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

Design of optimal rule-based controller for plug-in series hybrid electric vehicle

International audienceEnergy consumption of Hybrid Electric Vehicles (HEV) strongly depends on the adopted energy management strategy (EMS). Rule-Based (RB) controllers are the most commonly used for their ability of integration in real-time applications. Unlike global optimization routines, RB controllers do not ensure optimal energy savings. This study presents a methodology to design a close-to-optimal RB controller derived from global optimization strategies. First, dynamic programming (DP) optimization is used to derive the optimal behaviour of the powertrain components on the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), and then, the resulting performance of the powertrain components is used to design an optimized RB energy management strategy. Furthermore, the strategy is developed to cope with the variations in trip length and traffic conditions. The plug-in series hybrid electric vehicle is modelled using the energetic macroscopic representation (EMR). Results show that the proposed optimal RB controller is only consuming 1-2% more fuel compared to DP controllers and is resulting in a 13-16% less fuel consumption compared to basic RB controllers

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

Experimental Analysis of the Automated Fiber Placement Manufacturing Parameters for High and Low Tack Prepreg Material

Automated Fiber Placement (AFP) is a flexible but complex manufacturing technique which is used to layup carbon fiber tows into flat or curved structures. That could be achieved through linear and steered paths of fibers. The quality of AFP layup is highly dependent on the manufacturing process parameters such as layup temperature, tow feedrate, compaction force, and tow tension. Understanding how those parameters affect the process is crucial for achieving the desirable quality and productivity. For this work, an experimental investigation is carried out to determine the processing window that yields optimal quality for two types of material: High tack thermoset prepreg, and low tack thermoset prepreg. The project is split into two tasks. For the first task, experiments are carried out on linear paths to determine the effects of the parameters on substrate to tool adhesion as well as the substrate to substrate adhesion. A design of experiment is developed to cover a wide range of permutations to experimentally find the optimal process window. The second task investigates the quality of steered paths as function of the parameters at different steering radii. The quality of a steered path is governed by the critical curvature radius, which is the minimum radius allowed before the formation of wrinkles or other defects. However, this radius has been found to be dependent on the process parameters and thus by changing those parameters a higher curvature can be achieved. Data acquisition is performed using different sensors, to obtain the necessary information about the process to infer the relations between the quality and the parameters

Methodology to Design an Optimal Rule Based Energy Management Strategy Using Energetic Macroscopic Representation: Case of Plug-In Series Hybrid Electric Vehicle

International audienc