Journal of Manufacturing Engineering is an open access international journal, peer-reviewed, quarterly-published, launched in 2005. The journal is published by Society for Manufacturing Engineers in both the paper form as well as the electronic format.
Design and Implementation of an LPG Leakage Detection and Automatic Power Shutdown System
Liquefied Petroleum Gas (LPG) is widely used as a consumer gas to satisfy many consumer needs due to its affordability and availability. However, even a 10% (v/v) solution may ignite a spark. Finding LPG leaks is challenging because they are mostly colourless and odourless at low concentrations. To address this, a low-cost, reliable electronic system has been designed to detect LPG leaks and prevent ignition sources by shutting down power to the affected area using an MQ-2 gas sensor. Analog output from the MQ-2 sensor was received and processed by the ATmega13A-PU microcontroller, which compared the reading against a predefined threshold. Once the sensor threshold is reached, the microcontroller first activates a buzzer and a high-brightness red LED to alert occupants. After a 5-second delay, a 433 MHz RF transmitter sends a wireless signal to the receiver unit, which triggers a shunt-trip breaker via a solid-state relay. This action disconnects the main electrical supply, thereby eliminating the risk of sparks. The system was calibrated and tested under varying gas concentrations and distances, confirming accurate sensitivity and reliable operation. Once the power is shut down, occupants can safely ventilate the area by opening windows and doors. The system continues to operate in a loop until gas levels return to safe conditions. This approach ensures household safety, minimizes fire hazards, and provides a cost-effective solution suitable for residential and small commercial settings. Future enhancements include integrating Internet of Things (IoT) technology for remote notifications and automated ventilation controls, further improving safety and convenience.
Copyright (c) 2026 Journal of Manufacturing Engineering
Influence of Lattice Infill Geometry on the Mechanical Response of Fused Filament Fabricated Zirconia Functionally Graded Structures
Fused filament fabrication (FFF) enables the production of complex ceramic structures with tailored internal architecture. However, the influence of lattice infill geometry on the mechanical behaviour of zirconia-based functionally graded structures remains poorly characterised. This study systematically investigated the mechanical response and dimensional stability of zirconia functionally graded lattices incorporating grid, gyroid, and triangular infill patterns. Specimens were fabricated via extrusion-based FFF, followed by chemical debinding and high-temperature sintering. The mechanical performance was evaluated under compression, tension, and flexure in accordance with ASTM E9, E8, and C1161, respectively. Complementary porosity and shrinkage analyses were conducted to establish relationships between geometry and properties. Distinct topology-dependent mechanical behaviours were observed. The grid-based structures exhibited superior load-bearing capacity, sustaining maximum compressive loads of 26.0 ± 1.2 kN, followed by triangular (24.1 ± 1.5 kN) and gyroid (23.4 ± 1.8 kN) configurations. Under flexural loading, the grid lattices supported 20.0 ± 1.1 kN, compared to 15.8 ± 1.4 kN (triangular) and 11.0 ± 1.6 kN (gyroid). Tensile testing revealed similar trends: grid (15.0 ± 0.9 kN), triangle (11.5 ± 1.3 kN), and gyroid (9.7 ± 1.5 kN). Gyroid architectures demonstrated improved deformation tolerance despite lower peak loads. Shrinkage analysis confirmed geometry-dependent dimensional variations (19–22% linear shrinkage), consistent with the densification-driven behaviour typical of zirconia systems. These findings establish critical structure–property relationships for FFF-fabricated zirconia lattices, providing design guidance for lightweight ceramic components in mechanically demanding applications.
Fused filament fabrication, zirconia, functionally graded structures, lattice infill geometry, mechanical behaviour.
Low-Cost ESP32-Based Smart 18650 Li-Ion Battery Charger with IoT Monitoring
Lithium-ion batteries, particularly 18650 cells, are widely used in portable electronics, laboratory equipment, and energy storage applications. Safe charging, effective monitoring, and accurate capacity estimation are critical to ensure battery performance and longevity. This paper presents the design and implementation of a low-cost smart battery charger for 18650 lithium-ion cells using an ESP32 microcontroller. The proposed system supports controlled charging and discharging, real-time measurement of voltage, current, temperature, humidity, and battery capacity, and local visualization using an OLED display. In addition, the charger integrates Internet of Things (IoT) functionality through the MQTT protocol, enabling real-time data transmission and cloud-based logging using Google Sheets. Experimental results demonstrate stable charging performance, reliable data acquisition, and effective battery capacity estimation. The developed system is suitable for educational laboratories, battery testing, and low-cost IoT-based energy monitoring applications.
