Single Process Parameter Research Articles

A Comparative Analysis of Single and Multi-objective Process Parameter Optimization of Dissimilar Metal Friction Welds

A Comparative Analysis of Single and Multi-objective Process Parameter Optimization of Dissimilar Metal Friction Welds

Grey-Taguchi approach to optimize the tribological performance of hybrid composites

Modern manufacturing technology is evolving, necessitating the creation of materials with higher wear resistance. This research endeavors to forecast the dry sliding wear performance of AL6061-B4C-RM hybrid composites by employing Taguchi and grey relational analysis approaches. Present study applies the L16 Taguchi approach to optimize the process parameters specific wear, and coefficient of friction (COF) at four different levels. Using multiple responses for optimization for both responses, a single process parameter setting is obtained via grey relational analysis. In AL6061 composites, varying weight proportions of Boron Carbide particles (B4C) were added as reinforcements while keeping the red mud (RM) content constant at 2 %. To recognize the dominant factor, analysis of variance (ANOVA) was implemented, and the outcomes of the assessment showed speed plays a crucial role, followed by the weight percent of reinforcement and the load in determining the specific wear rate and COF. A research trial was completed to confirm the outcomes obtained from the combination of optimal parameters.

Modeling and optimizing of laser-assisted waterjet process parameters for near-damage-free micromachining of monocrystalline silicon

Monocrystalline silicon-based microstructure functional surfaces are widely used in the field of photoelectricity. However, the traditional process is prone to damage due to the hardness and brittleness of monocrystalline silicon. In this study, the depth, width, and depth-width ratio of the micro-V-groove on the surface of monocrystalline silicon were studied using laser-assisted waterjet technology. Firstly, the nanosecond laser pulse width was optimized to minimize processing damage. Then, the response surface method (RSM) was employed to optimize the V-groove’s depth-width ratio and show the process parameters’ interactive effects. The results show that the heated affected zone (HAZ) and lattice disorder at 10 ns are significantly smaller than those at other pulse widths, and there is almost no amorphous phase on the surface of V-grooves. Single process parameter usually has different effects on the size of the V-groove, and the process parameters have significant interactive effects, which are discussed in detail in the study. According to the prediction model, the highest depth-width ratio of a V-groove is 1.39. Near-damage-free V-groove with a high depth-width ratio helps to lower the reflectivity and increase the hydrophobicity of monocrystalline silicon.

Fluidized bed drying of supported Catalysts: Effect of process parameters

Fluidized bed drying is an important unit operation in the manufacturing of supported catalysts. This unit operation remains a challenging step due to simultaneous heat and mass transfer, making it difficult to predict and scale up. This work focuses on applying a mathematical model to predict drying curves and drying rates in a fluidized bed dryer. The model was compared to experiments in a Glatt GPCG-1 fluidized bed dryer. Experiments were conducted by varying air flow rate, bed loading, inlet air temperature, and initial moisture content using water impregnated supports. Results from the model showed good agreement with experimentaldrying curves. The model predicted the drying time for cases that were not initially fluidized. The modelpredicts drying curves for changes in single process parameters when the experiments started in the same drying period. Lastly, the model prediction was extended to multiple changes in process parameters and showed good agreement.

Experimental tensile strength analysis of ABS material through FDM technique

Additive manufacturing (AM) is a growing technique for generating intricate, challenging manufacturing things. Rapid prototyping for product development and design is typically done in industrial workplaces using fused deposition modelling, a layer-by-layer manufacturing technique. Using additive manufacturing technology, complex products may be easily produced. Patterns and percentages of infill have been researched. The majority study is established by findings from experiments using different single process parameters. Because infill patterns have an impact on manufacturing costs, material costs, and quality, they must be thoroughly studied. In this study, simulations of rectangular and triangular patterns with 25% were accomplished. During the analysis at 25% infill, it was found that the triangle pattern exhibits lower levels of stress compared to the rectangular pattern. For applications utilizing 25% infill, rectangular patterns demonstrate more favorable performance than triangular ones. This research work takes the overview of Finite Element Analysis (FEA) and Experimental Tensile Strength Analysis of Acrylonitrile Butadiene Styrene (ABS) Material through the Fused Deposition Modelling (FDM) Technique.

