mohammadjafar hadad

In the context of the industrial grinding process, the quality of products is often assessed by the final surface roughness, which is influenced by various parameters in the industrial environment. Previous studies lacked a feasible formulation based on a kinematical and statistical model to explain the uncertainty and non-linearity of grinding conditions, particularly concerning the cooling method, leading to significant discrepancies between the formulated and real results. This study introduces a novel strategy that combines deep learning and optimization to establish a suitable framework. It employs an artificial neural network to simulate and predict surface roughness, considering various dressing and cooling parameters in the industrial grinding of St37 steel alloy. Initially, an analysis of variance (ANOVA) is conducted to determine the correlation between input and output data. Subsequently, a neural network approach with one and two hidden layers, incorporating various activation functions, is employed. Therefore controlling and improving the accuracy of surface roughness predictions in industrial grinding processes can be automated. The mean squared error (MSE) metric is applied to each implementation to identify the best network architecture for the dataset. Upon selecting the network with the lowest MSE, the final algorithm predicts a set of randomly selected data from the dataset, achieving an overall accuracy of 80%. When compared to the accuracy of the formulated implementation, the neural network approach demonstrates a significantly higher accuracy of up to 30%, surpassing conventional analytical formulation in predicting final surface roughness. These results underscore the considerable potential and feasibility of deep learning approaches for industrial applications.

In this experimental work, the effects of cutting fluid and different cutting parameters on surface roughness, tool wear, and chip morphology during turning of Al7075-T6 were investigated. Machining experiments have been done in different environments such as dry, wet, minimum quantity lubrication (MQL) as well as a homemade ultrasonic nozzle- minimum quantity lubrication (UN-MQL). Ultrasonic vibrations can be used to effectively atomize the cutting fluid into fine and uniform-sized droplets and smaller spray angles with a larger spray deposition distance. The MQL system and machining parameters were evaluated by the design of experiments (DOE) method which allows us to carry out the optimization analysis by performing a relatively small number of experiments while determining the influence on the machining performance using the analysis of variance technique. In the second step, an optimization of the machining parameters was sought using signal-to-noise ratio analysis. Therefore, the response surface methodology was determined in a regression analysis, which was used to model the influence of the parameters on the performance. The fine droplets produced by the UN-MQL system penetrate effectively into the machining zone. Finally, the chip morphology, tool wear and surfaces of the machined parts were examined using optical microscopy to identify the chip formation mechanism in different machining conditions. Cutting tool wear were also examined using the SEM tests to quantify the tool wear zones under specific process parameters. Experimental results show that applying UN-MQL system reduces the surface roughness and tool wear by up to 30% in comparison to the conventional MQL system.

The production of nanostructure materials or ultrafine grain (UFG) has been noticed by most of research society due to high strength, wear resistance, formability and high plastic strain rate. These features result from microstructure materials (100-300 nm) and unique defect (grain boundary-dislocation) make these material ideal for medical implant and structured components of aerospace and energy systems. The ways of producing UFG for these advanced engineering projects have not been considered yet. Due to the fact that nanostructured materials can show a good mechanical strength, researchers are using different ways to change pure copper into nanostructure one. One of these methods is applying process in equal channel angular pressing (ECAP), which coarse grain copper changed to nanostructure one. In this study, machinability of UFG as well as coarse grain (CG) copper is really considered in turning. To evaluate the machinability, cutting force, tool wear, chip morphology and surface roughness have been studied. Experimental results confirmed that UFG copper can be machined more efficiently than CG copper. In other words, the amount of BUE is reduced during turning ECAP copper due to the hardening of the pure sample. In comparison to CG copper, cutting force and surface roughness for UFG copper were less. As a result, machining performance can be improved partly by cold-work applying ECAP process.

Nowadays, steel hardening has received much attention from researchers due to its frequent use in industries, especially is widely used in energy equipment, aerospace, and petrochemical industries. Low capability in chip removal of hardened steel has always been a significant machining issue. Mounted point grinding is a machining method to improve surface finish and remove burrs on the workpiece walls and hard-to-reach areas. This process is usually used without preparing the grinding wheel before and during the grinding operation, which reduces the proper performance of the process. Environmental contamination, surface integrity, coolant-lubricant-related diseases that affect workers' health, and machining costs heavily depend on the appropriate dressing and proper coolant-lubricant usage. In this study, the effect of dressing conditions (depth of dressing and dressing feed) and the workpiece feed rate during the mounted point grinding of a Mo40 hardened steel in two traditional wet and Minimum Quantity Lubrication (MQL) environments has been investigated. Surface roughness and wheel loading are two significant outputs in every grinding operation. The experimental result of this study reveals an improvement in enhancing the surface roughness in a soft dressing condition. Moreover, this study aimed to achieve proper surface roughness by implementing MQL technique to significantly reduce total cutting fluid usage compared to traditional wet machining. This study observed a higher wheel loading in MQL technique than in the conventional wet grinding.