DESIGN AND CFD-BASED PERFORMANCE EVALUATION OF A SMALL-SCALE HORIZONTAL AXIS WIND TURBINE FOR COASTAL LOW-WIND REGIONS
The growing environmental and economic impacts of fossil fuels have intensified demand for renewable energy. Wind energy is one of the most popular renewable energy sources, particularly in coastal regions. This study presents the design and numerical analysis of a small-scale horizontal-axis wind turbine (HAWT) intended for low-wind-speed applications. The turbine is designed for the coastal city of Hambanthota, Sri Lanka, where the average wind speed is 5 m/s. A wind turbine blade with a 5 m span was designed using NACA airfoil data and modelled in SOLIDWORKS. Computational Fluid Dynamics (CFD) analysis was performed using SOLIDWORKS flow simulation. It is used to evaluate the turbine's aerodynamic performance. Key performance parameters, including torque and efficiency. To evaluate the blade's stress, strain, and displacement characteristics, the aerodynamic loads obtained from the CFD analysis were subsequently applied in a structural simulation. The results indicate effective wind energy extraction and acceptable structural performance under the selected operating conditions. This study demonstrates the feasibility of using CFD-based simulation as a preliminary design and performance evaluation tool for small-scale wind turbines suitable for coastal and low-wind-speed regions.
HAWT, Computational Fluid Dynamics, Wind turbine
A Critical review on Artificial Intelligence in EDM: Advancements in Process Optimization, Monitoring, and Predictive Control
Electrical Discharge Machining (EDM) is a widely used non-traditional machining process that removes material from a workpiece by a series of repetitive discharges. EDM is widely used for machining hard, brittle, and heat-resistant materials, often achieving high precision. The EDM process is influenced by various factors, including discharge energy, pulse duration, and electrode wear. These factors can be challenging to optimize due to their interdependencies and the highly nonlinear behaviour of the process. Traditionally, the optimization process has been conducted through trial and error or operator expertise, both of which are time-consuming and costly. Artificial Intelligence (AI), including machine learning (ML), neural networks (NN), fuzzy logic, and real-time process monitoring, offers powerful tools to automate and optimize these processes. This paper explores how AI is applied in EDM for process optimization, quality enhancement, predictive modeling, and real-time adjustments, with a focus on practical applications, methodologies, and challenges.
Multi-Criteria Decision-Making for Forging 38MnSiVS5 Steel Using TOPSIS Approach
This study investigates the optimisation of forging parameters for 38MnSiVS5 micro-alloyed steel to optimise mechanical properties, specifically impact strength and hardness. Hot forging experiments were conducted using open-die forging to evaluate the effects of forging temperature, percentage deformation, and cooling rates on these properties. A full factorial design of experiments was employed, and the resulting samples were assessed for impact strength, hardness and microstructural changes. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis was applied to identify the optimal forging conditions. The results indicated that forging at 900°C with 60% deformation under normal air cooling provided the best balance between impact strength and hardness. TOPSIS effectively identified this condition as optimal, highlighting its utility in decision-making for material processing.
Copyright (c) 2025 Journal of Manufacturing Engineering
Simulation-Driven Design Optimisation for Defect-Free Gray Cast Iron Brake Discs: An Industrial Case Study
Gray cast iron brake discs are widely used in automotive applications; however, micro-shrinkage defects that occur during casting can significantly impact their performance. This study addresses this critical issue by combining simulation and experimental validation to optimise the casting design. Using MAGMASOFT®️ simulation software, the formation of shrinkage porosities in an industrial brake disc was analysed, revealing that thermal concentration at L-junctions between the disc and flange was the primary cause. Three modified designs were proposed, incorporating fillet radii, increased flange inner diameters, and reduced chamfers to redistribute heat and minimise defects. Experimental validation confirmed that Design 3 (9 mm chamfer reduction + 130 mm flange inner diameter) successfully eliminated subsurface porosity without additional feeders or process complexity. This work demonstrates that minor geometric adjustments—rather than costly feeder additions—can effectively mitigate shrinkage defects in sand-cast brake discs, offering a practical solution for foundries.
