Optimisation of micro W-bending process parameters using I-optimal design-based response surface methodology

Purpose This paper aims to focus on an experimental study of surface roughness, dimensional accuracy and time of fabrication of parts produced by fused deposition modelling (FDM) technique of additive manufacturing. The fabricated parts of acrylonitrile butadiene styrene (ABS) material have pyramidal and conical features. Influence of five process parameters of FDM, namely, layer thickness, wall print speed, build orientation, wall thickness and extrusion temperature is studied on response characteristics. Furthermore, regression models for responses are developed and significant process parameters are optimized. Design/methodology/approach Comprehensive experimental study is performed using response surface methodology. Analysis of variance is used to investigate the influence of process parameters on surface roughness, dimensional accuracy and time of fabrication in both outer pyramidal and inner conical regions of part. Furthermore, a multi-response optimization using desirability function is performed to minimize surface roughness, improve dimensional accuracy and minimize time of fabrication of parts. Findings It is found that layer thickness and build orientation are significant process parameters for surface roughness of parts. Surface roughness increases with increase in layer thickness, while it decreases initially and then increases with increase in build orientation. Layer thickness, wall print speed and build orientation are significant process parameters for dimensional accuracy of FDM parts. For the time of fabrication, layer thickness and build orientation are found as significant process parameters. Based on the analysis, statistical non-linear quadratic models are developed to predict surface roughness, dimensional accuracy and time of fabrication. Optimization of process parameters is also performed using desirability function. Research limitations/implications The present study is restricted to the parts of ABS material with pyramidal and conical features only fabricated on FDM machine with delta configuration. Originality/value From the critical review of literature it is found that some researchers have made to study the influence of few process parameters on surface roughness, dimensional accuracy and time of fabrication of simple geometrical parts. Also, regression models and optimization of process parameters has been performed for simple parts. The present work is focussed on studying all these aspects in complicated geometrical parts with pyramidal and conical features.

The present work highlights laser micro-turning operation of 10-mm diameter cylindrical-shaped alumina (Al2O3) ceramic using pulsed Nd:YAG laser. The paper also addresses development of mathematical models for correlating the various micro-machining parameters such as laser beam average power, pulse frequency, workpiece rotational speed, assist air pressure, and Y feed rate with the response criteria such as surface roughness and deviation in turned depth for achieving desired surface quality as well as dimensional accuracy during micro-turning operation using Nd:YAG laser system. Response surface methodology-based design of experiments has been adopted for the experimentation. This investigation also highlights the various test results that confirm the validity and correctiveness of the developed mathematical models through analysis of variance test. The test results were analyzed through various response surface plots to study the effect of the process parameters on the aforementioned responses. The results of validation experimentation show a good agreement for the developed empirical models. Sensitivity analyses of the developed models have been done to find out the variation in the output with respect to variations in the significant input process parameters. Moreover, multi-performance optimization has been done to find out the optimal parametric setting for achieving the desired process performances. Analysis also has been made based on scanning electron microscopy micrographs of the laser micro-turned surface achieved during machining at multi-criteria optimization setting.

For many years, injection molding is adopted as the most suitable manufacturing process in the production of complex plastic components. It is very difficult to boost the product's quality stability to attain a higher forming speed and higher automation in the injection moulding process because the process often disrupted by numerous inevitable deviations. The settings for different process parameters and machine parameters have quite a major influence on the overall product quality and its performance of the injection moulding process, which shows the final product quality as output responses. Identifying the perfect process parameter settings so that numerous responses may be optimized at almost the same time is becoming a significant research challenge these days. Smart manufacturing is at the heart of the new industrial revolution, and it will continue to be the primary means of transforming and upgrading the manufacturing industry in the upcoming decades. Melt temperature, the temperature of mould, injection pressure, and velocity of molten polymer while injection, injection time, pressure of molten polymer while holding into the mold cavity, holding time of molten polymer, and cooling time are the variety of input process parameters.  While mechanical characteristics, flaws such as warpage, volumetric or dimensional shrinkage, sink mark on outer surface, residual stress, and surface finish/roughness are examples of responses. To study and optimize the molding process, researchers have used a wide range of methods. Mathematical modeling, simulation, design of experiments, Taguchi methodology, analysis of variance - ANOVA, response surface methodology - RSM, evolutionary algorithm (EA), artificial neural networks - ANN, genetic algorithms - GA, fuzzy logic, particle swarm optimization - PSO, are examples of these techniques. This paper presents a review of the numerous strategies that used till date for optimizing the key injection moulding parameters, as well as their benefits and challenges. In the review, it is discovered that a perfect injection molding process, which takes care of overall quality control is still being researched upon. The process parameters involved in injection molding, required response, and the methodologies used for optimization of overall injection molding process are discussed throughout this paper.

