With the increasing use of automated fiber placement method for manufacturing highly precise bespoke composite components in the aerospace industry, the level of manufacturing defects within the laminate structure needs to be monitored and minimized for structural integrity. One of the main common defects in automated fiber placement process is misalignment between the tape paths in successive courses which leads to non-integrity of laminate and consequently significant reduction in mechanical strength of the laminate. Therefore, it is necessary to find an appropriate inspection method to monitor and identify these processing defects at the earlier stages of manufacturing. Since optical fiber Bragg grating sensors are being increasingly utilized for structural health monitoring in composite materials and as they were successfully implemented by Oromiehie et al. in their earlier work for on-line lay-up process monitoring, the same methodology is once again tried for identifying the misalignment defects in automated fiber placement process. The experiments are carried out on glass-fiber/nylon laminate with embedded fiber Bragg gratings for the automated tape placement method. The defects due to misalignment are identified by the fiber Bragg grating sensors through their reflected wavelength changes during the automated manufacturing process. The analysis of results indicates that the fiber Bragg grating sensors can be reliably implemented for on-line defect monitoring during the automated fiber placement process to ensure the quality of final product and maintain the expected design life.

Manufacturing of composites using Automated Fibre Placement(AFP) is a complicated process which involves large number of processing conditions and variables. Improper selection of these parameters adversely affect the quality and integrity of the manufactured laminates. Thus,it is important to develop a predictive model which can assess how changes in critical process conditions alter the outputs of the manufacturing process.The goal of this investigation is to learn the complex behaviour of composites by developing an intelligent model which can subsequently be used for the prediction of various characteristics of the composites. However,manufacturing of AFP composites is both expensive and time-consuming and therefore the available data samples are less,from the prospective of machine learning,which leads to the small data learning problem. This study first solves this problem through Virtual Sample Generation(VSG), then a Neural Network based predictive model is developed to accurately learn the complex relationships between various processing parameters in AFP.

The potential for increased productivity offered by automated fibre placement method has opened up a wider range of applications as well as new markets for composite materials. However, like many other manufacturing methods, different flaws such as voids or delamination may still occur during or after lay-up. Therefore, the use of automated fibre placement as an open-mould process where fibre/tape material is fed brings with it a need and an opportunity to establish a reliable inspection and monitoring method to ensure structural integrity not only after fabrication but also one step earlier, during the manufacturing process. Since optical fibre–based photonic sensing technologies are increasingly common for structural health monitoring of composite structures, selection of optical fibre Bragg grating sensors for manufacturing process monitoring has been successfully implemented here. Experiments are carried out on glass fibre/high-density polyethylene laminates with embedded fibre Bragg grating in the automated fibre placement method. The lay-up process conditions are monitored by the fibre Bragg grating sensors via measuring the reflected wavelengths which are related to pressure and temperature. The results presented in this article indicate that fibre Bragg grating sensing technique can be reliably employed for online monitoring of lay-up process to ensure the quality of final product.

Precision sensing in the characterization of complex additive manufacturing processes such as the Automated Fibre Placement (AFP) technique is important since the process involves a significant level of uncertainty in terms of quality and integrity of the manufactured product. These uncertainties can be monitored by embedding optical fibre Bragg grating (FBGs) sensors which provide accurate and simultaneous measurement of strain and temperature during the AFP process. The embedded sensors have been shown to remain resilient in continuous health monitoring after manufacturing. The thermal history obtained from the FBG sensors demonstrates a reduction of temperature on the bottom ply by up to 25% when the plies are laid one above the other. A numerical tool is developed to identify the physical parameters which may be responsible for the rise/fall of the temperature during ply layup. The numerical findings agree well with the sensor data and is extended to capture a breadth of parametric studies through the layup simulation. The model provides a comprehensive insight to the characteristics of the laid and the laying ply from a thermo-mechanics perspective.