Teknomekanik

Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines

2024-05-25T05:35:00+00:00

In each specific problem of finding the best solution among many available options, where each option has multiple criteria, multi criteria decision making methods are considered equally effective when they converge to the same optimal solution. Proximity Indexed Value, Preference Selection Index, Faire Un Choix Adéquat (in French), and Collaborative Unbiased Rank List Integration are four Multi Criteria Decision Making methods with very different characteristics. All these four methods have been used a lot in recent times. The effectiveness of these four methods have been confirmed to be comparable to other multi criteria decision making methods in many applications. However, the comparison of these four methods with each other has never been performed in any studies. This article is performed to fill that gap. These four methods have been used to find to the best option among five types of plastic injection molding machine. Ten criteria have been chosen to describe each alternative. Two different methods that have been used to calculate the weights for the criteria are the MEAN weight method and the CRiteria Importance Through Intercriteria Correlation weight method. Different scenarios have been created to compare the effectiveness of these four methods. The results have shown that the four multi criteria decision making methods mentioned above are equally effective in the selection of plastic injection molding machines. Among the five types of plastic injection molding machines, namely JSW J350EII-SPA ANBE-002-02, Meiki M-200B-SJ, Meiki M-350C-DF-SJ, JSW J350E II, and JSW J550E-C5, the JSW J550E-C5 is the best type.

Enhancing mechanical properties of polylactic acid through the incorporation of cellulose nanocrystals for engineering plastic applications

2024-06-13T10:55:39+00:00

This study investigates the potential of enhancing the mechanical properties of polylactic acid (PLA) using cellulose nanocrystals (CNC). Recognized for their high specific strength and stiffness, CNCs are considered to improve the performance of PLA in engineering plastic applications. The synthesis involves a twin-screw extrusion process, which facilitates the uniform dispersion of CNC within the PLA matrix. The mechanical properties, including tensile strength and elongation at break, are comprehensively analyzed, highlighting the effects of CNC concentrations on the performance of PLA composites. Notably, the addition of 1 wt% CNC resulted in a 20% increase in strain at break compared to pure PLA, demonstrating enhanced ductility. Additionally, the thermal resistance of the composite increased by 0.3% with the inclusion of 5 wt% CNC. This study highlights the positive effect of CNC addition on the mechanical properties of PLA composites, making them more suitable for specialized engineering uses.

Exploring QSAR of FLAP inhibitors using kernel partial least squares modeling: Insights from molecular binary fingerprints

2024-05-26T19:55:02+00:00

FLAP (5-Lipoxygenase Activating Protein) inhibitors, offering targeted intervention in leukotriene biosynthesis and holding therapeutic promise for inflammatory diseases like asthma, are hindered by current inhibitors' off-target effects, limited efficacy, safety concerns, potential drug interactions, and accessibility issues. Given these challenges, computational methods, particularly Quantitative Structure Activity Relationships (QSAR) modeling, are vital for developing novel FLAP inhibitors. This study specifically investigates the QSAR of FLAP inhibitors using Kernel Partial Least Squares (KPLS) modeling. Leveraging a dataset of FLAP inhibitors, we employ KPLS within the Schrödinger Canvas environment to correlate molecular descriptors with biological activity. Out of the eight models developed, the "atom pairs" fingerprint yielded a statistically significant 2D QSAR model with outstanding regression coefficient values (R²=0.9624). The model demonstrated high predictive ability for external test set data (R²pred = 0.7105), underscoring its robustness and reliability in accurately predicting biological activity based on molecular structure. Additionally, we tried to visualize the relative contributions of individual atoms within FLAP inhibitors, providing insights into their favorable and unfavorable characteristics. Through the analysis of atomic contributions, we identify key structural motifs crucial for predicting FLAP inhibitor activity. Our findings not only advance our understanding of FLAP inhibitor SAR but also demonstrate the utility of KPLS modeling and atomic contribution analysis in drug discovery efforts. Furthermore, this study contributes to the development of anti-inflammatory therapeutics by elucidating the structural determinants of FLAP inhibitor activity, with potential applications in the treatment of inflammatory disorders.

