Indoor air concentration from selective laser sintering 3d printer using Virgin Polyamide Nylon (PA12) Powder: a pilot study

Environmental emissions from additive manufacturing (AM) have attracted much attention recently. The capability in fabricating complex part make AM famous in developing prototype and product in various industries, especially in aerospace, medical, automotive, and manufacturing industries. However, the study on emission and exposure mainly focusses on the desktop type such as fused deposition modelling. This study investigates the emission and indoor concentration from powder bed fusion of selective laser sintering (SLS) technologies. Prior to the investigation, virgin PA12 has undergone characterization in terms of morphology, size and thermal analysis. Calibration block using virgin polyamide nylon (PA12) is selected to be printed in this study. Parameters such particulate matter size 2.5 μm (PM 2.5), total volatile organic compound (TVOC), carbon dioxide (CO2), formaldehyde, temperature and relative humidity (RH) are set to be monitored through real-time sampling of 8 hours based on Industry Code of Practice on Indoor Air Quality 2010 by Department Occupational Safety and Health (DOSH) Malaysia. Four phases of the printing process involve are background data, preprinting, during printing and post-printing. Based on the study it was found that PM 2.5 and CO2 exceed the acceptable limit recommended by DOSH Malaysia during the preparation of powder (preprinting) at 1218 ppm and 1070 μg/m3 respectively. Meanwhile TVOC concentration was influenced by the sintered powder temperature and recorded at 0.5 ppm. Temperature, relative humidity and formaldehyde were maintained throughout the SLS process. Mitigation strategies using mechanical ventilation and personal protective equipment (PPE) are recommended to be used to reduce the potential of occupational hazard to the operators

Development of Metal Matrix Composites and Related Forming Techniques by Direct Recycling of Light Metals: A Review

In this contribution, researchers have provided a summary of the agricultural and industrial waste recoveries to be deployed as the composite reinforced materials. It covers the work of previous researchers related to this area and addressed the key challenge to overcome for further development and advancement. The major contributions of this work were a comprehensive review on a wide variety of Sever Plastic Deformation (SPD) techniques implementation in development of the waste materials based reinforced metal matrix composite. The waste materials can be derived from either industrial or natural sources. Also, it discusses the range of Metal Matrix Composites (MMCs) applications in engineering and related manufacturing techniques with further emphasized on the process parameters which directly determine the material properties. Some useful suggestions were proposed to the industrialists, academicians and scientists to further improve the performance aspect of Metal Matrix Composites (MMCs) for commercialization reason. Furthermore, industrial and natural waste enhancement materials have been strongly proposed because of their higher reinforced content particulates such as alumina (Al2O3) and silica (SiO2). Also, the mechanical and physical properties are directly influenced by the size, shape and weight-volume friction of the composites as same as the potential reactions between matrixes/reinforced materials interfac

Techno-Economic Analysis of Off-Grid PV Solar System for Residential Building Load: A Case Study in Baidoa, Somalia

The demand for energy is increasing day by day globally. To overcome the problem of energy scarcity, solar energy promises to be one of the best solutions without a significant increase in the carbon footprint of the atmosphere. Currently, most Somalis do not have access to a regular source of power. The country does not have a national grid, relying on outdated, costly and inefficient diesel generators. The energy consumption in Somalia is dependent on firewood and charcoal, dependencies that rely on deforestation and desertification, which negatively influence the agricultural sector and also the environment. In this work, the potential of solar power in Somalia is assessed while estimating the cost of solar panels per household. The aim of this study is to assess the cost, ecological and economic efficiency of the off-grid PV home system in residential buildings in Baidoa, Somalia. A stand-alone solar home system of 1.98kW PV capacity with battery backup is designed by using HOMER software. The daily primary load considered is 7.530 kWh, with a peak of the nominal power of 1.60 kW. The results show that renewable energy sources can replace conventional energy sources and that they would be a viable solution for generating electrical energy in residential houses in Baidoa with a reasonable investment. It was also found that the amount of power produced by solar panels is 7,400kWh/year. With an initial investment of $5580, the annualized life cycle cost of the system is$0.483, the payback period of initial investment is 2 years and 8 months period, and the net present cost (NPC) of the project is $18,684

Numerical Study of Three-Dimensional Models of Single- and Two-Phase Nanofluid Flow through Corrugated Channels

