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Production of quality castings results from proper control of process parameters involved in casting. This study shows that the values of the selected green sand mold process parameters for an individual mold can be established by modeling and characterizing process parameters. Thus, proving the uniqueness of individual mold. This paper considers sand and melting parameters, which are traditionally assumed to be constant and the same over all the molds. However, this investigation has shown that the parameters are not constant but dynamic and change over time. The investigation establishes that no two molds are the same due to differences in their mold and melting parameters. The time series method has been incorporated into this study. Systematic predictive changes of the selected process parameters were observed, which can be utilized effectively for individual molds. Process parameters were recorded. A variation was found in the values of each parameter in different molds. Values can be obtained by regression analysis. Thus, this paper determines mold health by exclusively predicting the process parameter values of individual molds, which can be effectively correlated with the generation of casting defects.

Energy Consumption (EC) in the process of mechanical manufacturing directly leads to environmental pollution and resource waste. However, the EC characteristics of machine tool processing are complex, and most energy-saving optimization models require accurate material performance data and cutting force models. In response to the above issues, the study first analyzes the structural composition of the machining system, clarifies the main variable parameters for optimization, and then establishes a mathematical model with the determined optimization variables to describe the EC characteristics. Finally, the established optimization model is solved using the adaptive particle swarm algorithm to find the optimal combination of process parameters and achieve energy-saving optimization. The improved adaptive particle swarm intelligence algorithm tends to converge after more than 50 iterations. When taking low cost and low EC as the optimization goal, the cutting EC of the optimization solution is 3.49 × 10⁵ J, the processing time is 42.68 s, and the processing cost is 46.71 points, and the processing cost and EC are between the single optimization goal of low cost and low EC. It is indicated that the proposed method provides a reasonable energy-saving optimization strategy for machining process parameters, and provides support for the implementation of energy-saving optimization of machining center process parameters.

Hydrogenated amorphous silicon (a-Si:H) thin films were prepared by radio frequency (RF) plasma enhanced chemical vapor deposition (RF-PECVD) technique with silane (SiH${}_4)$ as reactive gas. The influence of process parameters on the morphological characteristics and optical properties of a-Si:H thin films were systematically investigated. When the RF power density was taken as the only variable, it firstly improves the smoothness of the surface with increasing the RF power density below the value of 0.17 W/cm², and then exhibits an obvious degradation at further power density. The refractive index, extinction coefficient, optical energy gap initially increase and reach a maximum at 0.17 W/cm², followed by a significant decrease with further RF power density. When the RF power density was taken as the only variable, the surface of a-Si:H thin films become smoother by increasing the reaction pressure in the investigated range (from 50 Pa to 140 Pa), and the refractive index, extinction coefficient, optical energy gap increase with increasing of reaction pressure. The effect of RF power density and the reaction pressure on the morphological characteristics and optical properties of a-Si:H thin films was obtained, contributing to the further studies of the performance and applications of a-Si:H thin films.

In order to detect weld defects in laser welding T-joint of Al–Li alloy, a real-time X-ray image system is set up for quality inspection. Experiments on real-time radiography procedure of the weldment are conducted by using this system. Twin fillet welding seam radiographic arrangement is designed according to the structural characteristics of the weldment. The critical parameters including magnification times, focal length, tube current and tube voltage are studied to acquire high quality weld images. Through the theoretical and data analysis, optimum parameters are settled and expected digital images are captured, which is conductive to automatic defect detection.

Various typical engineering components fail from surface under aggressive conditions like oxidation and hot corrosion. This paper is focused on the responsible failure mechanism of oxidation and hot corrosion. The surface properties like corrosion resistance can be enhanced by introducing a layer of Ni-based materials by using thermal coating techniques. The coatings developed by using processes like high velocity oxy-fuel, plasma spray and cold spray exhibits some surface defects like porosity, surface roughness and un-melted particles. Such defects can be further minimized by using optimization of process parameters and various heat treatment processes. The current study is restricted to the analysis of Ni-based coatings developed using high velocity oxy-fuel, plasma spray and cold spray process. In this paper, the optimization of various process parameters along with heat treatments has been discussed in regard to the tailoring of microstructure and the mechanical properties of the developed coatings.

