Unexpectedly, it was found that when a waterborne polyacrylate adhesive was placed on carbon steel it was not tacky; this was not the case for the same adhesive placed on stainless steel. It was determined that the surface energy, as measured with liquid contact angles, of the adhesive films is significantly different between the two films, with the non-tacky film having a higher surface energy. Atomic force microscopy (AFM) showed that the non-tacky surface has a higher roughness which minimizes the contacting area between the film and a contacting surface. Analysis of the heating of the carbon steel coupon with infrared thermography shows a non-uniform temperature profile at the surface. This experimental data is corroborated using a 2-D heat transfer model that incorporates the heat transfer characteristics of the various components of carbon steel. Surface driven flow, or Marangoni convection, can develop from temperature gradients and are known to cause increased roughness in polymer films. IR thermography measurements of the adhesive film during drying shows larger temperature differences for the films on carbon steel than on stainless steel. These larger temperature differences induce greater Marangoni convection, which result in the rougher surfaces on carbon steel that were measured with AFM. The effect of lowering the tack of a polyacrylate film has significant impact in the dryer section of a paper machine. This effect was quantified using the Web Adhesion Drying Simulator, which is a laboratory-scale apparatus that measures the energy required to pull the sheet from a metal surface. By substituting the adhesive-on-stainless steel with the less-sticky adhesive-on-carbon steel surface, the energy required to pull the sheet from the metal surface was reduced significantly and the picking associated with the test was nearly eliminated.

Light Detection And Ranging (LiDAR) is a hot topic today primarily because of its vast importance within autonomous vehicles. LiDAR sensors are capable of capturing and identifying objects in the 3D environment. However, a drawback of LiDAR is that they perform poorly under adverse weather conditions. Noise present in LiDAR scans can be divided into random and pseudo-random noise. Random noise can be modeled and mitigated by statistical means. The same approach works on pseudo-random noise, but it is less effective. For this, Deep Neural Nets (DNN) are better suited. The main goal of this thesis is to investigate how snow can be detected in LiDAR point clouds and filtered out. The dataset used is Winter Adverse DrivingdataSet (WADS). Supervised filtering contains a comparison between statistical filtering and segmentation-based neural networks and is evaluated on recall, precision, and F1. The supervised approach is expanded by investigating an ensemble approach. The supervised result indicates that neural networks have an advantage over statistical filters, and the best result was obtained from the 3D convolution network with an F1 score of 94.58%. Our ensemble approaches improved the F1 score but did not lead to more snow being removed. We determine that an ensemble approach is a sub-optimal way of increasing the prediction performance and holds the drawback of being more complex. We also investigate an unsupervised approach. The unsupervised networks are evaluated on their ability to find noisy data and correct it. Correcting the LiDAR data means predicting new values for detected noise instead of just removing it. Correctness of such predictions is evaluated manually but with the assistance of metrics like PSNR and SSIM. None of the unsupervised networks produced an acceptable result. The reason behind this negative result is investigated and presented in our conclusion, along with a model that suffers none of the flaws pointed out.