The present study investigated the feasibility of producing strong and defect-free joints between 1050 aluminum and pure copper sheets by micro friction stir welding using ordinary and new tools. By analyzing the results of the tensile test and signal to noise analysis, the average maximum ultimate tensile strength of the joints is reported to be 88 MPa, when using the parameters of rotational speed, traverse speed, and offset of the tool with levels of 2400 rpm, 40 mm/min and 0.25 mm. The variance analysis also showed that the parameters of rotational speed and traverse speed had the most significant effect on the joints' tensile strength. The maximum and minimum values obtained from the microhardness test were recorded for the weld nugget zone and the heat-affected aluminum zone, respectively, equal to 192 HV and 21 HV. The X-ray diffraction test results on optimal samples showed intermetallic compounds such as CuAl2 and Cu9Al4 in the welding area. The tensile strength of the joints created is strongly dependent on the formation of these intermetallic compounds.

Hot machining is a type of cutting operation that an external heat source is used to pre-heat and consequently reduce the yield strength of the workpiece material. In this study, the conventional and hot turning of AISI630 hardened stainless steel, which is widely used in energy equipment, aerospace, and petrochemical industries, have been evaluated in both numerical and experimental methods. Simulation of the turning process is carried out by finite elements method (FEM) using AdvantEdge software. To predict chip morphology and cutting forces, the 2D and 3D FEM analyses have been used, respectively. The numerical analysis showed that hot turning in 300°C causes a reduction of 28% in cutting forces and consequently decreases stressed on the cutting tool. It is found that the main factor affecting the fluctuations of the cutting forces in turning of hardened AISI630 is the saw-tooth formation phenomenon (chip segmentation) as well as the shear band generation due to thermal softening of the workpiece material. Furthermore, the relation between cutting force fluctuation and the machined surface roughness has been investigated applying numerical analysis and experimental data. The results of roughness measurement revealed that hot turning in 300°C reduces the machined surface roughness up to 23%. In addition, it has been observed that hot turning technique decreases side flow and surface damages in comparison to conventional turning.

AISI630 is a stainless steel that is strengthened by precipitation-hardening mechanism. This steel has high hardness and low thermal conductivity, has made it one of the difficult-to-cut materials. These two factors have its machining is associated with high tool wear and poor workpiece surface quality. In this study, the conventional and hot turning of AISI630 hardened stainless steel have been investigated. To determine the effect of machining parameters on tool wear, a hot turning process up to a preheating temperature of 400°C was performed. Turning was conducted at three feed rates and three levels of cutting speed using PVD-(Ti,Al)N/(Al,Cr)2O3 coated carbide tools. Tool flank wear and wear mechanisms have been studied in different cutting conditions as well as different preheating temperatures using SEM microscope. Experimental results showed that the lowest wear on the free surface of the tool was obtained by hot turning at 300 °C. Hot turning at this temperature reduced the flank wear by 33%. Observation of the worn surface of the tools showed that the tool wear mechanism in hot turning and conventional turning is of the type of abrasive wear and adhesive wear.Moreover, at each cutting speed and feed, with increasing the workpiece initial temperature up to 400°C, the surface roughness decreases. The optimal values of temperature, cutting speed and feed rate were obtained using Minitab software with the aim of reducing tool wear and surface roughness.

In grinding operation, wheel topography influences the workpiece surface quality, grinding forces, abrasive grain wear and wheel loading. The difficulties associated with grinding nickel-base superalloys are mainly attributed to the high strength and low thermal diffusivity of these materials. Their high strength leads to high removal energy. The application of green machining techniques for sustainable manufacturing becomes more and more attractive nowadays to reduce the consumption of energy and cutting tools and cut fluids and consequently decrease the production costs and environmental effects. In this study, the effect of dressing parameters and wheel topography on Minimum Quantity Lubrication-MQL grinding performance of Nickel-base superalloy-Inconel 738 is investigated. In other words, to generate different grinding wheel topographies, depth of dressing and dressing speed has been changed during dressing and conditioning of vitrified Al2O3 wheels using a single point diamond dresser. After dressing grinding wheels, machining tests have been conducted to study the influence of the wheel topography and coolant-lubricant types on the performance of grinding operation (workpiece surface quality and wheel loading). The tests have been performed in the presence of fluid as well as MQL with compressed air. The results show that applying MQL technique with the optimized dressing conditions improves the grinding performance of Inconel 738. Minimum quantity lubrication implemented in the grinding process is one of the realistic alternatives that can rise the abrasive processes on a sustainable level.