Electrolysis is a method used for producing copper (Cu) nanoparticles at faster rate and at low cost in ambient conditions. The property of Cu nanoparticles prepared by electrolysis depends on their process parameters. The influence of selected process parameters such as copper sulfate (CuSo₄) concentration, electrode gap and electrode potential difference on particle size was investigated. To optimize these parameters response surface methodology (RSM) was used. Cu nanoparticles prepared by electrolysis were characterized by using X-ray diffraction (XRD) and scanning electron microscope (SEM). After reviewing the results of analysis of variance (ANOVA), mathematical equation was created and optimized parameters for producing Cu nanoparticles were determined. The results confirm that the average size of Cu particle at the optimum condition was found to be 17nm and they are hexagonal in shape.

We present a new stretch forming method for axisymmetric sheet metal parts production. A test bench is designed, fabricated, and mounted on a drilling machine. The blank is fixed rigidly around its periphery in the apparatus, and the forming tool is mounted on the machine spindle. Thus, the control of tool rotational speed, feed rate, and vertical motion from the upper sheet surface becomes possible. Experimental tests are then conducted to form a reference shape. Rotational speed, feed rate, lubrication effect on the final thickness distribution, and geometric profiles are studied. A new multi-pass forming method is also developed and verified. The feed rate decrease, within the range of 0.11–0.18mm/rev, reduces the sheet thinning, while its increase improves the dimensional accuracy. The rotational speed increase, within the range of 112–710rpm, reduces the thickness, while its decrease enhances the geometrical accuracy. Mineral oil seems to be more effective than greases, so the thickness drop is reduced and the dimensional accuracy is improved. The use of the multi-pass strategy avoids the sheet cracking at great forming depth. The decrease in step size, within the range of 0.3–1mm, reduces the thickness minima, while good dimensional accuracy can be obtained in the range of 0.5–1mm.

Surface roughness prediction based solely on cutting parameters provides a quantified value regardless of tool condition, making it suitable for initial parameter selection. However, to achieve accurate surface roughness prediction, the current tool condition must be incorporated into the model. This can be accomplished by utilizing vibration or cutting force signals. In this study, we develop a hybrid response surface methodology-artificial neural network (RSM-ANN) model for predicting surface roughness by combining cutting parameters and vibration data. Four different RSM models were developed, and the best-performing model was selected as input for the hybrid RSM-ANN model. A comparison was made between the hybrid model, a basic ANN model with four inputs (three cutting parameters and one mean vibration in the $Z$-direction), and other ANN models with all possible combinations of these four variables. The hybrid model demonstrated the highest accuracy with the mean square error of 0.00332 with the highest coefficient of regression value of 0.99802 when compared to the other models.

Developing anisotropic particles of different shapes has been a hot topic of research since decades as they possess special features not possible to achieve through other means. It is considered challenging to control atoms for developing their particles of certain size and shape. In this study, different shapes of gold particles were developed while employing a pulse-based electron–photon–solution interface process. Gold atoms, when they are in certain transition state, develop their monolayer assembly around the light glow known in argon plasma generating at the bottom of copper capillary known in cathode. The rate of uplifted gold atoms to develop monolayer assembly at solution surface is controlled by electron streams and traveling photons of high-density entering the solution. Gold atoms dissociated from the precursor on transforming photons (propagating through immersed graphite rod known in anode) to heat energy. Double-packets of nanoshape energy are generated under tuned pulse protocol when they are placed over the compact monolayer assembly resulting in tiny-sized particles of own shape. On the separation of joined tiny particles into two equilateral triangular-shaped tiny particles, each atom of their one-dimensional array elongated on both sides from the centre while exerting opposite poles forces of surface format. This results in convertion of each array of atoms into a structure of smooth elements. Due to immersing force of solution surface and their termination at the centre of light glow, tiny-shaped particles pack from different zones where their structures of smooth elements assemble to develop monolayers of developing particle in a certain shape. Developing particles of one-dimension undertake assembling of structures of smooth elements where packing of their tiny-shaped particles is from the near regions belonging to rearward sides of north–south poles at the solution surface, whereas, developing particles of multi-dimension undertake assembling of structures of smooth elements where packing of their tiny-shaped particles is from the regions of east–west poles and near regions of east–west poles on the solution surface. Depending on the number and orientation of assembled structures of smooth elements nucleating monolayers for different particles, their different anisotropic shapes develop. At fixed precursor concentration, increasing the process time results in developing particles of low aspect ratio. Under tuned parameters, particles of unprecedented features developed.