MeltPoolGAN: Auxiliary Classifier Generative Adversarial Network for melt pool classification and generation of laser power, scan speed and scan direction in Laser Powder Bed Fusion

A reliable classification architecture for melt pool images is crucial for real-time monitoring and control in additive manufacturing. It enables for the identification of discrepancies between actual and desired process parameters, which could lead to defects in printed part. Moreover, adjusting process parameters according to classification results promotes consistent melt pool geometry and leads to enhanced part quality. In addition to the classification, controlled generation of melt pool images can be employed for data generation, training of classifiers, and offline process parameter optimization along the scan path. This study introduces MeltPoolGAN, a novel machine-learning architecture designed for handling both of these tasks. The architecture is able to classify up to 371 classes consisting of process parameters such as laser power, scan speed and scan direction. This is a significant increase in the total number of classes as well as process parameters compared to state-of-the-art that only classified a maximum of 6 classes, consisting of a single process parameter. With this input, the MeltPoolGAN reaches accuracies of around 97% for power and scan speed class classification, and scan direction estimation errors below 3 degrees making it a reliable solution. Furthermore, the MeltPoolGAN is able to generate plausible melt pool images in controlled and flexible manner based on disentangled input parameters. Finally, since the architecture was trained and tested on two distinct datasets, the publicly available NIST dataset and the in-house created ETH dataset, its robustness and capabilities of being a general-purpose AI model are also demonstrated.

In Situ Electrical Network Activation and Deactivation in Short Carbon Fiber Composites via 3D Printing

AbstractPrior studies on carbon‐filler based, conductive polymer composites have mainly investigated how conductive filler morphology and concentration can tailor a material's electrical conductivity and overlooks the effects of filler alignment due to the difficulty to control and quickly quantify the filler alignment. Here, direct ink write 3D printing's unique ability is utilized to control carbon fiber alignment with a single process parameter, velocity ratio, to instantaneously activate or deactivate the electrical network in composites. Maximum electrical conductivity is achieved by randomly aligning carbon fibers that enhances the chance of direct fiber‐to‐fiber contact and, thus, activating the electrical network. However, aligning the fibers by increasing the velocity ratio disrupts the electrical network by minimizing fiber‐to‐fiber contact that resulted in a drastic decrease in electrical conductivity by as much as five orders of magnitude in both short and long carbon fiber composites. With this study, this study demonstrates that electrically conductive or insulative composites can be fabricated sequentially with a single ink. This novel ability to instantaneously control the electrical conductivity of carbon fiber reinforced composites allow to directly embed conductive pathways into designs to 3D print multifunctional composites that are capable of localized heating and self‐sensing.

Development, Characterization and Process Optimisation of Foam- Mat Dried Tomato-Pepper-Onion Powder (TPOP Powder)

This study aimed to develop and optimize the foam-mat drying process for the production of shelf-stable tomato-pepper-onion powder (TPOP) with optimal quality characteristics using D-optimal design. The research commenced with the formulation of the TPOP mixture and investigated various ratios of the three major ingredients (tomato, pepper and onion) to achieve an optimal blend that offers a harmonious flavor profile and high nutritional value. Subsequently, the foam-mat drying process was explored, involving the use of foaming agents to stabilize air bubbles within the vegetable puree matrix. This process yielded a lightweight, porous and free-flowing powder with improved rehydration properties. A single process parameter namely drying temperature was optimized while keeping the whipping time of 7mins constant. The levels for various input variables were tomato (5 – 76%), pepper (5 – 78%), onion (5 – 80%), egg white (2 - 15%), carboxymethyl cellulose (0.15 - 0.75%) & drying temperature: 50-800C. Some selected physico-chemical, nutritional properties and sensory evaluation were conducted. The optimum condition with the best formulation of the mixture ingredients were found to be 67.26% tomato, 5.0% pepper, 20.0% onion, 7.59% egg white and 0.15% CMC at an optimum drying temperature of 61.940C. Results showed that foam-mat drying produced a powder with desirable physicochemical and nutritional properties. Sensory evaluation showed that the reconstituted powder had good acceptability.