Design and manufacture of tray feeding module for automatic kitchen
The demand for scalable and hygienic food service automation in industrial settings such as factories, canteens, and institutional kitchens has grown rapidly in response to rising labor costs, service inconsistencies, and the need for operational efficiency. While robotic and AI-driven systems have advanced significantly in commercial food services, the adaptation of such technologies in high-throughput, industrial environments remains limited. This research addresses that gap by presenting the design and development of a modular tray feeding module specifically tailored for use in automatic kitchen systems. The proposed system automates the transfer, alignment, and dumping of meal trays using a combination of conveyor mechanisms, pneumatic actuators, and programmable controls. The study covers the mechanical design, control integration, and functional validation of the tray feeding module under simulated industrial kitchen conditions. Key innovations include a dual-position conveyor interface, a pneumatically actuated tray engagement mechanism, and a rotary dumping system capable of sequential ingredient delivery. Structural and performance analyses, including finite element modeling for stress and displacement, demonstrate high reliability and safety under operational loads. Experimental testing confirms the system’s ability to operate continuously, accurately align trays, and manage sequential dumping with minimal intervention. This work contributes to the advancement of smart kitchen infrastructure by offering a robust, hygienic, and low-cost automation solution adaptable to diverse large-scale food service applications.
Low-Cost ESP32 Robotic Magnetometer for High-Resolution Indoor Magnetic Field Mapping
Indoor magnetic fields are strongly distorted by building materials and electrical infrastructure, complicating magnetometer-based applications such as localization and anomaly detection. We developed a low-cost, portable, ESP32-based three-axis magnetometer integrated on a small mobile robot to map indoor magnetic fields on a 10 cm × 10 cm grid. The robot followed predefined paths using infrared sensing, streamed measurements via Wi-Fi to a computer, and logged data to a Google Sheet for analysis. Spatial maps and contours were generated using Surfer to visualize field structure and variability. Field surveys were conducted in the Physics Department at the University of Colombo and compared against an outdoor baseline. Representative indoor corridor components reached X = 137.62 µT, Y = 261.98 µT, Z = 1.23 µT (resultant ≈ 296 µT), whereas outdoor measurements were X = 32.82 µT, Y = 23.09 µT, Z = 20.82 µT (resultant ≈ 45.2 µT). Contour maps reveal substantially greater spatial variation indoors than outdoors, with a resultant magnitude approximately 6.5 times higher at representative locations, indicating pronounced disturbances likely associated with structural metals and active wiring. These results demonstrate that a simple, reproducible robotic platform can produce high-resolution indoor magnetic cartography using commodity hardware, enabling rapid site assessment and supporting applications in magnetic fingerprinting, interference diagnosis, and infrastructure-free navigation.
Numerical Impact of Tool Geometry on Hip Prosthesis Forming
This paper examines the effect of incremental forming parameters on the process of a 304L stainless-steel biomedical part, which is inserted between the prosthesis and the cup to minimize wear and extend the lifespan of the prosthesis. The methodology involves several steps. First, the tool trajectory is determined. Then, modeling and numerical simulation are performed to select the optimal configuration, evaluate the final product geometry and forming tool force in relation to the forming angle, define the forming tool dimensions, and determine the axial step. Finally, the evaluation of the spring-back is conducted, along with an analysis of the parameters that influence the thinning of the component. The results highlight the relationship between punch diameter and axial displacement on the geometric quality of the deformed component. Furthermore, the force exerted varies as a function of axial steps, forming tool geometry, and the dimensions of the deformed component. Additionally, the number of contact points and the forming angle have an impact on the thinning.
Impact of Metal-Work Function and temperature on the performance of Schottky Barrier diode devices
N-Aluminum Gallium Arsenide Schottky Diodes are studied by I-V characteristics. Current-voltage characteristics were analyzed for some metal work Functions at a 300K temperature. A significant dependence was observed between this parameter and the electrical parameters. These results demonstrate that it affects the device's performance. Important electrical characteristics, like saturation current (IS), series resistances (RS ), ideality factor (n), and barrier height (bn ), are also defined for various temperature values by analyzing the current-voltage (I-V) behavior. R, b, and n parameters are computed using the Cheung method and the thermionic emission (TE) theory. The ideality factor (n) values were found to drop as temperatures increased, but the barrier height (bn) increased. The ideality factor values at the contact interface were found to exhibit a double Gaussian distribution based on temperature-dependent observations. Comparing the results to the experimental data revealed an equal result. These findings suggest that the n-AlGaAs structure may be effectively utilized in optoelectronic and photovoltaic applications, demonstrating excellent diode properties for solar cells. As a result, the outcomes of this recent research logically support the idea that these Schottky barrier diodes are ideal for effectively harvesting renewable energy.
Metal-Work Functions, Schottky diode, Barrier height, Electrical behavior, Ideality factor, Metal-semiconductor contacts, thermionic emission.