4D printing technology endows printed samples with self-driven performance that increasingly show strong application prospects. Polyurethane, as a typical shape memory polymer, is widely used in 4D printing. Current researches on 4D printed polyurethane materials are focused on investigating polyurethane composites or novel printing techniques to optimize the shape memory properties of the printed samples. In this study, the effects of pre-programmed 4D printing process parameters on the shape memory properties of polyurethane were systematically investigated. The higher printing speed, higher printing temperature, and lower fill rate result in faster response time of the biomimetic samples with thermal stimulation. Based on the programming of process parameters (e.g., printing temperature, printing speed and filling rate), the biomimetic flowers and hands were processed to achieve a controlled behavior of sequential deformation. It was successfully achieved that only one printing material could demonstrate the shape memory effects with a sequential response process. The adjustable sequential deformations further enlighten the application of 4D printing technology in specific engineering fields such as aerospace, biomedicine, robot and military engineering, where parts must undergo a sequence of deformations to serve practical requirements.

Algae have been considered as a promising biofuel feedstock, which can be converted to the precursor chemicals of drop-in fuels. Due to the high protein content in algal species and the limitations of conversion technologies, these biofuel precursors require further catalytic removal of heteroatoms such as nitrogen and oxygen, being upgraded to biofuels like green diesel and aviation fuel. This chapter reviews the state-of-the-art in hydroprocessing of microalgae-based biofuels, as well as the catalyst development, the effect of process parameters on hydrotreated algal fuels, and the standards suitable for characterization of algal biofuels. Hydroprocessing of algal fuels is a new and challenging task and still underdeveloped. For the long term, an ideal catalyst for this process should possess following characteristics: high activities towards deoxygenation and denitrogenation, strong resistance to poisons, minimized leaching problems and coke formation, and an economically sound preparation process.

The elongation of aluminum alloy 7075 at room temperature is extremely low. To overcome the defects in cold forming methods, aluminum hot stamping process was proposed. This process can effectively improve the stamping formability of aluminum. In this paper, heat treatment experiments and deep drawing tests were used to study the formability of aluminum alloy 7075 in hot stamping process. In the deep drawing tests, the effect of hot stamping process parameters on formability were also studied, which can be used to improve hot stamping formability and reduce forming defects.

In the process of injection molding will produce residual stress. It can seriously affect the product's shape, surface quality and usability. In this paper, on the basis of the source of the residual stress produced, by using MOLDFLOW software, with the aid of orthogonal optimization method, optimize the injection molding process parameters, to determine the combination of optimal parameters, and significant influence on residual stress were obtained through the analysis of variance of parameters. So as to achieve the aim of improving product quality, this has a certain meaning for actual production application.

The model of mouse base was taken as the object based on the production, Moldflow software and orthogonal experimental. The warpage and volume shrinkage in the injection molding were treated as quality objective, to study the effects of process parameters on part's quality. Two optimum parameter combination for two quality objective were obtained by analysis of range. The process parameter combination giving attention to the two quality objectives was selected by integral balance method and the optimized process parameter was verified by simulation.