Effect of Single and Double Pulse MIG Welding Process Parameters on Microstructure and Properties of Aluminium Alloy

Lightweight aluminium alloys are widely used in many industries due to their advantageous mechanical properties. As welding is a popular method for joining aluminium alloy sheets, it is important to understand how the welding process affects the alloy’s performance. Here, we investigated the influence of various operating parameters during pulsed metal inert gas (MIG) welding of thin aluminium alloy plates on the input energy and metallurgical microstructure of the weld. A series of welding tests were performed using surface welding of AA6061 aluminium alloy plates under conditions of a single strong-pulse, single weak-pulse, or double pulse. The morphology, metallurgical structure, and hardness of the welds produced using the different welding parameters were then examined. We observed that increasing the welding speed and the arc duration of low-energy pulses can reduce the heat input. Lower heat input is advantageous as it results in a higher cooling rate of the welding pool and a smaller average grain size of the weld structure. In addition, increasing the welding speed increased the hardness of the weld. Double pulsed MIG welding showed the best overall weld performance and is recommended for improving the quality of aluminium alloy bonding.

Experimental study on forming process of the high-precision high-alkali glass tube

Rigid optical fiber products (optical fiber panels, image inverters, etc.) are a kind of image coupling and transmission device with excellent performance, which is formed by regular arrangement and fusion of tens of millions of optical fibers with a diameter of 3 to 6 μm. As the basic material for optical fiber, the high-alkali glass tube is one of the cores of optical fiber technology. The geometric parameters and precision of the high-alkali glass tube are completely determined by the molding process. This paper summarizes the influence of a single process parameter on the geometric parameters of the glass tube by studying the aspects of temperature, pressure in the tube, and traction speed, and systematically analyzes a large number of experimental data to establish an overall forming control theory, which provides a theoretical basis for actual production and proposes an improvement plan for high-precision high-alkali glass tube forming equipment to improve product quality.

Machine-learning-based monitoring and optimization of processing parameters in 3D printing

ABSTRACT Additive manufacturing (AM), commonly known as 3D printing, is a rapidly growing technology. Guaranteeing the quality and mechanical strength of printed parts is an active research area. Most of the existing methods adopt open-loop-like Machine Learning (ML) algorithms that can be used only for predicting properties of printed parts without any quality assuring mechanism. Some closed-loop approaches, on the other hand, consider a single adjustable processing parameter to monitor the properties of a printed part. This work proposes both open-loop and closed-loop ML models and integrates them to monitor the effects of processing parameters on the quality of printed parts. By using experimental 3D printing data, an open-loop classification model formulates the relationship between processing parameters and printed part properties. Then, a closed-loop control algorithm that combines open-loop ML models and a fuzzy inference system is constructed to generate optimized processing parameters for better printed part properties. The proposed system realizes the application of a closed-loop control system to AM.

Optimization of molding process parameters for enhancing mechanical properties of jute fiber reinforced composites

Compression molding is a manufacturing technique that is frequently and widely utilized in fabricating composites. However, bast fiber reinforced composites (BFRC) with good quality and high performance by compression molding are quite challenging because it is complicated to find suitable process parameters. Previous works on improving the mechanical performance of compression molded BFRCs normally considered the influence of a single process parameter and ignored the mutual effects of process parameters. To enhance the mechanical qualities of BFRCs and enlarge their applications, this paper studied the synergistic effects of two molding process parameters on the mechanical characteristics of jute reinforced polypropylene composites (jute/PP) from contour and surface plots through response surface methodology (RSM). The significance of each molding process factor on the mechanical behaviors of jute/PP was investigated by conducting the analysis of variance (ANOVA). Multi-parameter co-optimization achieved the optimal mechanical performance of molded jute/PP at 186.8°C, 5.6 MPa, and 10.1 min, which was consistent with experimental findings. This suggests that response surface equations obtained from RSM can accurately characterize the relationships between mechanical characteristics and molding parameters of jute/PP. The RSM provides a useful and reliable way to find the optimal process parameters for better mechanical behaviors.