Progress on Predictive Performance of Metal Cutting by Machining Processes – Study Case: Turning Operations through Controlled-Contact
Studies on the development of models for the predictive performance of metal cutting by machining processes, initiated by CIRP, are currently ongoing at several research centers worldwide. This paper describes the successful outcomes achieved from primary research on the development of the ORTHO-OB CHATTER computer program, which enables data-driven predictive performance analysis of metal cutting by turning operations. Since data are critical to proper description of models, the work involved, gathering, generating and developing necessary support structured data that can be computationally applied through situated single- and multiple- input search queries, to predict generated forces, stresses, temperatures, strain rates, tool wear, possibility of fracture and tool life, stability and economic costs in lathe metal cutting involving any selected workpiece and tool materials combinations as a recommendation system to aid decision making. The paper also discusses predicting metal cutting operations under uncertainty by analyzing and identifying patterns and trends in data through mechanistic reasoning, providing a quick aid to help machinists select the correct size tools, cutting conditions, and make energy-saving decisions for a machining task. Validation shows errors in cutting force predictions by mechanistic reasoning of most material classes tested are within the range of zero to five percent.
Machining, Metal Cutting, Cutting Force Prediction, SMART Machining.
This paper presents the performance of the Teaching-Learning-Based Optimization (T.L.B.O.) algorithm for the optimum design of an air cooling system. The optimal cost of an air cooling system is investigated using the TLBO algorithm and compared with other optimization algorithms, including the Lagrange Multipliers (LM) method, Differential Evolution (DE) algorithm, and Particle Swarm Optimization (PSO) algorithm. TLBO is a recently proposed population-based algorithm that simulates the teaching-learning process in a classroom. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. Computational results demonstrate that the TLBO algorithm is successfully applied to the air cooling system, exhibiting better performance compared to other optimization algorithms considered for this problem.
Elaboration and Characterization of PVC Matrix Nanocomposites Reinforced with Nanoclay: A Case Study of Maghnite
Poly vinyl chloride (PVC)-Maghnite (MMT) nanocomposites were prepared by melt blending using a two-roll mill. The aim of this study was to investigate the effect of incorporating Algerian natural clay (Maghnite), extracted from the Roussel deposit in Maghnia, located in the northwest of Algeria, into the PVC matrix. This nanofiller was incorporated into the PVC matrix at low contents (1 to 4 phr) in two forms: raw (RMMT) and modified (OMMT) using a surfactant (hexadecylamine) and in the presence of a plasticizer (DOP). The properties of PVC-Maghnite nanocomposites were analyzed using multiple physicochemical techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), Light transmittance, scanning electron microscopy (SEM), and their resistance to degradation was also evaluated. The analyses conducted using XRD and SEM revealed that the modified Maghnite is well dispersed and exfoliated within the PVC matrix. The results obtained from TGA, combined with thermal stability studies and light transmission tests of the various nanocomposites, demonstrate that the incorporation of OMMT enhances the thermal stability of PVC through its barrier effect, which limits the diffusion of heat and volatile decomposition products. Furthermore, the modified Maghnite increases the light transmission (UV, visible, and IR) of the nanocomposites compared to pure PVC and composites containing unmodified Maghnite.
Crack Formation and Control Strategies in Concrete Systems
Concrete cracks in buildings are a common and routine occurrence that occurs in civil engineering, regardless of the nature and size of the work. It is almost unavoidable due to the nature of concrete material, i.e., its weak tensile strength, and for several environmental and structural reasons. The cracks not only deteriorate the aesthetic finish and appearance of the buildings but, above all, compromise their mechanical stability, safety, and life. They result in the weakening of major structural members such as slabs, beams, and columns, and thus destabilise the building. Concrete cracking is initiated by a number of causes, such as construction defects, defects in design, poor-quality materials, defective curing, and environmental factors such as variations in temperature, shrinkage, or chemical reaction. Engineers and scientists have been working hard over the past couple of years with the aim of understanding the crack initiation and propagation, with the aim of utilising appropriate preventive and corrective action. Detection and structural vs. non-structural differentiation of cracks are now possible, as the majority of them are obligated to carry out the right type of repair jobs. Their ability to repair is a factor of knowing the right repair method and material, depending on the width, direction, location, and level of the crack. There are cracks that are surface minor imperfections and others that are inherent flaws that require treatment with some level of urgency. This paper aims to induce cracking in concrete structures, i.e., causes, nature, and effects of some patterns of cracking in general. It also provides prevention strategies and remedial techniques, i.e., chemical injection, control joints, reinforcement, and advanced repairs such as epoxy injection, stitch cracking, and dry packing. Proper practice during construction and vigilant observation of structures have been stressed. They are significant steps in averting further increases in cracks. In short words, understanding causative causes of crack initiation and respective remedies can go a long way in the durability, safety, and performance of concrete structures over their required period.
