The robotic fiber placement (RFP) process depends on continuous fusion bonding and solidifying of prepreg tows onto a substrate by subjecting them to a compression force and heat flux. However the advantages provided when using the RFP to fabricate the composite structures, it has adverse effects due to the induced residual stresses (RSs). The current work presents the effect of the RFP on the induced RSs in thermoset composites. RFP is utilized to fabricate specimens from carbon fiber reinforced polymers. Effects of placement speed, compression force, heating temperature, heating gas flowrate, and the location of the heat flux outlet nozzle are all included in the present study. A total of 69 flat panels are manufactured under different process conditions. Subsequently, the in‐depth RSs are measured using the incremental hole‐drilling method. Besides, the microstructures of the samples are also analyzed. The results show that the change in process parameters could significantly affect the induced RSs of the thermoset composites. Subjecting the samples to higher thermal and pressure loads would induce more RSs, which are attributed to the occurrence of partial curing during the manufacturing process along with the change in laminate geometry and porosity.
Solid-State LiDAR (SSL) takes an increasing share of the LiDAR market. Compared with traditional spinning LiDAR, SSLs are more compact, energy-efficient and cost-effective. Generally, the current study of SSL mapping is limited to adapting existing SLAM algorithms to an SSL sensor. However, compared with spinning LiDARs, SSLs are different in terms of their irregular scan patterns and limited FOV. Directly applying existing SLAM approaches on them often increase the instability of a mapping process. This study proposes a systematic design, which consists of a dual-LiDAR mapping system and a three DOF interpolated six DOF odometry. For dual-LiDAR mapping, this work uses a 2D LiDAR to enhance a 3D SSL performance on a ground vehicle platform. The proposed system takes a 2D LiDAR to preprocess the scanning field into a number of feature sections according to the curvatures on the 2D fraction. Subsequently, this section information is passed to 3D SSL for direction feature selection. Additionally, this work proposes an odometry interpolation method which uses both LiDARs to generate two separated odometries. The proposed odometry interpolation method selectively determines the appropriate odometry information to update the system state under challenging conditions. Experiments are conducted in different scenarios. The results proves that the proposed approach is able to utilise 12 times more corner features from the environment than the comparied method, thus results in a demonstrable improvement in its absolute position error.
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