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Cutting tool wear during drilling operations can cause damage to cutting tools, machine tools, and workpieces which should be analyzed and minimized. Cutting tool wear impacts not just tool life but also the quality of the final product in terms of dimensional accuracy and surface integrity. High mechanical and thermal loads are generated during drilling operations of difficult-to-cut materials such as Ti-6Al-4V alloy which can reduce the life of cutting tool during chip formation process. Thus, to increase the accuracy of drilled parts from titanium alloy Ti6-Al-4V, the cutting tool wear during drilling operations should be accurately predicted in order to be minimized. Application of virtual machining systems is developed in the study in order to predict and minimize the cutting tool wear during drilling operations of titanium alloy Ti-6Al-4V. To predict the tool wear during drilling operations, cutting forces and temperature are calculated. Then, the finite element method (FEM) is utilized to predict the tool wear using the analytical model of Takeyama–Murata and updating the cutting tool geometry during chip formation process. To minimize the cutting tool wear during drilling operations, the optimum drilling parameters of feed rate and spindle speed are obtained using the Taguchi method-based response surface analysis algorithm. As a result, optimized drilling parameters are used in order to minimize the cutting tool wear during drilling operations. To validate the study, the experimental works are implemented to the sample workpiece from titanium alloy Ti6-Al4-V and the values of tool wear are then measured. To present the effectiveness of the proposed virtual machining system in minimization of cutting tool wear, the obtained results with and without optimized machining parameters are evaluated and compared. So, precision and productivity in drilling operations of titanium alloy Ti6-Al4-V can be enhanced using the developed virtual machining system in the study.

Advanced virtual manufacturing systems refer to highly sophisticated computer-based systems which simulate and optimize manufacturing processes in a virtual environment. Recently, many researchers have presented research works in different areas of simulation and analysis of manufacturing process in virtual environments such as cloud manufacturing, virtual training systems, virtual inspection systems, virtual process planning, flexible manufacturing systems, virtual manufacturing networks, virtual monitoring systems, virtual manufacturing for optimized production process, virtual machining systems and virtual commissioning systems. The advantages of virtual manufacturing systems include improving the quality of the produced components, decreasing the quantity of waste materials and accelerate product and process design using virtual simulation and modification. As a result, accuracy as well as efficiency in process of part manufacturing can be increased by applying the virtual environments to manufacturing operations. Moreover, digital marketing by using virtual manufacturing systems can increase the added value in the process of part production. To analyze and modify the processes of part production, recent achievements in virtual manufacturing systems are reviewed and presented in the study. The applications of virtual manufacturing systems in creating manufacturing processes are discussed, and future research works are also proposed. It has been discovered that reviewing and evaluating recent achievements in the published papers can promote the process of manufacturing engineering using virtual simulation and modification.

Composite materials are used to produce multi-objective structures such as fluid reservoirs, transmission pipes, heat exchangers, pressure vessels due to high strength and stiffness to density ratios and improved corrosion resistance. The mathematical concepts can be used to simulate and analyze the generated mechanical and thermal properties of composite materials regarding to the desired performances in actual working conditions. To solve and obtain the exact solution of the developed nonlinear differential equations in the composite materials, analytical methods can be applied. Mechanical and thermal analysis of complex composite structures can be numerically analyzed using the Finite Element Method (FEM) to increase performances of composite structures in different working conditions. To decrease failure rate and increase performances of composite structures under complex loading system, thermal stress and effects of static and dynamic loads on the designed shapes of composite structures can be analytically investigated. The stresses and deformation of the composite materials under the complex applied loads can be calculated by using the FEM method in order to be used in terms of safety enhancement of composite structures. To increase the safety level as well as performances of the composite structures in different working conditions, crack development in elastic composites can be simulated and analyzed. To develop and optimize the process of composite deigning in terms of mechanical as well as thermal properties under different mechanical and thermal loading conditions, the advanced machine learning systems can be applied. A review in recent development of composite materials and structures is presented in the study and future research works are also suggested. Thus, to increase performances of composite materials and structures under complex loading systems, advanced methodology of composite designing and modification procedures can be provided by reviewing and assessing recent achievements in the published papers.