This study delves into computational fluid dynamics (CFDs) predictions for SiO2–water nanofluids, meticulously examining both single-phase and two-phase models. Employing the finite volume approach, we tackled the three-dimensional partial differential equations governing the turbulent mixed convection flow in a horizontally corrugated channel with uniform heat flux. The study encompasses two nanoparticle volume concentrations and five Reynolds numbers (10,000, 15,000, 20,000, 25,000, and 30,000) to unravel these intricate dynamics. Despite previous research on the mixed convection of nanofluids using both single-phase and two-phase models, our work stands out as the inaugural systematic comparison of their predictions for turbulent mixed convection flow through this corrugated channel, considering the influences of temperature-dependent properties and hydrodynamic characteristics. The results reveal distinct variations in thermal fields between the two-phase and single-phase models, with negligible differences in hydrodynamic fields. Notably, the forecasts generated by three two-phase models—Volume of Fluid (VOF), Eulerian Mixture Model (EMM), and Eulerian Eulerian Model (EEM)—demonstrate remarkable similarity in the average Nusselt number, which are 24% higher than the single-phase model (SPM). For low nanoparticle volume fractions, the average Nusselt number predicted by the two-phase models closely aligns with that of the single-phase model. However, as the volume fraction increases, differences emerge, especially at higher Reynolds numbers. In other words, as the volume fraction of the nanoparticles increases, the nanofluid flow becomes a multi-phase problem, as depicted by the findings of this study.</p

Modeling and Speed Control for Sensorless DC Motor BLDC Based on Real Time Experiement

This paper presents a modeling of the Brushless DC motor based on the system identification method. The input and output data were collected and simulated based on the real-time experiment. Taking a continues time form for the system model, a transfer function was selected in this work. The potentiometer has been used to send &nbsp;Pulse Width Modulation (PWM) signals as input signal to the Brushless DC motor to determine the open-loop model of brushless DC motor (BLDC). LM2907 Tachometer attached with Brushless DC motor driver to measure the output speed. The input signal and measured output data were interfaced to plant by C code generation Matlab/Simulink through Arduino Mega controller. System identification toolbox was used for collecting data to obtain the estimates model. The best fit found for the system was 90.2%. The PID controller was developed to control the desired speed based on the given speed to demonstrate the feasibility of the given method. &nbsp;&nbsp

Effect of Direct Recycling Hot Press Forging Parameters on Mechanical Properties and Surface Integrity of AA7075 Aluminum Alloys

The current practice in aluminum recycling plants is to change the waste into molten metal through the conventional recycling (CR) manufacturing process. However, the CR technique is so energy-intensive that it also poses an indirect threat to the environment. This paper presents a study on meltless direct recycling hot press forging (DR-HPF) as an alternative sustainable approach that has fewer steps with low energy consumption, as well as preventing the generation of new waste. A laboratory experiment was conducted to study the mechanical properties and surface integrity of AA7075 aluminum alloy by employing a hot press forging (HPF) process under different temperatures (380, 430, and 480 &deg;C) and holding times (0, 60, and 120 min). It was found that as the parameter increased, there was a positive increase in ultimate tensile strength (UTS), elongation to failure (ETF), density, and microhardness. The recycled chips exhibit the best mechanical properties at the highest parameters (480 &deg;C and 120 min), whereas the UTS = 245.62 MPa and ETF = 6.91%, while surface integrity shows that the calculated microhardness and density are 69.02 HV and 2.795 g/cm3, respectively. The UTS result shows that the highest parameters of 480 &deg;C and 120 min are comparable with the Aerospace Specification Metals (ASM) Aluminum AA7075-O standard. This study is a guide for machinists and the manufacturing industry to increase industry sustainability, to preserve the earth for future generations

Effect of the Heat treatment on Mechanical and Physical Properties of Direct Recycled Aluminium Alloy (AA6061)

