Aluminum 1100 is a commercially pure aluminum alloy with properties suitable for applications requiring ductility and workability. It is soft, weldable, and corrosion-resistant. This study attempts to determine the influence of machining on high-speed turning operations. The experiment is designed using the Design of Experiments of Response Surface Methodology, using input parameters such as cutting speed, feed rate, and cutting depth, to estimate surface roughness, temperature, and machining time of aluminum1100 as the workpiece material, with a carbide tool used for operation. The Analysis of Variance technique has been used to test the material's performance. In contrast, the Design Expert software has been used to study the impact of cutting parameters on the workpiece. A Backpropagation ANN model is developed in MATLAB to optimize cutting parameters and reduce Ra, T, and Tm values. The ANN indicates that the lowest expected value is in this case. The Multi-Objective Genetic Algorithms are employed to forecast turning parameters, and it is observed that, for an input parameter grouping of 16 Pareto-optimal solution sets, the ideal Ra ranges from 1.37 to 1.62 µm, and the temperature ranges from 34.10 to 34.08 °C. The machining time ranges from 1.27 to 1.34 min. Among all, cutting speed has the greatest influence on the parameter. The confirmatory analysis shows that the experimental and predicted values differ by less than ±2% and agree admirably with the experimental values.

In the present work, modeling of hoop stress in the defect-free structural steel pipe was done under the combined effect of internal hydrostatic pressure and temperature difference using finite element analysis (FEA) and response surface methodology (RSM). The FEA simulation was done on the quarter-ellipsoidal sections of the structural steel pipe specimen using ANSYS 2022 R1 software. The calculated hoop stress using FEA was in agreement with the analytical solution of hoop stress. The thermodynamically induced hoop stress due to the temperature difference (without internal hydrostatic pressure) exhibited a compressive state at the inner wall and a tensile state at the outer wall of the specimen. This compression-tension state also provided a thermodynamic situation at which the total hoop stress becomes null at the neutral axis. However, when the specimen was subjected to internal hydrostatic pressure (in addition to the temperature difference), the initial neutral axis received a thermomechanical hoop stress of a tensile nature. A drop in the burst pressure from 11.5 MPa to 9.9 MPa was observed when the steel pipe was subjected to a maximum temperature difference of 40 °C. A new analytical equation for thermomechanical hoop stress was developed using RSM modeling by considering independent variables as the normalized position in the wall (ṝ), internal hydrostatic pressure (P), and temperature difference (ΔT). The developed analytical equation envisages that the interacting effect of independent variables ṝ(ΔT) was maximum, followed by the interacting effect of ṝ(P). An optimum internal hydrostatic pressure of 7.43 MPa was calculated considering the flow stress of the material for all possible combinations of the other two independent variables (ṝ and ΔT). Furthermore, at the optimum ΔT of 39.65 °C, the interacting effect of the ṝ(P) provided contour-curvature plots, both below the yield strength and endurance limit, considering different combinations of normalized position in the wall and internal hydrostatic pressure.

The poor maintenance and usage of the equipment and machines of a vegetable oil manufacturing company adversely affect its competitive advantage. These industries are faced with numerous equipment maintenance challenges in the path to increasing their throughput as well as profitability. To address the said maintenance challenges, process data were obtained for the Overall Equipment Effectiveness (OEE) factors after their Total Productive Maintenance (TPM) implementation in the company. Minitab 21 software was used to analyze the data collected, and the results showed that the mean for quality, availability, and performance obtained were 96.929%, 63.35%, and 61.20%, respectively. This shows that the quality of products is the greatest OEE factor that vegetable oil manufacturing companies must consider meticulously to reduce the six big losses in their production processes. Response Surface Method (RSM) with Central Composite design, with the application of Design Expert 13 software, was used to model, analyze, and optimize the Overall Equipment Effectiveness (OEE) using availability, quality, and performance as the input parameters. The analysis of both the actual and coded values, which is the main contribution of the study, showed that quality has the greatest value followed by availability and performance. It was found that, to effectively reduce the six big losses, the quality, performance, and availability should be targeted as 98.3052%, 81.6022%, and 80.103%, respectively.

In the current paper, the authors are intended to manufacture the aluminum based metal matrix composite (MMC) employing the stir casting process. Further, the fabricated composite sample is investigated for machining characteristics during the die sink electrical discharge machining process (EDM). EDM is most commonly employed to satisfy the special needs of industry such as developing deep holes and complex contours from high strength materials such as composites, alloys, smart materials, and functionally graded materials. In the current study A356 and 4%, tungsten carbide (WC) powder are considered as matrix and strengthening materials respectively to fabricate the MMCs. During the machining activity, the input factors like discharge current (Ip), Voltage (Vg), Pulse On-Time (Ton), and flushing pressure (P) are optimized for achieving optimum surface roughness (SR), Tool Wear Rate (TWR) and Material Removal Rate (MRR). To estimate the ideal set of process factors grey regression analysis (GRA) is used. From the results, it was observed that the GRA is found to perform better than the RSM.