Concrete, ceramics, rocks, metal-matrix composites, titanium alloys, and other hard-to-cut materials are frequently cut using the non-traditional cold processing method known as Abrasive Water Jet Machining (AWJM). A virtual machining system is proposed in the study to minimize roughness of surface and residual stress during AWJM of Titanium Alloy Ti6Al4V. The water and workpiece are simulated in virtual environments using the Smoothed Particle Hydrodynamics (SPH) approach and finite element method respectively. Then, the Johnson Cook law models of titanium alloy is used to obtain the cutting temperature during cutting operations. To calculate residual stress during the AWJM operations, the finite element method is then utilized. The Taguchi optimization method is implemented to minimize surface roughness and residual stress in AWJM operations. Thus, the optimized machining parameters such as water jet pressure, traverse speed, abrasive mass flow rate and standoff distance are calculated to minimize the surface roughness and residual stress during the AWJM of titanium alloy Ti6Al4V. Experimental and computational examinations are carried out in order to validate the effectiveness and efficiency of proposed virtual machining system in minimization of surface roughness and residual stress during abrasive waterjet cutting of titanium alloy Ti6Al4V. As a result, by using the optimized machining parameters, 23.7% and 27.1% reduction in the measured and predicted residual stress of sample machined part are obtained. Also, the sample machined part's surface roughness is reduced by 26.7% and 27.9%, for the measured and predicted surface roughness of sample machined part respectively. So, the quality and reliability of the machined parts can be enhanced using the developed virtual machining system in the study to minimize the surface roughness and residual stress in AWJM operations.

Advancement of Waste Electronics Recycling Through Automation

verviewStatsCommentsCitationsReferencesRelated research (10+) Share More Beta Want to get 4x more reads of your article? Showcase your recent work in a Spotlight to get 4x more reads on average. Learn more Create Spotlight Abstract During chip formation process of machining operations, thermo-mechanical loads are generated which can decrease life of cutting tool and quality of machined components. As a result, analysing the cutting temperatures and cutting tool life during milling operations can enhance productivity in process of part manufacturing using CNC machine tools. To predict the cutting tool life and cutting temperature during machining operations of AISI 1038 Carbon Steel, an application of the virtual machining system is developed. The impact of machining parameters such as cutting speed, feed rate and depth of cut on the cutting tool life and temperature are investigated in order to enhance productivity of milling operations. The modified Johnson-Cook model is used to investigate the combined influence of strain rate and deformation temperature on yield stress during alloy milling operations. Finite element analysis of milling operations is implemented to obtain the cutting temperature of the milling tool during the chip formation process. Then, cutting tool life during milling operations is predicted in order to be analyzed and maximized. The results of virtual machining system in prediction of cutting temperature as well as life of cutting tool are compared with the experimental results in order to validate the developed methodology in the study. So, an advanced virtual machining system is developed in the study in order to decrease cutting temperatures and increase cutting tool life in terms of efficiency enhancement of part production using milling operation.

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To increase accuracy as well as productivity in CNC machine tools, Finite Element Method (FEM) are applied to the machining operations and machine tool structures. As a result, the errors of machining operations can be analyzed and minimized in order to enhance accuracy of machined parts. Also, by analyzing the stress and deformations of the machine tool elements under actual loads, performances as well as working life of the machine tool structures can be enhanced. The study reviews the analysis and modification of CNC machine tool operations and structures using finite element methods from the recent published papers. Applications of the FEM methods in the simulation of cutting temperatures, cutting forces and energy consumption, chip formation, deflection and deformation errors of thin-walled components, and tool wear rate during machining processes are reviewed to demonstrate the capabilities of the FEM simulation in CNC machining modification. Furthermore, applications of the FEM to mod el various machine tool components such as the bed, ball screw, spindle, and guideways are studied in order to enhance the performances of various machine tool elements such as mechanical properties in real-world working situations. In order to fill the gaps between the existing studies and published papers, innovative concepts and approaches of future research works are also suggested. Thus, the research field can be developed by reviewing and analyzing previous achievements in published research works in order to offer innovative concepts and approaches in applications of FEM techniques in modifying machining procedures and CNC machine tool structures.