The study focuses on improving the production processes' dimensional accuracy for thick materials through the optimization of the area of contact during machining, forming, and additive manufacturing. The performance and dependability of components depend heavily on dimensional accuracy, especially in sectors like heavy machinery, automotive, and aerospace, which demand high precision. The research employs predictive modeling using machine learning algorithms and optimization techniques to determine the optimal contact area that minimizes deviations in final dimensions. The process parameters were modeled, predicted, and enhanced by the use of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). To design experiments, the central composite design is applied with machining trials conducted on a CNC machine. The results show that optimizing the contact area significantly improves stress distribution, reduces deformation risks, and enhances overall dimensional accuracy. Key findings include the validation of a quadratic model for predicting the area of contact, which demonstrated high statistical significance and predictive accuracy. The ANN model further corroborated these results, showing strong correlation and minimal error in predictions. The study offers a strong framework that producers can use to process thick materials more precisely and effectively by leveraging advanced modeling and optimization techniques.

Design of experiments using box behnken design in the development, characterization, mathematical modeling, and evaluation of lung targeted nebulized antiviral camostat mesylate loaded pegylated nanosuspension product

There is in-depth understanding of the effects and interactions of various process parameters on the mechanical properties and dimensional accuracy of parts produced through fused filament fabrication (FFF). Surprisingly, local cooling in FFF has been largely overlooked and is only rudimentarily implemented. It is, however, a decisive element of the thermal conditions governing the FFF process and of particular importance when processing high-temperature polymers such as polyether ether ketone (PEEK). This study, therefore, proposes an innovative local cooling strategy, which allows for feature-specific local cooling (FLoC). This is enabled by a newly developed hardware in combination with a G-code postprocessing script. The system was implemented on a commercially available FFF printer and its potential was demonstrated by addressing typical drawbacks of the FFF process. Specifically, with FLoC, the conflicting requirements for optimal tensile strength versus optimal dimensional accuracy could be balanced. Indeed, feature-specific (i.e., perimeter vs. infill) control of thermal conditions resulted in a significant increase in ultimate tensile strength and in strain at failure in upright printed PEEK tensile bars compared with those manufactured with constant local cooling-without sacrificing the dimensional accuracy. Furthermore, to improve the surface quality of downward-facing structures the controlled introduction of predetermined breaking points at feature-specific part/support interfaces was demonstrated. The findings of this study prove the importance and capabilities of the new advanced local cooling system in high-temperature FFF and provide further directions on the process development of FFF in general.

Additive manufacturing (AM) of high-strength Al alloys promises to enhance the performance of critical components related to various aerospace and automotive applications. The key advantage of AM is its ability to generate lightweight, robust, and complex shapes. However, the characteristics of the as-built parts may represent an obstacle to the satisfaction of the parts’ quality requirements. The current study investigates the influence of selective laser melting (SLM) process parameters on the quality of parts fabricated from different Al alloys. A design of experiment (DOE) was used to analyze relative density, porosity, surface roughness, and dimensional accuracy according to the interaction effect between the SLM process parameters. The results show a range of energy densities and SLM process parameters for AlSi10Mg and Al6061 alloys needed to achieve “optimum” values for each performance characteristic. A process map was developed for each material by combining the optimized range of SLM process parameters for each characteristic to ensure good quality of the as-built parts. This study is also aimed at reducing the amount of post-processing needed according to the optimal processing window detected.