Experimental Analysis of Surface Roughness in the Cutting Process of Sugar Palm Fiber Reinforced Unsaturated Polyester Composites with Laser Beam and Abrasive Water Jet Cutting Technologies

This research aims to investigate the effects of the input parameters on the surface roughness as output parameters of CO2 laser and abrasive water jet (AWJ) machining technologies utilized in cutting sugar palm fiber reinforced unsaturated polyester (SPF-UPE) composite of three specimen thicknesses. The objective of this study is to collect data involve the optimal parameters of these technologies regarding the surface roughness response. The motive was to avoid defects arising use in the conventional cutting techniques. In the AWJ technique, stand-offdistance, traverse speed, and water pressure were chosen as variable input parameters to optimize the surface roughness, whereas laser power, traverse speed, and gas pressure were the variable input parameters in the CO2 laser cutting technique. Taguchi’s approach was used to estimate the input parameter’s levels that produce the best surface roughness. Analysis of variation (ANOVA) was used to determine the contribution of every single input processing parameter to the effect on the surface roughness response. Good surface roughness responses could be attained by applying the optimum input parameters determined in this study. The experimental results of the current research provide practical data for the cutting of SPF-UPE composites with CO2 laser and AWJ machining techniques, and the findings can be used as a good starting point for the testing of other similar composites under the same cutting conditions

Parametric analysis and a predictive model for color difference during laser-induced coloration on titanium.

Laser-induced coloration on a metallic surface has been of interest to many application arweas. However laser machining of metals involves many complex problems including nonlinear unstable coupled with multiple factors. Therefore there are still some significant challenges in the precise control of color creation. Here we explored the process of the laser-induced coloration and find the connection between surface colors and processing parameters. The Response Surface Methodology (RSM) based experimental design was adopted to explore the influence of the single processing parameter and the interaction between parameters on color changes of titanium. The results showed that the scanning speed laser power repetition rate and hatch distance had significant effects on color changes of titanium. Then we demonstrated that using artificial neural network (ANN) is an effective solution of nonlinear problems in laser-induced coloration which can match the processing parameters and the L*a*b* color values on titanium surface precisely with limited experiments. Finally we successfully used the processing parameters estimated by ANN model to create unique art painting on titanium with nanosecond pulsed laser. This work can provide a potential method to solve the problem in the color consistency and open a new perspective in industrial application of laser-induced coloration technology.

A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems

A permanent magnet actuator (PMA) is a critical device for transforming, transmitting, and protecting electrical energy in renewable energy systems. The reliability of a PMA exerts a direct effect on the operational safety, stability, and reliability of renewable energy systems. An effective fault diagnosis and adjustments for manufacturing processes (MPs) are vital for improving the reliability of a PMA. However, the state-of-the-art fault diagnosis methods are mainly used for single process parameters, extensive sample data, and automated manufacturing systems under real-time monitoring and are not applicable to a PMA with low levels of automation and high human factor-induced uncertainties. This study proposes a novel fault diagnosis approach based on a surrogate model and machine learning for multiple manufacturing processes of a PMA with insufficient training data due to human factor uncertainties. First, a surrogate model that correlated the MP parameters with the output characteristics (OCs) was constructed by a finite element simulation. Second, the quality performance of the OCs under different fault combinations with the mean or variance of the shift of the MP parameters as typical patterns was calculated by the Monte Carlo method. Finally, using the above computations as the training data, a fault diagnosis model capable of identifying the fault pattern of the manufacturing process parameters according to the OCs was constructed based on machine learning. This approach compensated for the inadequacies of traditional fault diagnosis methods with complex analytical models or numerous processing data. The effectiveness and potential applications of the proposed approach were verified through a case study of a rotary PMA in smart grids.