Advanced virtual manufacturing systems refer to highly sophisticated computer-based systems which simulate and optimize manufacturing processes in a virtual environment. Recently, many researchers have presented research works in different areas of simulation and analysis of manufacturing process in virtual environments such as cloud manufacturing, virtual training systems, virtual inspection systems, virtual process planning, flexible manufacturing systems, virtual manufacturing networks, virtual monitoring systems, virtual manufacturing for optimized production process, virtual machining systems and virtual commissioning systems. The advantages of virtual manufacturing systems include improving the quality of the produced components, decreasing the quantity of waste materials and accelerate product and process design using virtual simulation and modification. As a result, accuracy as well as efficiency in process of part manufacturing can be increased by applying the virtual environments to manufacturing operations. Moreover, digital marketing by using virtual manufacturing systems can increase the added value in the process of part production. To analyze and modify the processes of part production, recent achievements in virtual manufacturing systems are reviewed and presented in the study. The applications of virtual manufacturing systems in creating manufacturing processes are discussed, and future research works are also proposed. It has been discovered that reviewing and evaluating recent achievements in the published papers can promote the process of manufacturing engineering using virtual simulation and modification.

Forming, joining and finishing process during car manufacturing are described. Future research works are suggested.

Share During chip formation process of machining operations, thermo-mechanical loads are generated which can decrease life of cutting tool and quality of machined components. As a result, analysing the cutting temperatures and cutting tool life during milling operations can enhance productivity in process of part manufacturing using CNC machine tools. To predict the cutting tool life and cutting temperature during machining operations of AISI 1038 Carbon Steel, an application of the virtual machining system is developed. The impact of machining parameters such as cutting speed, feed rate and depth of cut on the cutting tool life and temperature are investigated in order to enhance productivity of milling operations. The modified Johnson-Cook model is used to investigate the combined influence of strain rate and deformation temperature on yield stress during alloy milling operations. Finite element analysis of milling operations is implemented to obtain the cutting temperature of the milling tool during the chip formation process. Then, cutting tool life during milling operations is predicted in order to be analyzed and maximized. The results of virtual machining system in prediction of cutting temperature as well as life of cutting tool are compared with the experimental results in order to validate the developed methodology in the study. So, an advanced virtual machining system is developed in the study in order to decrease cutting temperatures and increase cutting tool life in terms of efficiency enhancement of part production using milling operation.

Optimization of energy consumption in industrial robots can reduce operating costs, improve performance and increase the lifespan of the robot during part manufacturing. Choosing energy-efficient components such as motors, drives, and controllers can significantly reduce energy consumption in industrial robots. Over-sized motors and heavy robot arms can waste energy and decrease efficiency of industrial robots. By optimizing the robot programs and reducing idle time in robot operations, the amount of spent time can be reduced to minimize energy consumption of industrial robots. By using energy-efficient motors and drives, the amount of energy consumed by the robot can be reduced. Also, regular maintenance can reduce energy consumption of industrial robots by providing maximum efficiency for the robot's components. By implementing energy management systems, energy consumption of industrial robot can be monitored and analyzed to optimize energy consumption of industrial robot during working conditions. To minimize lost energy and reuse the energy usage during working times, regenerative braking can be used in the robots. The process of part manufacturing can be optimized in order to minimize the robot's movements and energy usage during working times of industrial robots. To analyze and optimize energy consumption in working schedules of industrial robots, different methodologies from recent published papers are reviewed in the study. Proper robot selection, energy-efficient robot motor and low wight robot arms, efficient programming of working schedules, regenerative braking system, regular maintenance of robot elements and optimized process of part production regarding the minimization of energy usage are discussed to optimize the energy consumption in industrial robots. As a result, future research works in the research field can be presented in order to optimize energy consumption, reduce operational costs, and increase sustainability of industrial robot operations in terms of productivity enhancement of part manufacturing.