Cracks, causes and remedial measures, preventive techniques
Unmanned Weather Station Real-time Data Dissemination via Twitter
Knowing what the weather will be like is important for most human activities. Therefore, most meteorological services are available online but provide generalized information. It is more reliable if a person can get weather information from their real-time environment. This project aims to create a point-specific online platform to distribute rapid and accurate meteorological data from a real-time environment of interest and send that data on social media without human involvement, using a fully automated system. This project used industrial sensors such as BME 280 for temperature, humidity, and pressure, ML8511 sensor to measure Ultraviolet radiation as an index, and customized sensors to get wind speed, wind direction, solar light intensity, and rain gauge. ESP 32 is a microcontroller used to process data and send that data to Google Sheets and Twitter. All the weather parameters are in standard units, and 0.01 accuracy of the reading. This is a fully automated system that uses a solar panel to run during the day and at night, using battery power that is charged during the day using solar power. In this instrument, most of the sensors are calibrated using chemical methods and laboratory instruments. The instrument is tested for several days indoors and several weeks outdoors. In testing, there are different weather scenarios, so all the instrument parameters are tested in real-time in the real world. In indoor testing, under less solar light conditions, it runs smoothly all day with the built-in battery for 4 to 5 days without charging. Using solar panels and batteries as a power source, it can run without human involvement. The purpose of the project is to send a designed unmanned weather station that sends weather data as a Twitter message, so the system is working smoothly; hence, the overall project is completed.
Unmanned weather station, Twitter, DIY Wind speed sensor, DIY Wind direction sensor, DIY rain gauge, Solar charger for ESP 32, ESP 32 Twitting
Manufacturing And Mechanical Testing of Duck Eggshell Powder Reinforced Epoxy Composite - A Study on Effect of Reinforcement
The world produces roughly 7.2 million tons of eggshell waste each year, which is about 11% of the weight of an egg. The leading egg-producing countries are China, India, and the United States. However, eggshell waste can also contribute to environmental pollution if it's disposed of in landfills. In landfills, eggshell waste can produce odors and encourage microbial growth. However, eggshells can be used in many ways including production of composite materials. Duck eggshells are popularly stronger than chicken eggshells, with a more compact palisade layer and higher calcium content. Since eggshells are a source of calcium carbonate (CaCO3), they could be used as fillers in composite materials to improve the mechanical properties. Using eggshell waste as fillers can help to reduce waste and increase the strength and wear resistance of the final product. This work aimed, for the first time, to investigate mechanical properties of duck eggshell epoxy composites. Duck eggshell powder is used with epoxy resin to prepare the composite material. Different types of composites are prepared using different wt% duck eggshell powder with epoxy matrix material. The composites are manufactured by simple casting pouring process. Specimens cut and tested for mechanical properties according to ASTM standards. The experimental results substantiate the potential use of duck eggshell powder as a reinforcement filler in polymeric composites, with 12 wt% being the optimal weight fraction for further applications.
Multi-Objective Electric Discharge Machining Process Parameters Optimization of Inconel 718 by using Full Factorial Design of Experiments.
This study investigates the optimization of Electrical Discharge Machining (EDM) parameters for Inconel 718 using a copper electrode, focusing on improving material removal rate (MRR) and minimizing surface roughness (Ra). A full factorial design of experiments (DOE) was employed with three key input parameters: pulse on time, pulse off time, and peak current. Regression models and machine learning algorithms were applied to predict response outcomes, with statistical validation through ANOVA and multi-objective optimization using desirability functions. The optimal parameters achieved an MRR of 54.12 mm³/min and Ra of 3.38 µm. The findings demonstrate the effectiveness of full factorial design of the exprements in enhancing EDM performance, supporting its adoption for precision machining of nickel-based super alloys.
Inconel 718, EDM, MRR, Ra, Machine Learning predictive model, full factorial, pulse on and off, peak current.
