Integrated Design and Manufacturing Analysis for Automated Fiber Placement Structures

Automated fiber placement provides many advancements beyond traditional hand layups in terms of efficiency and reliability. However, there are also a variety of unique challenges that arise with automated fiber placement technology. In particular, steering of tows over doubly-curved tool surfaces can result in material overlaps and gaps due to path convergence/divergence, fiber angle deviation, as well defects in the tows themselves such as puckers and wrinkles. Minimization of these defects is traditionally considered a task for the manufacturing discipline. Manufacturing specifications are often created for these defects based on laminate testing and can be inflexible to avoid more tests. Recent efforts have been made under the National Aeronautics and Space Administration (NASA) Advanced Composites Project (ACP) to develop software tools and processes that provide automated coupling between design and manufacturing disciplines. The objective of this coupling is to provide information to the design discipline on the manufacturability of a laminate while the laminate is being designed. A variety of software tools, both existing commercial tools and research tools under development, will be used to achieve this objective: HyperSizer for laminate optimization, the Computer Aided Process Planning module for selection of manufacturing process parameters, Vericut Composite Programming for tow path simulation, and COMPRO for deposition and cure defects. The newly developed Central Optimizer tool will be used to tie the modules together and drive the design for manufacturing process

Machine Learning Based AFP Inspection: A Tool for Characterization and Integration

Automated Fiber Placement (AFP) has become a standard manufacturing technique in the creation of large scale composite structures due to its high production rates. However, the associated rapid layup that accompanies AFP manufacturing has a tendency to induce defects. We forward an inspection system that utilizes machine learning (ML) algorithms to locate and characterize defects from profilometry scans coupled with a data storage system and a user interface (UI) that allows for informed manufacturing. A Keyence LJ-7080 blue light profilometer is used for fast 2D height profiling. After scans are collected, they are process by ML algorithms, displayed to an operator through the UI, and stored in a database. The overall goal of the inspection system is to add an additional tool for AFP manufacturing. Traditional AFP inspection is done manually adding to manufacturing time and being subject to inspector errors or fatigue. For large parts, the inspection process can be cumbersome. The proposed inspection system has the capability of accelerating this process while still keeping a human inspector integrated and in control. This allows for the rapid capability of the automated inspection software and the robustness of a human checking for defects that the system either missed or misclassified

Automated Fiber Placement of Composite Wind Tunnel Blades: Process Planning and Manufacturing

The ability to accurately manufacture large complex shapes in a consistent and repeatable manner has led to Automated Fiber Placement (AFP) being the predominant mode of manufacturing for large composite aerospace structures today. Currently, AFP is being considered for medium- and small-scale parts. Composite wind tunnel blades have traditionally been fabricated by hand layup for pre-impregnated or dry fabrics with resin infusion. Though well proven, the traditional fabrication method is laborious and tedious, and hence expensive. The project described in this paper used the Integral Structural Assembly of Advanced Composites (ISAAC) facility at the NASA Langley Research Center to build a manufacturing demonstration unit (MDU) with a shape representative of a wind tunnel blade. This MDU is used to discuss tooling, process planning, and fabrication. Additionally, details of the generic manufacturing workflow are presented

Development of a Design for Manufacturing Tool for Automated Fiber Placement Structures

Existing design processes for laminates constructed with automated fiber placement lack significant integration between the various software tools that compose the process. Tools for finite element analysis, computer aided drafting, stress analysis, tool path simulation, and manufacturing defect prediction are all critical parts of the design process. With traditional hand-layup laminates, the analysis performed with each of these tools could be fairly well decoupled from one another. However, for laminates generated by automated fiber placement, the disciplines can become significantly coupled, especially on structures with curvature. This gives rise to a need for integrated design for manufacturing software tools that are able to balance the competing objectives from each discipline. This paper describes the preliminary development of such a tool

Automated Fiber Placement Defect Identity Cards: Cause, Anticipation, Existence, Significance, and Progression

Automated Fiber Placement (AFP), a major composite manufacturing process, can result in many defects during the layup process that often require manual corrective action to produce a part with acceptable quality. These defects are the main limitation of the technology and can be hard to categorize or define in many situations. This paper provides a thorough definition and classification of all AFP defects. This effort constitutes a comprehensive and extensive library relevant to AFP defects. The defects selected and defined in this work are based on understanding and experience from the manufacture and research of advanced composite structure. Proper classification of these defects required an in-depth literature review and consideration of various viewpoints ranging from designers, manufacturers, analysts, and inspection professionals. Collectively, these sources were utilized to develop the most accurate view of each of the individual defect types. The results are presented as identity cards for each defect type, intended to provide researchers and the manufacturing industry a clear understanding of the (1) cause, (2) anticipation, (3) existence, (4) significance, and (5) progression of the defined AFP defects. The link between AFP defects and process planning, layup strategies, and machining was also investigated. Categorization of all important automated fiber placement defects is presented

