This study investigates the fatigue degradation and crack propagation in the locking mechanism of PET bottle blow molding machines, focusing on the impact of elliptical cracks on the mechanism’s performance and longevity. The locking mechanism, which plays a vital role in securing the mold during the blow molding process, is subjected to repeated loading, making it susceptible to fatigue damage. Using a combination of finite element analysis (FEA) and experimental methodologies, we examine the stress distribution, deformation, and displacement in the mechanism under operational loads. The study identifies the most stressed component and models the behavior of an elliptical crack located at the center of this component. A stress intensity factor (K) of 3.7553 MPa.mm-0.5 is found, indicating significant risk in the crack region. Fatigue analysis using Goodman’s law predicts a service life of one million cycles with a safety factor of 2.08. These findings highlight the need for targeted design and maintenance strategies to enhance the reliability and durability of PET blow molding machines.

This study investigates the fatigue degradation and crack propagation in the locking mechanism of PET bottle blow molding machines, focusing on the impact of elliptical cracks on the mechanism’s performance and longevity. The locking mechanism, which plays a vital role in securing the mold during the blow molding process, is subjected to repeated loading, making it susceptible to fatigue damage. Using a combination of finite element analysis (FEA) and experimental methodologies, we examine the stress distribution, deformation, and displacement in the mechanism under operational loads. The study identifies the most stressed component and models the behavior of an elliptical crack located at the center of this component. A stress intensity factor (K) of 3.7553 MPa.mm-0.5 is found, indicating significant risk in the crack region. Fatigue analysis using Goodman’s law predicts a service life of one million cycles with a safety factor of 2.08. These findings highlight the need for targeted design and maintenance strategies to enhance the reliability and durability of PET blow molding machines.

This paper investigates the relationship between the extended spectrum of a bounded linear operator and its group inverse. We also establish a connection between the extended spectrum of the bounded linear operator and that of its Drazin inverse. As part of our analysis, we prove the following equality: σext((BA)D) = σext((AB)D), where (BA)D and (AB)D represent the Drazin inverses of BA and AB, respectively. 2020 Mathematics Subject Classification. 35K15; 35K55; 35K65; 35B40. Key words and phrases. extended spectrum; operator group inverse; Drazin inverse.

This paper investigates the relationship between the extended spectrum of a bounded linear operator and its group inverse. We also establish a connection between the extended spectrum of the bounded linear operator and that of its Drazin inverse. As part of our analysis, we prove the following equality: σext((BA)D) = σext((AB)D), where (BA)D and (AB)D represent the Drazin inverses of BA and AB, respectively. 2020 Mathematics Subject Classification. 35K15; 35K55; 35K65; 35B40. Key words and phrases. extended spectrum; operator group inverse; Drazin inverse.

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.

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.