A Review: Aluminium Metal Matrix Composites
The behavior of the mechanical and tribological characteristics of aluminum metal matrix composites made using various techniques is reviewed in this research. Hybrid composites have demonstrated exceptional performance and versatility. Excellent strength-to-weight ratio, resistance to erosion and wear, and relatively low cost are the benefits of aluminum Metal Matrix Composites (MMCs). Because of their remarkable specific strength and thermal stability, they are used in various industries, including steel production, structural engineering, marine engineering, aerospace, defense, and the automotive sector. Unconventional engineering materials known as Metal Matrix Composites (MMCs) are reinforced with materials that exhibit superior mechanical and tribological properties. The most common types of reinforcement include fly ash, graphite, silicon carbide, TiO2, boron carbide, and particulate alumina. In addition to their reinforcement in various applications, this paper provides an overview of their mechanical characteristics and tribological behavior. The effects of various reinforcements on aluminum composites vary. For instance, boron carbide enhances the tensile strength, electrical conductivity, thermal conductivity, and elastic modulus because of its lubricating properties. The inclusion of aluminum oxide produces favorable tribological behavior. Using fly ash improves the mechanical characteristics overall, yield, and tensile strengths. The Al composites are also affected differently by other reinforcements.
Aluminium metal matrix composite, reinforcement, silicon carbide, alumina, graphite, tribological behavior, Mechanical properties.
Traces of Similarity and Consistency of Data in the Choices of Specific Cutting Energy Models for Predicting Cutting Force in Metal Cutting by Machining Processes
The specific cutting energy is of significant interest in predictive performance evaluation in metal cutting, as it aids in estimating the main cutting force. Several models and methods have been proposed for estimating the specific cutting energy, with some suggested models under-predicting, some over-predicting, for a given set of defined cutting conditions. This paper presents some applicable methods and reported supporting data for computational predictive evaluation during metal cutting, with validations on actual representative metal cutting data. Evidence shows that each proposed model can predict the main cutting force through simulated variation of the cutting conditions.
Specific Cutting Energy, Cutting Stiffness, Cutting Force, Specific Cutting Pressure.
Evaluation of Flash temperature parameter in TMPTO-Based Nano-Lubricants
Flash Temperature Parameter (FTP) is the temperature at which a lubricant ceases to function effectively, leading to metal-to-metal contact, increased friction, wear, and possible equipment failure. This study explores the impact of TiO2 nanoparticles in trimethylolpropane trioleate (TMPTO)-based bio-lubricants on the Flash Temperature Parameter. A four-ball tester assesses the Anti-Wear Scar Diameter (AWSD) of TMPTO-TiO2 nano-lubricants at varying concentrations under controlled speed, load, and temperature conditions. The FTP is determined from the average AWSD measured on the bottom three balls. Results from Taguchi experiments and corresponding signal-to-noise ratios were used to rank the parameters. The combination of TMPTO base oil and TiO2 nanoparticles synergized to enhance FTP. FTP assesses a lubricant’s performance under high load and temperature conditions, such as in rolling mills and cutting processes. This research may benefit specialty lubricant manufacturers. Further studies on the wear mechanisms of TMPTO-based nanolubricants and their potential for high-speed metal cutting applications are recommended.
Trimethylolpropane trioleate, Flash Temperature Parameter, ANOVA, Taguchi
Enhancing Material Performance: A Review of Magnesium-Based Metal Matrix Composites
Magnesium Matrix Composites (Mg-MMCs) are emerging as highly promising materials for aerospace and defence applications due to their low density and favourable mechanical properties. Compared to conventional engineering materials, these composites exhibit improved specific strength, stiffness, damping behaviour, wear resistance, creep, and fatigue properties when reinforced with various elements. This paper provides an overview of the effects of different reinforcements in magnesium and its alloys, highlighting their advantages and drawbacks. Key phenomena such as interfacial transition, agglomeration effects, fibre-matrix bonding, and particle distribution challenges are discussed in detail. The impact of reinforcements on microstructure and mechanical properties, including tensile strength, yield strength, ductility, strain, hardness, wear, and fatigue, is critically analyzed. The study also reviews processing techniques, characterization, and the tribological and mechanical behavior of Mg-MMCs, with a focus on reinforcements like carbon nanotubes (CNT), carbonaceous materials, fullerenes, SiC, Al2O3, TiC, B4C, and graphene. Observations indicate that ceramic reinforcements enhance hardness and strength but reduce ductility, whereas titanium-based metallic reinforcements improve both ductility and strength. Significant applications of various magnesium MMCs are also examined.