A comparative study utilizing hybridized ant colony optimization algorithms for solving dynamic capacity of vehicle routing problems in waste collection system

2024-06-04T04:40:08+00:00

The waste collection stage generated 70% of the cost of the total Municipal Solid Waste (MSW) management system. Therefore, choosing the most affordable waste collection method is essential to accurately estimate the waste collection and transportation costs, thus selecting the required vehicle capacity. The study aims to design a waste collection system for calculating waste collection and transportation costs using a systematic framework that includes Hybridized Ant Colony Optimization (HACO) with Sequential Variable Neighborhood Search Change Step (SVNSCS) and Sequential Variable Neighborhood Decent (SVND). The framework addresses a Dynamic Capacity of Vehicle Routing Problem (DCVRP) and improves ACO's ability in exploration and exploitation stages. The objectives are to minimize the cost of travel distance and arrival time formulated in a mathematical model and to design a new strategy for eliminating the sub-tour problem in the following steps: (1) minimize the number of routes assigned, (2) increase the amount of waste in the vehicle capacity, and (3) define the best amount of waste allowed in vehicle capacity. The waste collection system compared HACO with ACO across four benchmark datasets. The results indicate HACO outperformance ACO at 100%, 91%, 100%, and 87%, respectively. The visualization results demonstrated that HACO has fast convergence and can be considered another essential tool for route optimization in the waste collection system.

Experimental study of gas adsorption using high-performance activated carbon: Propane adsorption isotherm

2024-06-10T07:24:13+00:00

Activated carbon is widely used for its diverse adsorptive abilities, with a vast range of current and emerging uses. This study developed a data set for high-performing activated carbon, its adsorption abilities with differing adsorbents, and an understanding of what deviations are present compared to the widely used adsorption models. This study included the construction of Tóth isotherms in varying conditions. Building a strong isotherm correlation is desired, with an understanding of the relationship between the pores of the activated carbon sample, operating parameters, and the adsorbent. The present data could complement efforts in designing adsorbed natural gas storage systems. Experimental data was collected using a Constant Volume Variable Pressure (CVVP) apparatus, consisting of a temperature-regulated vessel containing the activated carbon sample dosed with varying adsorbents through a controlled dosing vessel. Analysis of the derived data gave a well-fitted Tóth adsorption isotherm, giving the maximum specific adsorption capacity of the activated carbon to be 2.28 g of propane per gram of activated carbon with a standard error of regression

Comparative analysis of the least squares method and double moving average technique for forecasting product inventory

2024-06-29T16:29:44+00:00

The cosmetics industry necessitates efficient inventory management to balance customer demand with stock control. This case study explores how Liza Cosmetics Shop optimized inventory for Lip Cream Implora 01, a popular product, using data-driven forecasting techniques. Traditional trend-based methods often resulted in inaccurate forecasts. This study proposed implementing the SDLC Waterfall Model to apply two forecasting techniques: Least Squares and Double Moving Average. Historical sales data (April 2021 - June 2022) was analyzed to identify demand patterns, seasonality, and trends. The Least Squares method was chosen for its suitability in capturing stable, linear relationships between sales and time, while the Double Moving Average method catered to data exhibiting both long-term trends and short-term fluctuations. Rigorous testing using white-box and black-box methods ensured the accurate functionality and system behavior of the implemented models. The Mean Absolute Percentage Error (MAPE) determined the method best suited for predicting July 2022 demand. This case study contributes insights into data-driven inventory management in cosmetics, highlighting benefits such as optimized stock levels, reduced costs, and enhanced customer satisfaction through improved demand fulfillment. This studys’ limitations including unforeseen marketing campaigns and economic fluctuations impacting forecasts were acknowledged. Despite these challenges, the study emphasizes the potential of data-driven techniques to optimize inventory management and meet customer demands effectively.

Application of ground penetrating radar for evaluating foundation structure condition after earthquake

2024-06-30T08:45:05+00:00

At the time of seismic activity, the failure of the foundation structure will lead to building damage. When the West Pasaman 2022 earthquake occurred, PT. XYZ is constructing a feed mill tower. Since strong earthquake shocks were felt at the project location, foundation structure evaluation is needed to ensure the safety of the building. Ground Penetrating Radar (GPR) is a tool that is widely used to detect subsurface conditions. This study used GPR as a non-destructive testing technique to evaluate the condition of the foundation structure. The building evaluated is a high-rise steel building, using spun pile foundation. GPR test was carried out in specified lanes, with measurement tracks set at 10 lanes. Any cracks or fractures on the foundation will be indicated by the interruption of waves at the point of the crack or fracture. The GPR test results from readings of electromagnetic wave propagation showed that waves can reach the end of each foundation tested, ranging from 17.10 m to 17.82 m deep. It means that there are no cracks or fractures found on the slab, pile cap, or foundation. Analysis results showed that all slabs and pile caps thicknesses and the detected foundation piles depths are in accordance with the foundation design, which means that the foundations are still in good condition.