One of the methods to improve and achieve superior properties, is to modify and optimize the Manufacturing Process and to consider the use of nanoparticles as reinforcements in these materials. In this regard, stir casting method is considered as one of the methods of distribution of refractory particles in the melt and three percent by weight of 0.01, 0.05 and 0.1% of graphene nameplates and carbon nanotubes as reinforcements particles added to The primary alloy A356, due to the properties of carbon based nanoparticles. The optimum conditions, including the rotational speed of the graphite mixer, 500 RPM, were obtained for one-minute mixing in a row, at 740⁰C. The results of elemental, phasic and microstructural analysis, confirmed, the distribution correctly of reinforcements nanoparticles in the composite matrix. The tensile test showed, an increase in yield, ultimate and fracture strength, and also strain, so that the maximum increase in strength and strain using 0.1 wt.% graphene, was 28% and 2.6%, respectively. Also, by using 0.1 wt.% carbon nanotube, the nanocomposite hardness increased to 88.4 Vickers, indicating a 33% improvement in the ratio of non-reinforced alloy.

Wheel loading in the grinding process is inevitable due to the chips arrangement separated from the surface of the workpiece in the gap between the grains. The amount of this loading depends on the factors such as workpiece material, grinding parameters, coolant-lubricant conditions and wheel surface topography. Determining the time to remove the chips from the wheel surface is one of the important variables in the grinding process, preventing the chips from sticking to the surface of the wheel and thus increasing the roughness of the surface. There are several methods for analyzing and monitoring the condition of wheel surface. In this study the influence of dressing condition and coolant-lubricant environments on the wheel surface loading has been investigated using image processing in python software. Using the examination of wheel surface images in pre and post grinding process of Inconel 738 under various condition of coolant-lubricant, optimal conditions for grinding wheel surface topography as well as workpiece feed rate are provided to prevent excessive loading.

17-4 ph stainless steel is one of the high strength stainless steel that is widely used in military, oil and gas industries, which has many problems due to its high strength, such as other new and high strength materials face to a lot of problems. Due to the great improvement in material properties especially their strength and hardness in recent years, this has become more evident. The use of controlled heat source along with machining (hot machining) (heat-assisted machining) has been introduced as an efficient way for machining hard-cutting materials. This also significantly improves the process output parameters. Minimum quantity lubrication (MQL) has been used to eliminate other dangers, such as tool wear and high-temperature infiltration. The surface roughness as an important parameter in machining due to its high strength and hardness is one of the challenges of machining this steel. Using this method, surface integrity has improved significantly. In this study, the effect of cutting velocity, feed rate and applied temperature on the surface have been investigated on the surface integrity. The surface roughness has been improved by using a hybrid machining up to 70 to 100%, as well as a uniform surface roughness profile. The hybrid machining has shown that all surface imperfections, such as tricks and microcracks, sticking particles Eliminates at the surface, which can indicate a dramatic improvement in fatigue strength.

One of the most significant issues of recent decades is pollution and dangers that may threat the environment. Different approaches were presented to protect the environment and target various sources of pollution. Old vehicles are one of the major sources of pollution in megacities as they consume and emit a lot of emissions. Therefore governments in different countries try to levy tax on pollution to motivate people to drive environment friendly and more efficient vehicles. Tehran is one of the cities suffering rigorously from poor air quality. As a result, approximately 44 days in each year the air quality reckons as unhealthy for all residents. One of the suggested solutions is replacing conventional taxis across the city with hybrid electric vehicles. In this article this solution for the city of Tehran, Iran will be discussed and its feasibility will be evaluated using life cycle assessment.   In order to conduct this, first data associated with air quality, pollution and taxis distribution in the city were presented. Then different designated vehicles were evaluated based on their technical performance and the emission they generate in different stages. Using the proposed model a comprehensive cost is defined and different vehicles were compared and the most viable choices by various considerations is introduced.

The use of a minimum quantity of lubrication (MQL) with extremely low consumption of lubricant in machining processes has been reported as a technologically and environmentally feasible alternative to conventional flood cooling. In hot machining, the external heat source is applied during machining that will assist to increase machining performance. Many external heating techniques are available and each type has advantages/disadvantages. 17-4 PH stainless steel (AISI630) is martensitic stainless steel, which is widely used in energy equipment, aerospace and petrochemical industries. The objective of the present paper is to integrate MQL technique, for the first time, with a hot turning process for finding an optimum possible hybrid technique for a particular machining process. The effects of different machining parameters on MQL turning of 17-4 PH stainless steel have been investigated in comparison with dry and wet machining processes. Experiments were also designed for machining using MQL and dry techniques to evaluate surface roughness, tool wear, machined surface morphology, chip morphology as well as chip formation mechanism under different pre-heating temperatures. The results show that applying MQL technique with online thermally enhanced turning (MQL-hot turning) increases the efficiency of machining of 17-4 PH stainless steel. The cutting parameters and pre-heating temperature are important parameters and should be selected carefully when using hybrid MQL-hot turning. In addition, machining with MQL is beneficial to the environment and machine tool operator health as lubricant consumption during operation with MQL is 7-fold lower than in the conventional system.