Magnesium, MMC, Reinforcement, Hardness, MMC Fabrication method
Analysis of Casting System Design of Using a Computer-Aided Simulation
Due to casting defects, the Akaki Basic Metals Industry (ABMI) encounters a significant rate of rejection and rework concerning flange roller products, exacerbating manufacturing lead times, increasing production costs, and diminishing customer satisfaction. According to the factory production reports, 89 flange roller cast parts were rejected due to casting defects, resulting in a financial loss. The primary factor contributing to the casting defects that lead to product rejection is the inadequacy of the gating and feeding system design, coupled with ineffective process control. Consequently, the primary objective of this paper is to analyze the casting system design for the flanged roller utilizing a computer-aided simulation methodology. To achieve this goal, the existing gating and feeding system of the flanged roller is modelled using CATIA V5, and simulation analyses are performed employing ProCAST 2019 casting simulation software. From the simulations, shrinkage porosity and hot spot defects have been identified as the principal defects present in the product. In summary, it is concluded that computer-aided casting simulation facilitates the company's ability to preemptively manage defects prior to their occurrence in the physical production environment.
Casting defects, casting simulation, shrinkage porosity, hotspot
Copyright (c) 2020 Journal of Manufacturing Engineering
Mathematical Modeling of Shape relationship including % Dilution and Total Bead Volume in SAW Process by using Two Level Half Factorial Technique
The fabrication industry extensively uses SAW (Submerged Arc Welding) owing to its quality, precision and high production rate. The quality and strength of the weld are controlled by the weld bead geometry criterion and shape relationship, which are greatly influenced by the SAW process control variables, namely welding current, arc voltage, welding speed and nozzle-to-plate distance. The present study aims to develop mathematical relations between weld penetration shape factor (WPSF), weld reinforcement form factor (WRFF), % dilution, and total volume with process control variables. Experiments were conducted using “Two Level Half Factorial Design Techniques”. Design Expert software was deployed for graph plotting for the primary and interaction effects of process control variables on bead geometry parameters. Results indicated that voltage affects WPSF positively, while welding current and nozzle-to-plate distance affect WPSF negatively, and welding speed has an insignificant effect on WPSF. Similarly, for WRFF, arc voltage and welding speed have a positive effect, while welding current and nozzle-to-plate distance have a negative impact. For % dilution, nozzle-to-plate distance has an adverse effect and welding current has an insignificant impact. Welding current effects, the total bead volume, arc voltage, welding speed, and nozzle-to-plate distance have adverse effects.
Bead geometry, Mathematical model, Response, Two-level half factorial design technique, Design Expert Software.
Affordable virtual and augmented reality training modules for workforce development and smart manufacturing
In the face of rapid technological advances, including digital, additive, and smart manufacturing, it is essential to continually update manufacturing competency to facilitate skilled workforce development. This study explains a user-friendly Virtual Reality/Augmented Reality/ (VR/AR) training framework for training manufacturing processes. Process equipment and operational protocols are complex and demand for highly skilled and trained operators. Operators trained using the current methods, including in-class and computer-based training, continue to need help with some of the key process equipment operations, causing batch failures resulting in significant loss of time and money. Conventional avenues and traditional computer-based training have been routinely employed to educate operators. However, VR/AR-based process training will remedy the exposed inadequacies of smart manufacturing training processes that are causing continued operator errors. The virtual simulation offers coordinated visual and proprioceptive (spatial perception) feedback cues to develop motor skills for procedural knowledge and memory. The interactive training explores the relationships between actions and outcomes to promote schema building and strengthen trainees' mental models in manufacturing operations. This project has a significant commercial impact within the manufacturing industry owing to increased operational efficiency, reduced product delivery time, and increased accuracy. Optimal training aids affordable, precise, and quality product delivery for generic and personalized medicines to needy patients, thus resulting in a significant societal impact. It will positively impact the professional development of trainees and operators through training in cutting-edge research, innovation and entrepreneurship.
Copyright (c) 2024 Journal of Manufacturing Engineering
Live Dimension Detection During Machining of Workpiece using Smart Detection Set up on General-Purpose Lathe
In a manufacturing setting, the profile dimensions of the workpiece are key quality factors. Various automatic inspection approaches currently assess dimensions based on characteristics and variables, tool paths as OK or Not OK in CNC, and online inspection methods for other machines after manufacturing. However, continuous on-machine profile dimensions and simultaneous manufacturing were left to focus. An intelligent recognition system based on a laser lines sensor using a smart profile dimension detection algorithm (SPDA) communicates real-time measurement information to the operator. It plots the current status of the Workpiece, mapping linear distance by accurate laser reflection of the exact mid-surface line of the workpiece as profile data of a symmetric shaft. It compares the length-diameter obtained from longitudinal and lateral sensors with the master data provided. The algorithm implements Python libraries for displaying panels and organizing workpieces. The standard image changes color from red to green as it is relevant to the status. The predetermined order of manufacturing parts and gradient descent optimization feature sets benchmarks to display the status of the workpiece. The real-time live measurement is facilitated with an error of less than 10%, i.e., 0.1 mm, saving manufacturing time by 30%. It also eliminates the inspection stage, and no reduced job rejection is found.