verviewStatsCommentsCitationsReferencesRelated research (10+) Share More Beta Want to get 4x more reads of your article? Showcase your recent work in a Spotlight to get 4x more reads on average. Learn more Create Spotlight Abstract During chip formation process of machining operations, thermo-mechanical loads are generated which can decrease life of cutting tool and quality of machined components. As a result, analysing the cutting temperatures and cutting tool life during milling operations can enhance productivity in process of part manufacturing using CNC machine tools. To predict the cutting tool life and cutting temperature during machining operations of AISI 1038 Carbon Steel, an application of the virtual machining system is developed. The impact of machining parameters such as cutting speed, feed rate and depth of cut on the cutting tool life and temperature are investigated in order to enhance productivity of milling operations. The modified Johnson-Cook model is used to investigate the combined influence of strain rate and deformation temperature on yield stress during alloy milling operations. Finite element analysis of milling operations is implemented to obtain the cutting temperature of the milling tool during the chip formation process. Then, cutting tool life during milling operations is predicted in order to be analyzed and maximized. The results of virtual machining system in prediction of cutting temperature as well as life of cutting tool are compared with the experimental results in order to validate the developed methodology in the study. So, an advanced virtual machining system is developed in the study in order to decrease cutting temperatures and increase cutting tool life in terms of efficiency enhancement of part production using milling operation.

Recent Development of Robotics Applications in Different Industries

The residual stress and surface roughness enhancement of ground surfaces can decrease the lifetime of workpiece by reducing its fatigue life. It is critical to accurately predict and minimize the residual stress and surface toughness of ground surfaces in order to enhance efficiency of part production using grinding operations. A virtual machining system is developed in the research work to minimize the surface integrity and residual stress during grinding operations of Inconel 718. The cutting temperature during grinding operations is calculated using the Inconel alloy Johnson Cook law models. Then, using the finite element method, residual stress during the grinding operation is estimated. Utilizing the established virtual machining method, the surface roughness is predicted in the study. Using the Taguchi optimization approach, the grinding parameters of depth of cut and feed velocity are optimized in order to reduce surface roughness as well as residual stress throughout grinding operations on Inconel 718 Superalloy. To confirm the effectiveness of the developed technique in the study, simulation and experimentation are conducted. As a result, the quality as well as reliability of the ground surfaces can be enhanced by using the developed virtual machining system in the study to minimize the surface roughness and residual stress of produced parts using grinding operations.The residual stress and surface roughness enhancement of ground surfaces can decrease the lifetime of workpiece by reducing its fatigue life. It is critical to accurately predict and minimize the residual stress and surface toughness of ground surfaces in order to enhance efficiency of part production using grinding operations. A virtual machining system is developed in the research work to minimize the surface integrity and residual stress during grinding operations of Inconel 718. The cutting temperature during grinding operations is calculated using the Inconel alloy Johnson Cook law models. Then, using the finite element method, residual stress during the grinding operation is estimated. Utilizing the established virtual machining method, the surface roughness is predicted in the study. Using the Taguchi optimization approach, the grinding parameters of depth of cut and feed velocity are optimized in order to reduce surface roughness as well as residual stress throughout grinding operations on Inconel 718 Superalloy. To confirm the effectiveness of the developed technique in the study, simulation and experimentation are conducted. As a result, the quality as well as reliability of the ground surfaces can be enhanced by using the developed virtual machining system in the study to minimize the surface roughness and residual stress of produced parts using grinding operations.The residual stress and surface roughness enhancement of ground surfaces can decrease the lifetime of workpiece by reducing its fatigue life. It is critical to accurately predict and minimize the residual stress and surface toughness of ground surfaces in order to enhance efficiency of part production using grinding operations. A virtual machining system is developed in the research work to minimize the surface integrity and residual stress during grinding operations of Inconel 718. The cutting temperature during grinding operations is calculated using the Inconel alloy Johnson Cook law models. Then, using the finite element method, residual stress during the grinding operation is estimated. Utilizing the established virtual machining method, the surface roughness is predicted in the study. Using the Taguchi optimization approach, the grinding parameters of depth of cut and feed velocity are optimized in order to reduce surface roughness as well as residual stress throughout grinding operations on Inconel 718 Superalloy. To confirm the effectiveness of the developed technique in the study, simulation and experimentation are conducted. As a result, the quality as well as reliability of the ground surfaces can be enhanced by using the developed virtual machining system in the study to minimize the surface roughness and residual stress of produced parts using grinding operations.