Plasma Surface Functionalization of AFP Manufactured Composites for Improved Adhesive Bond Performance

Application of carbon fiber reinforced polymer (CFRP) as a high-performance structural material has widespread application in the present aerospace industry. However, as-processed composite materials require a comprehensive surface treatment prior to bonding to remove contaminants and impart surface functionality and topography to overcome their poor adhesion properties. Atmospheric pressure plasma jet treatment (APPJT) has been increasingly garnering attention as an alternate method for surface preparation of CFRP. This method has been reported to achieve success in imparting favorable polar functional groups into the composite surfaces enhancing wettability and surface energy of the bonded surfaces. In some cases, APPJT has been demonstrated to remove contaminants or, in the case of silicones, convert them to silica. In this study, an atmospheric pressure plasma jet (APPJ) system was used for surface activation of a composite laid-up by an automated fiber placement (AFP) machine. Surface modifications prior to and after treatment were characterized using water contact angle (WCA) measurements, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and atomic force microscopy (AFM). Double cantilever beam (DCB) tests were performed to quantify the bonding performance of the composites. The results show a marked enhancement of the mode I interlaminar fracture toughness with the application of APPJT

Numerical Simulation of AFP Nip Point Temperature Prediction for Complex Geometries

Material placement at the ideal nip point temperature over complex surfaces with uniformity across the width of the compaction rollers results in optimized part properties for Automated Fiber Placement (AFP) processes. However, current AFP systems utilize heat control models and methodologies, based on multiple process parameters such as feed-rate and orientation, that are mostly open-loop. Here, infrared (IR) heater input is calibrated as a function of process parameters during machine qualification. This work presents a numerical simulation to predict arrayed-infrared (AIR) emitter radiation onto a substrate that includes view factor implementation, IR radiative heat flow calculation, energy rate balance, and a transient heat transfer model. The purpose of this numerical model is to predict nip point temperature on complex surfaces, serving as a baseline for a new arrayed-infrared (AIR) thermoset heater to improve AFP process control. It is anticipated that this simulation will accurately control the temperature for high-speed AFP layup of complex geometries. An anticipated result of an AIR heater system is that material calibration and testing will be reduced as temperature is instantaneously monitored and controlled. Therefore, temperature across the roller width will be uniform during placement of complex parts, independent of their geometry

Assessment of Automated Fiber Placement for the Fabrication of Composite Wind Tunnel Blades

Composite wind tunnel blades are frequently fabricated by hand layup of prepreg fabrics. Though well proven, this fabrication method is laborious and expensive. The study described in this paper used the Integrated Structural Assembly of Advanced Composites (ISAAC) facility at the NASA Langley Research Center to explore whether automated fiber placement (AFP) could reduce manufacturing time and cost for production of wind tunnel blades. Two blades, taken from two NASA wind tunnels, were investigated as representative geometries. Computer-aided design models of the blade surfaces were created, and AFP process planning and programming were employed to study the manufacturability of the shapes. A placement/cure tool was manufactured for the chosen blade surface from thermoplastic material using an additive manufacturing process. The present study revealed that the AFP head geometry, primarily the heater configuration of the ISAAC system, is the primary constraint that limits the ability to manufacture the selected wind tunnel fan blades using AFP

Automated Fiber Placement Defects: Automated Inspection and Characterization

Automated Fiber Placement (AFP) is an additive composite manufacturing technique, and a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time consuming, which led to the motivation to develop a rapid automatic method of inspection. This paper suggests a new automated inspection system based on convolutional neural networks and image segmentation tasks. This creates a pixel by pixel classification of the defects of the whole part scan. This process will allow for greater defect information extraction and faster processing times over previous systems, motivating rapid part inspection and analysis. Fine shape, height, and boundary detail can be generated through our system as opposed to a more coarse resolution demonstrated in other techniques. These scans are analyzed for defects, and then each defect is stored for export, or correlated to machine parameters or part design. The network is further improved through novel optimization techniques. New training instances can also be created with every new part scan by including the machine operator as a post inspection check on the accuracy of the system. Having a continuously adapting inspection system will increase accuracy for automated inspections, cutting down on false readings