Stepped Shaft, Real-Time Profile Dimension Detec-tion, Laser Line Sensor, Algo-rithm, Inspection Stage, Manufac-turing Time
Barriers to Adopting Metaverse Technology in Manufacturing Firms
Metaverse technology (MT) signifies a computer-generated sphere where operators can interconnect in a virtual setting. The adoption of MT has the possibility to transform business operation methods, business-to-customer interaction, and firm-worker collaboration approaches. On the other hand, some problems should be tackled and overcome to guarantee the success of adopting the MT. The research seeks to investigate MT adoption in manufacturing firms, emphasising estimating possible hindrances to present effective adoption plans. The data for this study was gathered from 311 participants in manufacturing companies in Palestine. This study uses machine learning tools, namely artificial neural network (ANN) modelling, combined with structural equation modelling (SEM) to assess the theoretical framework. The SEM findings showed that a company's technological inadequacies are the most critical obstacle to MT adoption in manufacturing firms. The ANN findings underlined the company's technological inadequacies as the most vital input, poor regulation and governance, incorporation barriers, low perceived usefulness among consumers, weak collaborative actions, low dedication from stakeholders, conservative company culture, and low transmission over the networks. As MT is prospective to offer companies competitive benefits, better efficiency, enhanced consumer involvement and stimulated innovation, it is imperative to deliberate and foster resolutions to adoption risks in the businesses. Firms can achieve successful adoption of the intriguing and rapidly everchanging technological environment by solving those risks/barriers. The research gives new understandings to MT inventors and managers for effective MT adoption in manufacturing firms and novel conceptual insights for practitioners intending to adopt this technology in their manufacturing firms.
Slicing on the Tessellated Model for the 3D Printing Process based on the Variable Thickness Layers
Additive Manufacturing (AM) is a modern technology that enhances manufacturing by building thin layers of material from digitized (3D) designs, entirely constructed using advanced CAD software. This method facilitates the creation of new types of objects with unique material properties. However, while AM is often hailed as the next industrial revolution, significant challenges remain for its successful commercialization. This project focuses on the microstructural aspects and the dimensional accuracy of the product. Additionally, the paper discusses the slicing algorithm and the material extrusion process in detail.
Additive Manufacturing (AM), CAD, 3D Printing, Slicing
Investigations on Mechanical Properties of Nano Particulates (Al2O3/B4C) Reinforced in Aluminium 7075 Matrix Composite
Metal Matrix Nano Composites (MMNCs) with the addition of nano-particulate reinforcements can be of significance for automobile, aerospace, and numerous applications due to their low density and good mechanical properties, better corrosion and wear resistance, low coefficient of thermal expansion as compared to conventional materials. Designing the metal matrix composite material aims to combine the desirable attributes of metals and ceramics. The present work is focused on studying the mechanical properties of aluminium alloy (7075) with Al2O3 and B4C nanocomposite produced using the stir casting technique and ultrasonic cavitation method. Different % age of reinforcement is used. An ultrasonic cavitation technique is used to achieve a uniform dispersion of nano-particulate Al2O3 and B4C in molten aluminium alloy. Tensile, Hardness, and Impact tests were performed on the samples obtained by the fabrication processes. A structural study will be carried out through a Scanning Electron Microscope (SEM) to know the distribution of Al2O3/B4C Nano particulates in Al alloy.
Al7075, Ultrasonic Cavitation, Scanning Electron Microscope (SEM).Nano composite, reinforce particulates, metal matrix composite
Parametric Optimization of Warm Deep Drawing Process of 304 Stainless Steel Cylindrical Cups
The present work is a comprehensive parametric optimization of the warm deep drawing process of 304 stainless steel. The warm deep drawing process is a crucial metal forming technique that leverages elevated temperatures to enhance the material's formability and reduce defect occurrence. This study systematically investigates the influence of various process parameters, including sheet thickness, temperature, coefficient of friction, and strain rate, on the quality of the final product in order. Using the Taguchi method for the design of experiments and Finite Element Analysis (FEA) for simulation, the research aims to determine the optimal settings for these parameters. Every parameter played its part in responses. All the factors had a similar impact on the cups designed. The strain-based FLDs are prepared to study the strain levels in the process. The optimized cup was the trail 8 cup with 1 mm, 300°C, 0.02 and 1000s-1.