LM
L. Meregalli
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Automated Fiber Placement (AFP) is a manufacturing process used to produce composite structures, mainly in the aerospace industry, where a robotic system deposits one or more long-fiber tapes on a mold or surface by applying pressure and high temperature to the material. Currently, AFP-manufactured components are mostly manually inspected by operators or using post-production Non-Destructive Testing (NDT). These methods are time-consuming and often have low reliability. For these reasons, online monitoring systems are being adopted, which are technologies capable of detecting defects while the component is being manufactured. However, these systems still require human operators to fix the defects or to change process parameters to avoid them.
This thesis addresses this gap by developing an Adaptive Process Improvement System (APIS) for AFP, which consists of a closed-loop approach for defect mitigation. Central to the system is a profilometer sensor integrated directly onto the AFP machine. This sensor, coupled with an embedded defect detection software, automatically acquires geometric and defect data from the deposited layer, which are then used to generate a detailed defect visualization and a quantitative defect report. Prior to the algorithm design, a comprehensive foundational analysis was conducted to quantify the relationship between AFP process parameters and the formation of specific defects. This correlation analysis established the necessary knowledge base by determining which parameter adjustments cause specific improvements in laminate quality.
The system was validated through a series of deposition tests on the AFP machine at the Airbus Composite Technology Center. Following the production and scanning of a ply, the defect report is analyzed, and the system autonomously adjusts the process parameters for the immediate subsequent layer. This is performed by the APIS algorithm, developed using Python 3.12.9. The results are highly promising: for each subsequent layer, the improved process parameters consistently led to a measurable improvement in laminate quality, quantified by a significant reduction in the frequency and severity of defects. The successful implementation of the system demonstrates the feasibility of achieving fully autonomous, adaptive control in AFP, representing an important step toward Industry 4.0 integration for high-performance composite manufacturing. ...
This thesis addresses this gap by developing an Adaptive Process Improvement System (APIS) for AFP, which consists of a closed-loop approach for defect mitigation. Central to the system is a profilometer sensor integrated directly onto the AFP machine. This sensor, coupled with an embedded defect detection software, automatically acquires geometric and defect data from the deposited layer, which are then used to generate a detailed defect visualization and a quantitative defect report. Prior to the algorithm design, a comprehensive foundational analysis was conducted to quantify the relationship between AFP process parameters and the formation of specific defects. This correlation analysis established the necessary knowledge base by determining which parameter adjustments cause specific improvements in laminate quality.
The system was validated through a series of deposition tests on the AFP machine at the Airbus Composite Technology Center. Following the production and scanning of a ply, the defect report is analyzed, and the system autonomously adjusts the process parameters for the immediate subsequent layer. This is performed by the APIS algorithm, developed using Python 3.12.9. The results are highly promising: for each subsequent layer, the improved process parameters consistently led to a measurable improvement in laminate quality, quantified by a significant reduction in the frequency and severity of defects. The successful implementation of the system demonstrates the feasibility of achieving fully autonomous, adaptive control in AFP, representing an important step toward Industry 4.0 integration for high-performance composite manufacturing. ...
Automated Fiber Placement (AFP) is a manufacturing process used to produce composite structures, mainly in the aerospace industry, where a robotic system deposits one or more long-fiber tapes on a mold or surface by applying pressure and high temperature to the material. Currently, AFP-manufactured components are mostly manually inspected by operators or using post-production Non-Destructive Testing (NDT). These methods are time-consuming and often have low reliability. For these reasons, online monitoring systems are being adopted, which are technologies capable of detecting defects while the component is being manufactured. However, these systems still require human operators to fix the defects or to change process parameters to avoid them.
This thesis addresses this gap by developing an Adaptive Process Improvement System (APIS) for AFP, which consists of a closed-loop approach for defect mitigation. Central to the system is a profilometer sensor integrated directly onto the AFP machine. This sensor, coupled with an embedded defect detection software, automatically acquires geometric and defect data from the deposited layer, which are then used to generate a detailed defect visualization and a quantitative defect report. Prior to the algorithm design, a comprehensive foundational analysis was conducted to quantify the relationship between AFP process parameters and the formation of specific defects. This correlation analysis established the necessary knowledge base by determining which parameter adjustments cause specific improvements in laminate quality.
The system was validated through a series of deposition tests on the AFP machine at the Airbus Composite Technology Center. Following the production and scanning of a ply, the defect report is analyzed, and the system autonomously adjusts the process parameters for the immediate subsequent layer. This is performed by the APIS algorithm, developed using Python 3.12.9. The results are highly promising: for each subsequent layer, the improved process parameters consistently led to a measurable improvement in laminate quality, quantified by a significant reduction in the frequency and severity of defects. The successful implementation of the system demonstrates the feasibility of achieving fully autonomous, adaptive control in AFP, representing an important step toward Industry 4.0 integration for high-performance composite manufacturing.
This thesis addresses this gap by developing an Adaptive Process Improvement System (APIS) for AFP, which consists of a closed-loop approach for defect mitigation. Central to the system is a profilometer sensor integrated directly onto the AFP machine. This sensor, coupled with an embedded defect detection software, automatically acquires geometric and defect data from the deposited layer, which are then used to generate a detailed defect visualization and a quantitative defect report. Prior to the algorithm design, a comprehensive foundational analysis was conducted to quantify the relationship between AFP process parameters and the formation of specific defects. This correlation analysis established the necessary knowledge base by determining which parameter adjustments cause specific improvements in laminate quality.
The system was validated through a series of deposition tests on the AFP machine at the Airbus Composite Technology Center. Following the production and scanning of a ply, the defect report is analyzed, and the system autonomously adjusts the process parameters for the immediate subsequent layer. This is performed by the APIS algorithm, developed using Python 3.12.9. The results are highly promising: for each subsequent layer, the improved process parameters consistently led to a measurable improvement in laminate quality, quantified by a significant reduction in the frequency and severity of defects. The successful implementation of the system demonstrates the feasibility of achieving fully autonomous, adaptive control in AFP, representing an important step toward Industry 4.0 integration for high-performance composite manufacturing.