Effect of Substrate-RF on Sub-200 nm Al0.7Sc0.3N Thin Films.

Sc-doped aluminum nitride is emerging as a new piezoelectric material which can substitute undoped aluminum nitride (AlN) in radio-frequency MEMS applications, thanks to its demonstrated enhancement of the piezoelectric coefficients. Furthermore, the recent demonstration of the ferroelectric-switching capability of the material gives AlScN the possibility to integrate memory functionalities in RF components. However, its high-coercive field and high-leakage currents are limiting its applicability. Residual stress, growth on different substrates, and testing-temperature have already been demonstrated as possible knobs to flatten the energy barrier needed for switching, but no investigation has been reported yet on the whole impact on the dielectric and ferroelectric dynamic behavior of a single process parameter. In this context, we analyze the complete spectrum of variations induced by the applied substrate-RF, from deposition characteristics to dielectric and ferroelectric properties, proving its effect on all of the material attributes. In particular, we demonstrate the possibility of engineering the AlScN lattice cell to properly modify leakage, breakdown, and coercive fields, as well as polarization charge, without altering the crystallinity level, making substrate-RF an effective and efficient fabrication knob to ease the limitations the material is facing.

Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution

The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared.

The Significance of Electrical Polarity in Electrospinning: A Nanoscale Approach for the Enhancement of the Polymer Fibers' Properties

AbstractThanks to its versatility, electrospinning is a method often used to produce polymer fibers for bioengineering, water, and energy harvesting applications. The change of a single process parameter in electrospinning provides a tremendous opportunity to modify the properties of the materials produced without any additional post‐treatment. One of these is the voltage and electrical polarity applied to the nozzle. The alternating polarity, between positive and negative, causes a change in the types of charges accumulated on the polymer jet. Consequently, the produced fibers show different molecule configurations on the surface, resulting in changes in surface chemistry while tailoring their properties to fit various application needs. This review explains polymer chain reorientation phenomena caused by electrical polarity in electrospinning. Furthermore, the electrical polarity effect on the surface and the mechanical properties of electrospun fibers, and the methods to investigate it is demonstrated. Finally, this review illustrates the significance of electrical polarity in electrospinning as a nanoscale approach to enhance fiber surface properties and their applicability.

Numerical comparison of additive manufacturing of ABS material based on infill design subjected to tensile load

Additive manufacturing is a developing approach for producing complicated, difficult manufacturing objects. Fused deposition modeling is a manufacturing technique that works on a layer-by-layer basis that is widely utilized in industrial applications for quick prototyping in product design and development. The application of additive manufacturing technology allows the easy creation of complicated objects. Researcher have worked on infill patterns and infill percentages. Most of the research is based on experimental results with varying single process parameters. A study on infill patterns is an important aspect because it influences quality, material cost as well as production cost. In this research work, the simulations were carried out on the 50% and 70% infill rectangular and triangular patterns. It was found that, stresses generated in the triangular pattern are large compared to the rectangular pattern at the 50% infill and vice versa for 70% infill. For 50% infill, the triangular patterns are best suited for applications than the rectangular pattern, and for 70% infill, the rectangular patterns are best suited for applications than the triangular Pattern.

Comparative Study Regarding the Welding Behavior of Galvanized Steel Using Laser Welding and Gas Metal Arc Welding Processes

Gas metal arc welding (GMAW) and laser welding are two commonly used joining methods. In this study, these two welding methods were used to join the galvanized sheets. For GMAW, the process parameters were: welding wire diameter, shielding gas flow, welding speed, current and wire feed speed. In term of laser welding, the analyzed parameters were: welding speed, power and pulse duration. In order to establish the effects of the main parameters on the resistance of the welded joint, changes were made to the values ​​of a single process parameter, and the others were kept constant. The combined welded samples were examined non-destructively using X-rays method and then were subjected to destructive tensile tests, analyzing the tensile strength values.