Friction stir processing is one of the solid-state processes which can be used to modify the structure and properties of alloys. In addition, it has become one of the most promising techniques for the preparation of the surface layer composites. To pursue cost savings and a time-efficient design, the mathematical model and optimization of the process can represent a valid choice for engineers. Friction stir processing was employed to generate an Al 6061/Al2O3-TiB2 hybrid composite layer, and mechanical properties such as the hardness and wear behavior were also measured. The relationship between the hardness and wear behavior, process parameters of friction stir processing were evaluated using an artificial neural network and response surface methodology. The rotational speed (1500–1800 rpm), traverse speeds (25, 50, 100 mm/min), and the number of passes (1–4) with constant axial force (2.61 kN) were used as the input, while the hardness and weight loss values were the output. Experimentally, the results showed that the process parameters have significant effect on hardness and wear behavior of Al 6061/Al2O3-TiB2. In addition, the developed artificial neural network and response surface methodology models can be employed as alternative methods to compute the hardness and weight loss for given process parameters. The results of both models showed that the estimated values for the hardness and wear behavior of the processed zone had an error less than 0.60%, which indicated reliability, and an evaluation of the estimated values of both models and the experimental values confirmed that the artificial neural network is a better model than response surface methodology.

PurposeAn experimental study has been conducted to model and optimize wire electric discharge machining (WEDM) process parameters such as pulse-on time, pulse-off time, servo voltage and peak current for response characteristics during machining of Nimonic 75 alloy.Design/methodology/approachThe response surface methodology (RSM)-based Box–Behnken's design has been employed for experimental investigation. RSM is used for developing quadratic regression models for selected response variables i.e. material removal efficiency and kerf width. To validate the model, confirmation experiments have been performed. The multi-response optimization has been done using desirability function approach.FindingsThrough analysis of variation, the percent contribution of process parameters on the response characteristics has been found. Pulse-off time is the most significant parameter affecting the kerf width and material removal efficiency followed by pulse-on time. The quadratic regression models have been developed for prediction of selected response variables. An attempt has been made to optimize the WEDM parameters for material removal efficiency and kerf width. The recommended process parameter setting for maximum material removal efficiency and minimum kerf width have been found to be pulse-on time = 0.6 µs, pulse-off time = 14 µs, servo voltage = 25 V and peak current = 200 A.Originality/valueThe “kerf width” is an important response variable for maintaining dimensional accuracy of the machined component, but has not been given due attention by the researchers. In the present work, the developed regression model for “kerf width” can be used in estimating wire offset setting and thereby getting a dimensionally accurate product. The optimum process parameters obtained in WEDM of Nimonic 75 alloy will contribute in database of machining. The outcome of this study would be added to scare database of the machining of Nimonic 75 alloy and also would be extremely useful for making the technology charts for WEDM.

This study conducts the simulation on optimisation of injection moulding process parameters using Autodesk Moldflow Insight (AMI) software. This study has applied some process parameters which are melt temperature, mould temperature, packing pressure, and cooling time in order to analyse the warpage value of the part. Besides, a part has been selected to be studied which made of Polypropylene (PP). The combination of the process parameters is analysed using Analysis of Variance (ANOVA) and the optimised value is obtained using Response Surface Methodology (RSM). The RSM as well as Genetic Algorithm are applied in Design Expert software in order to minimise the warpage value. The outcome of this study shows that the warpage value improved by using RSM and GA.

