause less minimum variance compared to the crisp mode.
Publisher: MDPI AG
Date: 25-04-2022
DOI: 10.3390/APP12094321
Abstract: The adoption of biorenewable alternative fuel resources from biofuels (ethanol or biodiesel) has produced promising solutions to reduce some toxic greenhouse gas (GHG) emissions from gas turbine engines (GTEs). Despite the reduced hydrocarbon associated with adopting alternative bio-renewable fuel resources, GTE operations still emit toxic gases due to inefficient engine performance. In this study, we assess the impact of the integration of plasma combustion technology on a micro-GTE using biodiesel fuel from animal fat with the aim of addressing performance, fuel consumption, and GHG emission reduction limitations. Laboratory design, fabrication, assembly, testing, and results evaluation were conducted at Kuwait’s Public Authority for Applied Education and Training. The result indicates the lowest toxic emissions of sulfur, nitrogen oxide (NO), NO2, and CO were from the biodiesel blended fuels. The improved thermal efficiency of GTE biodiesel due to the volume of hydrogen plasma injected improves the engine’s overall combustion efficiency. Hence, this increases the compressor inlet and outlet firing temperature by 13.3 °C and 6.1 °C, respectively. The Plasma technology produced a thrust increment of 0.2 kgf for the highest loading condition, which significantly impacted horsepower and GTE engine efficiency and reduced the cost of fuel consumption.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for mohd khairol anuar mohd ariffin.