III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

This study presents the development and validation of a hybrid cutting force prediction model for ball end milling of aluminum 7075-T6 alloy. The model combines a mechanistic approach with a specific cutting force coefficient (Ks=850 N/mm²) sourced from experimental literature. Cutting forces in the x, y, and z directions are predicted by integrating differential force components with tool geometry and machining parameters. Experimental validation was performed under dry conditions at a spindle speed of 15,000 rpm. In the x-direction, the simulated force was 162.4 N versus an experimental force of 215.4 N; in the y and z-directions, predicted values (65.2 N and 25.3 N) closely matched experimental forces (74.3 N and 28.2 N), respectively. The corresponding mean absolute errors were 18.2% (x), 4.5% (y), and 3.3% (z). The higher error in the x direction highlights limitations in modeling tangential force dynamics, while the y and z predictions align closely with experimental data. Leveraging the experimentally derived Ks, the proposed model offers a practical tool for optimizing machining processes in the aerospace sector, with potential for further refinement in tangential force modeling.

This study presents the development and validation of a hybrid cutting force prediction model for ball end milling of aluminum 7075-T6 alloy. The model combines a mechanistic approach with a specific cutting force coefficient (Ks=850 N/mm²) sourced from experimental literature. Cutting forces in the x, y, and z directions are predicted by integrating differential force components with tool geometry and machining parameters. Experimental validation was performed under dry conditions at a spindle speed of 15,000 rpm. In the x-direction, the simulated force was 162.4 N versus an experimental force of 215.4 N; in the y and z-directions, predicted values (65.2 N and 25.3 N) closely matched experimental forces (74.3 N and 28.2 N), respectively. The corresponding mean absolute errors were 18.2% (x), 4.5% (y), and 3.3% (z). The higher error in the x direction highlights limitations in modeling tangential force dynamics, while the y and z predictions align closely with experimental data. Leveraging the experimentally derived Ks, the proposed model offers a practical tool for optimizing machining processes in the aerospace sector, with potential for further refinement in tangential force modeling.

This study presents the development and validation of a hybrid cutting force prediction model for ball end milling of aluminum 7075-T6 alloy. The model combines a mechanistic approach with a specific cutting force coefficient (Ks=850 N/mm²) sourced from experimental literature. Cutting forces in the x, y, and z directions are predicted by integrating differential force components with tool geometry and machining parameters. Experimental validation was performed under dry conditions at a spindle speed of 15,000 rpm. In the x-direction, the simulated force was 162.4 N versus an experimental force of 215.4 N; in the y and z-directions, predicted values (65.2 N and 25.3 N) closely matched experimental forces (74.3 N and 28.2 N), respectively. The corresponding mean absolute errors were 18.2% (x), 4.5% (y), and 3.3% (z). The higher error in the x direction highlights limitations in modeling tangential force dynamics, while the y and z predictions align closely with experimental data. Leveraging the experimentally derived Ks, the proposed model offers a practical tool for optimizing machining processes in the aerospace sector, with potential for further refinement in tangential force modeling.

This study presents the development and validation of a hybrid cutting force prediction model for ball end milling of aluminum 7075-T6 alloy. The model combines a mechanistic approach with a specific cutting force coefficient (Ks=850 N/mm²) sourced from experimental literature. Cutting forces in the x, y, and z directions are predicted by integrating differential force components with tool geometry and machining parameters. Experimental validation was performed under dry conditions at a spindle speed of 15,000 rpm. In the x-direction, the simulated force was 162.4 N versus an experimental force of 215.4 N; in the y and z-directions, predicted values (65.2 N and 25.3 N) closely matched experimental forces (74.3 N and 28.2 N), respectively. The corresponding mean absolute errors were 18.2% (x), 4.5% (y), and 3.3% (z). The higher error in the x direction highlights limitations in modeling tangential force dynamics, while the y and z predictions align closely with experimental data. Leveraging the experimentally derived Ks, the proposed model offers a practical tool for optimizing machining processes in the aerospace sector, with potential for further refinement in tangential force modeling.

This study presents the development and validation of a hybrid cutting force prediction model for ball end milling of aluminum 7075-T6 alloy. The model combines a mechanistic approach with a specific cutting force coefficient (Ks=850 N/mm²) sourced from experimental literature. Cutting forces in the x, y, and z directions are predicted by integrating differential force components with tool geometry and machining parameters. Experimental validation was performed under dry conditions at a spindle speed of 15,000 rpm. In the x-direction, the simulated force was 162.4 N versus an experimental force of 215.4 N; in the y and z-directions, predicted values (65.2 N and 25.3 N) closely matched experimental forces (74.3 N and 28.2 N), respectively. The corresponding mean absolute errors were 18.2% (x), 4.5% (y), and 3.3% (z). The higher error in the x direction highlights limitations in modeling tangential force dynamics, while the y and z predictions align closely with experimental data. Leveraging the experimentally derived Ks, the proposed model offers a practical tool for optimizing machining processes in the aerospace sector, with potential for further refinement in tangential force modeling.

