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PurposeThe purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control. Design/methodology/approachThis study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN. FindingsThis study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%. Practical implicationsOnce the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM. Originality/valueTo the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM. 

Abstract Enhancing fiber surfaces through in situ growth of nanomaterials is known to improve fiber composite properties by enhancing the interface between the fiber and matrix. In this study, hydrothermal processes are used to achieve two types of interfacial modification for carbon fiber: zinc oxide nanowires (ZnO NWs) and nickel‐based metal–organic frameworks (MOF). The interfacial strengths are evaluated using single fiber push‐in tests via nanoindentation and the interfaces are analyzed through dynamic modulus‐mapping. It is found that ZnO modification increases the interface strength by 9.40%, while MOF modification yields an even higher improvement of 16.34%. The load‐displacement plots exhibit distinctive inflection points, elucidated through microstructural observations. Examining the modulus map of the interface region, a transition in the storage modulus from the fiber to the matrix is identified. A capillary flow‐based model is developed to explain the resin penetration through nanoscale features. The findings reported here indicate that the timescale for resin absorption is significantly shorter than the curing timescales for the surface modifications explored in this study. 

The interface characteristics of the matrix and fibers significantly influence the evolution of residual stress in composite materials. In this study, we provide a methodology for reducing the residual stress in laminated composites by modifying the thermomechanical properties at the fiber–matrix interface. A hydrothermal chemical growth method was used to grow Zinc Oxide nanowires on the carbon fibers. We then utilized a novel digital image correlation approach to evaluate strains and residual stresses, in situ, throughout the autoclave curing of composites. We find that interface modification results in the reduction of residual stress and an increase in laminate strength and stiffness. Upon growing ZnO NWs on the carbon fibers, the maximum in situ in-plane strain components were reduced by approximately 55% and 31%, respectively, while the corresponding maximum residual stresses were decreased by 50.8% and 49.33% for the cross-play laminate [0°/90°] layup in the x and y directions, respectively. For the [45°/-45°] angle ply layup in the x-direction, the strain was decreased by 27.3%, and the maximum residual stress was reduced by 41.5%, whereas in the y-direction, the strain was decreased by 166.3%, and the maximum residual stress was reduced by 17.8%. Furthermore, mechanical testing revealed that the tensile strength for the [45°/-45°] and [0°/90°] laminates increased by 130% and 20%, respectively, with the interface modification. 

Additively manufactured (AM) composites based on short carbon fibers possess strength and stiffness far less than their continuous fiber counterparts due to the fiber’s small aspect ratio and inadequate interfaces with the epoxy matrix. This investigation presents a route for preparing hybrid reinforcements for AM that comprise short carbon fibers and nickel-based metal-organic frameworks (Ni-MOFs). The porous MOFs furnish the fibers with tremendous surface area. Additionally, the MOFs growth process is non-destructive to the fibers and easily scalable. This investigation also demonstrates the viability of using Ni-based MOFs as a catalyst for growing multi-walled carbon nanotubes (MWCNTs) on carbon fibers. The changes to the fiber were examined via electron microscopy, X-ray scattering techniques, and Fourier-transform infrared spectroscopy (FTIR). The thermal stabilities were probed by thermogravimetric analysis (TGA). Tensile and dynamic mechanical analysis (DMA) tests were utilized to explore the effect of MOFs on the mechanical properties of 3D-printed composites. Composites with MOFs exhibited improvements in stiffness and strength by 30.2% and 19.0%, respectively. The MOFs enhanced the damping parameter by 700%. 

Residual stresses are detrimental to composite structures as they induce processing defects like debonding, delamination, and matrix cracking which significantly decrease their load-bearing capability. In this research, a new in-situ approach using digital image correlation is utilized to analyze the effect of the cure cycle modification on residual stress evolution during processing. It was found that the modified cure cycle comprising abrupt cooling after gelation reduces the residual stresses. Five different layup configurations are investigated to examine the effect of fiber direction. A maximum average residual stress reduction of 31.8% is observed for the balanced unsymmetric [30/-30/60/-60] laminate. The residual stress reduction results in an increase in failure strength between 4 and 12% in the different layups and can lead up to a 22% increase in first-ply failure strength.