Scallop height, which is the uncut amount of workpiece materials in metal cutting paths, has an impact on the surface condition of manufactured products. To increase accuracy and surface quality of machined parts, the scallop height should be analyzed in order to be minimized. In this study, a virtual machining system is created to produce uniform scallop height throughout free form surface end milling. Machined surfaces are analyzed by the developed system in order to obtain the curvature of free form surfaces. Then, the machined surfaces are divided to the at, concave and convex surfaces in order to analyze and modify the cutting tool paths along milling operations. The machining parameters of step over, feed rate and spindle speed are modi ed in order to obtain uniform scallop height during milling operations of free form surfaces. As a result, the modi ed cutting tool paths can generate the uniform scallop height in order to increase surface quality of machined free form surfaces. To enhance the process of component manufacturing utilizing machining operations, the study's produced virtual machining system can raise the productivity as well as the accuracy of machined free from surfaces.

In the machining process, tool wear is an unavoidable reason for tool failure. Tool wear has an impact on not just tool life but also the quality of the finished product in terms of dimensional accuracy and surface integrity. Tool wear is a significant element in the annual cost of machining. It happens when the tool-work contact zone experiences abrupt geometrical damage, frictional force, and heat generation. It's essential to accurately evaluate tool wear during machining so that the cutting tool can be replaced before the workpiece surface sustains significant damage. The capacity to assess tool wear is crucial for ensuring high-quality workpieces. Artificial neural network, Deep learning and Machine learning systems, heat generation analysis, image data processing, finite element method and gaussian process are used in order to accurately predict the tool wear during machining operations. In this paper, cutting tool wear prediction in machining operations is reviewed in order to be analyzed and minimized. The main purpose of the study is to provide a useful resource for researchers in the field by presenting an overview of current research on cutting tool wear prediction in machining processes. As a consequence, the research area can be progressed by reading and assessing existing achievements in published articles in order to provide new ideas and methodologies in prediction and minimization of tool wear during machining operations.In the machining process, tool wear is an unavoidable reason for tool failure. Tool wear has an impact on not just tool life but also the quality of the finished product in terms of dimensional accuracy and surface integrity. Tool wear is a significant element in the annual cost of machining. It happens when the tool-work contact zone experiences abrupt geometrical damage, frictional force, and heat generation. It's essential to accurately evaluate tool wear during machining so that the cutting tool can be replaced before the workpiece surface sustains significant damage. The capacity to assess tool wear is crucial for ensuring high-quality workpieces. Artificial neural network, Deep learning and Machine learning systems, heat generation analysis, image data processing, finite element method and gaussian process are used in order to accurately predict the tool wear during machining operations. In this paper, cutting tool wear prediction in machining operations is reviewed in order to be analyzed and minimized. The main purpose of the study is to provide a useful resource for researchers in the field by presenting an overview of current research on cutting tool wear prediction in machining processes. As a consequence, the research area can be progressed by reading and assessing existing achievements in published articles in order to provide new ideas and methodologies in prediction and minimization of tool wear during machining operations.. In the machining process, tool wear is an unavoidable reason for tool failure. Tool wear has an impact on not just tool life but also the quality of the finished product in terms of dimensional accuracy and surface integrity. Tool wear is a significant element in the annual cost of machining. It happens when the tool-work contact zone experiences abrupt geometrical damage, frictional force, and heat gene . Artificial neural network, Deep learni