Objective: The work aims at prediction of the peak temperature using Response Surface Methodology. Methods/ Statistical Analysis: Dissimilar Aluminum Alloys AA2024 and AA5083 O were friction stir welded. A fifteen run 3 factorial 3 level Box Behnken design was conducted and temperature generated were measured at both sides of the alloys. Thermocouples were employed for the same. The temperature was recorded and studies conducted. Response surface plots were used to obtain the peak temperature and effect of various parameters. Findings: The relationship between the process parameters and the temperature were established using ANNOVA. Mathematical models were development which can apply for future predictions. The traverse speed, rotational speed and the tool profile contributed to the temperature generation. The peak temperature was obtained at 1200 rpm, 25 m/min and threaded cylindrical tool pin profile. Application/Improvements: The temperature at the re-treating side was recorded to be higher compared to the advancing side. Keywords: Anova, Heat Input, Mathematical Modeling. Peak Temperature, RSM

Metal Injection Moulding (MIM) is an effective near-net-shape process for producing small, intricate parts in high volumes. Many optimizations of the MIM process parameter have been published without using any of the statistical based Design of Experiment (DOE) methodology and ends with unsatisfactory results in a wide range of experimental settings. This paper focuses on analysing the optimization of injection moulding process parameters for titanium alloy Ti-6Al-4V green parts using response surface methodology (RSM) to determine significant parameters. The feedstock is prepared by mixing Ti-6Al-4V powder with palm stearin (PS) and polyethylene (PE) binders in three different powder loadings: 63 vol%, 65 vol%, and 67 vol%. RSM is then used to create a Design of Experiment (DOE) to optimize parameters, including A: injection pressure, B: injection temperature, C: powder loading, D: mould temperature, and E: holding pressure. Analysis of variance (ANOVA) was performed to identify the significant parameters influencing defects in Ti-6Al-4V green parts. The study successfully identified the optimal conditions for producing Ti-6Al-4V components using PS and PE binders: A: 443.139 bars, B: 142.11 ºC, C: 65.38 vol.%, D: 44.694 ºC, and E: 629.561 bar. Scanning electron microscopy (SEM) from optimised of green part shows that the primary backbone polymer PE holds the green part and generates wetting for the feedstock and will ease moulding and shaping while the secondary backbone PS will act as a soft polymer that can provide the moisture to the feedstock.

Hybrid-Electrochemical Magnetorheological (H-ECMR) Finishing process is an advanced surface finishing technique that produces surface roughness (Ra) up to a few nanometers required for the implants. In this study, duplex stainless steel (DSS), a biomaterial constituting ferrite and austenite in equal proportion, is considered as the workpiece. DSS is widely used for its high corrosion resistance compared with other grades of stainless steel biomaterials. Furthermore, optimization of the process parameters for the trochoidal, a high-speed finishing toolpath, is carried out to achieve optimum surface roughness parameters during H-ECMR finishing. The surface roughness parameters (i.e., average Surface roughness (Ra). Kurtosis (Rku), and Skewness (Rsk)) are attained from the integration of the Design of Experiment (DOE), Response Surface Methodology (RSM), and Machine Learning Genetic Algorithm (ML-GA). DOE is utilized to plan the experiments based on Central Composite Design (CCD) using input process parameters of the H-ECMR finishing process. The regression equation is developed with the RSM, and its optimization study is performed with ML-GA. This paper aims to analyze the impact of the process parameters for the H-ECMR finishing process on different surface roughness parameters.

In this study, a statistical methodology using design of experiments (DoE) and the response surface methodology (RSM) is employed to optimize the electromagnetic forming (EMF) process of sheet metals with a flat spiral coil according to desirable constraints by choosing optimal process parameters and predicting the electrical and mechanical responses. Regression equations were used to carry out the optimization criterion considering the conditions for the maximum workpiece height and rise time of discharge current, and the other case included the condition for the maximum peak of discharge current. These optimizations were later validated through experiments that were in agreement with the mathematical model. Regarding the maximization of workpiece height, results showed that the windings number was the most significant parameter, followed by the capacitance and energy. Finally, process design principles for the free bulging of metal sheets by the EMF process are outlined based on experimental and statistical methods. These results can contribute to spreading industrial applications of the EMF process by introducing statistical approaches for target-oriented optimization.

Predictive modeling and statistical analysis of mechanical properties OF heat treated Cu-10 %Ni alloy using response surface methodology