Demand for eco-friendly materials increases each year due to their excellent properties, which has proved to contribute to developing a sustainable environment. One of the promising raw materials in producing Glass is rice husk, a waste product from paddy harvesting, containing about 90% of silica. Rice husks are usually burnt in an open area and contribute to severe air pollution problems. In this research, Soda-Lime-Silica Rice Husk Ash (SLRHA) glass which is a new combination of soda-lime silicate (SLS) glass and rice husk ash (RHA), was developed for building glass and window application. The hardness properties of the developed SLS-RHA glass system are presented in this paper. These glasses were investigated to determine the effect of RHA addition on the physical properties of SLS glass. The experimental works using RSM have successfully identified the significant factors and optimized the responses. Based on the Rockwell hardness test, the outcomes demonstrated that the glass sample contained 29.84% weight SLS and 0.06% weight RHA. The result indicated that crack propagation was increased with the increasing addition of RHA, which causes an increase in cracks and voids due to the creation of more debonding.

Designing a production process normally is involved with some important constraints such as uncertainty, trade-off between production costs and quality, customer’s expectations and production tolerances. In this paper, a novel multi-objective robust optimization model is introduced to investigate the best levels of design variables. The primary objective is to minimize the production cost while increasing robustness and performance. The response surface methodology is utilized as a common approximation model to fit the relationship between responses and design variables in the worst-case scenario of uncertainties. The target mean ratio α is applied to ensure the quality of the process by providing the robustness for all types of quality characteristics and with a trade-off between variability and deviance from the ideal point. The Lp metric method is used to integrate all objectives in one overall function. In order to estimate target value of the quality loss by considering production tolerances, the process capability ratio (Cpm) is applied. At the end, a numerical chemical mixture problem is served to show the applicability of the proposed method.

Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this research work has been carried out in order to elucidate surface roughness and productivity mathematical models during turning of Inconel 718 superalloy (35 HRC) with SiC Whisker ceramic tool at various cutting parameters (depth of cut, feed rate, cutting speed and radius nose). A small central composite design (SCCD) including 16 basics runs replicated three times (48 runs), was adopted and graphically evaluated using Fraction of design space (FDS) graph, completed by a statistical analysis of variance (ANOVA). Mathematical models for surface roughness and productivity were developed and normality was improved using the Box-Cox transformation. Results show that surface roughness criterion Ra was mainly influenced by cutting speed, radius nose and feed rate, and that the depth of cut had major effect on productivity. Finally, ranges of optimized cutting conditions were proposed for serial industrial production. Industrial benefit was illustrated in terms of high surface quality accompanied with high productivity. Indeed, results show that the use of optimal cutting condition had an industrial benefit to 46.9 % as an improvement in surface quality Ra and 160.54 % in productivity MRR.

Inconel 825 is high nickel-chromium-based superalloy which retains its mechanical properties and exhibits good corrosion and oxidation resistance at elevated temperature. Inconel 825 is extensively used for making aircraft engine parts like combustor casing and turbine blades in aero space industry. This research proposed the Response Surface Methodology with GRA to optimize multiple responses during Wire-cut EDM of Inconel 825. At optimum combination of input parameters i.e. A4B1C1D5E4F2, increase in MRR from 36.13 mm2/min to 41.822 mm2/min, decrease in SR from 2.842μm to 2.445μm and decrease in WWR from 0.01832 to 0.01758 was obtained. Experimental results showed that pulse-on time, wire feed, pulse-off time, and peak current significantly affected the MRR, and surface integrity of specimen and electrode with the formation of craters, pockmarks, debris, micro cracks, and recast layer. The optimal parametric combination obtained from the present study will be advantageous for working on high strength; high thermal conductivity and low melting point materials like nickel alloys.

Abrasive Water Jet Machining is one of the novel nontraditional cutting processes found diverse applications in machining different kinds of difficult-to-machine materials. Process parameters play an important role in finding the economics of machining process at good quality. This research focused on the predictive models for explaining the functional relationship between input and output parameters of AWJ machining process. No single set of parametric combination of machining variables can suggest the better responses concurrently, due to its conflicting nature. Hence, an approach of Multi-objective has been attempted for the best combination of process parameters by modelling AWJM process using of ANN. It served a set of optimal process parameters to AWJ machining process, which shows a development with an enhanced productivity. Wide set of trail experiments have been considered with a broader range of machining parameters for modelling and, then, for validating. The model is capable of predicting optimized responses.

Laser direct structuring (LDS) is very important step in the MID process and it is a complex process due to different parameters, which influence on this process and its final product. Therefore, it is very important to use a reliable model to predict, analyze and control the performance of the (LDS) process and the quality of the final product. In this work we develop mathematical models by using Artificial Neural Network (ANN) and Response Surface Methodology (RSM) to study this process. The proposed models are used to study the effect of the LDS parameters on the groove dimensions (width and depth), lap dimensions (groove lap width and height) and finally the heat effective zone (interaction width), which are important to determine the line width/space in the MID products and the metallization profile after the metallization step. We also study the relationship between the LDS parameters and the surface roughness which is very important factor for the adhesion strength of MID structures. Moreover these models capable of finding a set of optimum LDS parameters that provide the required micro-channel dimensions with the best or the suitable surface roughness. A set of experimental tests are carried out to validate the developed ANN and the RSM models. It has been found that the predicted values for the proposal ANN and RSM models were closer to the experimental values, and the overall average absolute percentage errors were 4.02 % and 6.52%, respectively. Finally, it has been found that, the developed ANN model could be used to predict the response of the LDS process more accurately than RSM model.