Products by solid-state recycling of aluminum chips in hot extrusion process were controlled by temperature related parameters using preheating temperature 450 °C, 500 °C, and 550°C for 1 hr, 2 hr, and 3 hr preheating time. By using Design of Experiments (DOE), the results found that the preheating temperature is more important to be controlled rather than the preheating time in analysis both mechanical and physical properties. The results also found that increasing of temperature led to the high tensile strength and low microhardness. The profile extruded at 550 °C with 3 hr duration had gained the optimum case to get the maximum tensile strength and the profile extruded at 450 °C with 1 hr had result the optimum case to gain the maximum microhardness. For the optimum cases, heat treatment was carried out using quenching temperature at 530 ºC for 2 hr and aging process at 175 ºC for 4 hr. The tensile strength and microhardness of extrudes specimens were improved significantly by heat treatment

Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging

Direct recycling of aluminum waste is crucial in sustainable manufacturing to mitigate environmental impact and conserve resources. This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. The experimental runs were designed using Full factorial and RSM via Minitab 21 software. RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. The chips of different morphology were cold compressed into billet form and then hot forged. The effect of varying forging temperature (Tp, 450–550°C), holding time (HT, 60–120 minutes), and chip surface area to volume ratio (A&lt;jats:sub/&gt;S:V, 15.4–52.6 mm&lt;jats:sup/&gt;2/mm&lt;jats:sup/&gt;3) on ultimate tensile strength (UTS) was examined. Maximum UTS (237.4 MPa) was achieved at 550°C, 120 minutes and 15.4 mm&lt;jats:sup/&gt;2/mm&lt;jats:sup/&gt;3 of chip’s A&lt;jats:sub/&gt;S: V. The Tp had the largest contributing effect ratio on the UTS, followed by HT and A&lt;jats:sub/&gt;S:V according to ANOVA analysis. The proposed optimization process suggested 550°C, 60 minutes, and 15.4 mm&lt;jats:sup/&gt;2 as the optimal condition yielding the maximum UTS. The developed models’ evaluation results showed that ANN (with MSE = 1.48%) outperformed RSM model. Overall, the study promotes sustainable production by demonstrating the potential of integrating RSM and ML to optimize complex manufacturing processes and improve product quality

Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging

Direct recycling of aluminum waste is crucial in sustainable manufacturing to mitigate environmental impact and conserve resources. This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. The experimental runs were designed using Full factorial and RSM via Minitab 21 software. RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. The chips of different morphology were cold compressed into billet form and then hot forged. The effect of varying forging temperature (Tp, 450–550°C), holding time (HT, 60–120 minutes), and chip surface area to volume ratio (A&lt;jats:sub/&gt;S:V, 15.4–52.6 mm&lt;jats:sup/&gt;2/mm&lt;jats:sup/&gt;3) on ultimate tensile strength (UTS) was examined. Maximum UTS (237.4 MPa) was achieved at 550°C, 120 minutes and 15.4 mm&lt;jats:sup/&gt;2/mm&lt;jats:sup/&gt;3 of chip’s A&lt;jats:sub/&gt;S: V. The Tp had the largest contributing effect ratio on the UTS, followed by HT and A&lt;jats:sub/&gt;S:V according to ANOVA analysis. The proposed optimization process suggested 550°C, 60 minutes, and 15.4 mm&lt;jats:sup/&gt;2 as the optimal condition yielding the maximum UTS. The developed models’ evaluation results showed that ANN (with MSE = 1.48%) outperformed RSM model. Overall, the study promotes sustainable production by demonstrating the potential of integrating RSM and ML to optimize complex manufacturing processes and improve product quality

Recycling aluminium AA6061 chips with reinforced boron carbide (B&lt;sub&gt;4&lt;/sub&gt;C) and zirconia (ZrO&lt;sub&gt;2&lt;/sub&gt;) particles via hot extrusion

Compared to the recycling process by remelting, hot extrusion significantly reduces the energy consumption and CO2 emission and ensures good mechanical and microstructural properties. This study investigates the effects of reinforcing aluminium AA6061 chips with mixed boron carbide (B4C) and zirconia (ZrO2) particles by employing a design of experiment (DOE) under 550 °C processing temperature and three hours preheating time. The findings showed that compressive strength (CS) and hardness increased with up to 5% added particles, and beyond 5%, the yielded values decreased because of materials agglomeration. However, the decreasing density was dependent on the addition of ZrO2 particles. The distribution of particles with different volume fractions of mixed particles was investigated by employing SEM, AFM, and EDS tests. Thus, the process can produce a net shape structure that utilises material-bonding consolidation to provide sufficient support to reuse the recovered materials in engineering applications, such as in the automotive industry