One of the main factors that adversely affect surface quality, dimensional accuracy, and geometric precision during turning processes is workpiece’s deformation. The manufacturer's optimization of the cutting process is crucial. The goal of this work is to model and optimize workpiece’s deflection using statistical analysis. The tangential and radial cutting forces were observed as a function of the cutting parameters: cutting speed (Vc in m/min), advance (f in mm/rev), cutting depth (ap in mm), workpiece hardness (HB), and tool rake angle (An) using a numerical experimental plan (DOE) based on the Taguchi L32 table and the finite element analysis (FEA) tool (Third Wave AdvantEdge). For every test, the cantilever beam equation is used to determine the workpiece's deflection, which is then examined using the statistical approach based on the controllable parameters through cutting forces and the workpiece's overhang ratio (L/d). Prediction models have been found for the quantity of interest.

One of the main factors that adversely affect surface quality, dimensional accuracy, and geometric precision during turning processes is workpiece’s deformation. The manufacturer's optimization of the cutting process is crucial. The goal of this work is to model and optimize workpiece’s deflection using statistical analysis. The tangential and radial cutting forces were observed as a function of the cutting parameters: cutting speed (Vc in m/min), advance (f in mm/rev), cutting depth (ap in mm), workpiece hardness (HB), and tool rake angle (An) using a numerical experimental plan (DOE) based on the Taguchi L32 table and the finite element analysis (FEA) tool (Third Wave AdvantEdge). For every test, the cantilever beam equation is used to determine the workpiece's deflection, which is then examined using the statistical approach based on the controllable parameters through cutting forces and the workpiece's overhang ratio (L/d). Prediction models have been found for the quantity of interest.

One of the main factors that adversely affect surface quality, dimensional accuracy, and geometric precision during turning processes is workpiece’s deformation. The manufacturer's optimization of the cutting process is crucial. The goal of this work is to model and optimize workpiece’s deflection using statistical analysis. The tangential and radial cutting forces were observed as a function of the cutting parameters: cutting speed (Vc in m/min), advance (f in mm/rev), cutting depth (ap in mm), workpiece hardness (HB), and tool rake angle (An) using a numerical experimental plan (DOE) based on the Taguchi L32 table and the finite element analysis (FEA) tool (Third Wave AdvantEdge). For every test, the cantilever beam equation is used to determine the workpiece's deflection, which is then examined using the statistical approach based on the controllable parameters through cutting forces and the workpiece's overhang ratio (L/d). Prediction models have been found for the quantity of interest.

One of the main factors that adversely affect surface quality, dimensional accuracy, and geometric precision during turning processes is workpiece’s deformation. The manufacturer's optimization of the cutting process is crucial. The goal of this work is to model and optimize workpiece’s deflection using statistical analysis. The tangential and radial cutting forces were observed as a function of the cutting parameters: cutting speed (Vc in m/min), advance (f in mm/rev), cutting depth (ap in mm), workpiece hardness (HB), and tool rake angle (An) using a numerical experimental plan (DOE) based on the Taguchi L32 table and the finite element analysis (FEA) tool (Third Wave AdvantEdge). For every test, the cantilever beam equation is used to determine the workpiece's deflection, which is then examined using the statistical approach based on the controllable parameters through cutting forces and the workpiece's overhang ratio (L/d). Prediction models have been found for the quantity of interest.

The usual use of value stream mapping is studying to improve production lines that are already running. In this study, we used value stream mapping and the PDCA cycle on a production line that is still being finished and not yet operational. This work is important and unique because it uses a proactive approach to improve processes. The used method aims to create a waste-free production chain from the start. This is a big plus because it means avoiding losses with high costs and getting a very efficient production line from the start. The findings demonstrate that lean manufacturing tool (VSM) can be used on current and future production lines, and this strategy enhances production line efficiency from the outset by minimising non-value-added activities and maximising value-added activities.

The usual use of value stream mapping is studying to improve production lines that are already running. In this study, we used value stream mapping and the PDCA cycle on a production line that is still being finished and not yet operational. This work is important and unique because it uses a proactive approach to improve processes. The used method aims to create a waste-free production chain from the start. This is a big plus because it means avoiding losses with high costs and getting a very efficient production line from the start. The findings demonstrate that lean manufacturing tool (VSM) can be used on current and future production lines, and this strategy enhances production line efficiency from the outset by minimising non-value-added activities and maximising value-added activities.