Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic. / PHD / A lot of people think the car noise is mostly from the engine, exhaust, or wind. However, this is not true. The noise in the exterior mainly comes from tires at over 25 mph. At normal highway speed, e.g., 60 mph, tire noise contributes over 70% of total noise. A quiet tire is desired for driving comfort. A number of attempts to reduce tire noise have been made in tire industries, including the tread pattern optimization and the tire structure design. In this work, a model was developed to predict the tire noise based on the tread pattern, tire size, tread rubber hardness, and vehicle speed. The model is called Artificial Neural Network Model of Tire-Pavement Interaction Noise (ANN Model of TPIN, or AMOT). This model is able to predict the noise contributions from the tread pattern and the pavement separately. Tire companies can use the model to design quite tires while customers can have an insight on choosing quite tires based on the tread patterns and/or tire structure.

Abstract In the world where every sector of industrial manufacturing is being converted toautomated systems, surface finishing processes like sanding and polishing seem to lag.This phenomenon is not surprising as these processes are complex to optimize. Therehave been projects going on with the support of European Commission to findsolutions under SYMPLEXITY (Symbiotic Human-Robot Solutions for ComplexSurface Finishing Operations). One of the projects in under this include poliMATIC(Automated Polishing for the European Tooling Industry). Halmstad University isinvolved in doing projects. This project took a portion of this study in aim to understand a foam material’s behavior used for sanding tool at the tip of a robotic arm. This is studied using a forcemeasurement system developed at Halmstad University. The project has two sectionsand starts with one; Understanding the force measurement system and upgrading innecessary ways. Two; studying how the foam material compressive hardness propertyis affected when the material is fit with sandpaper for sanding operation using theforce measurement system. The study finally revealed how the combination of thefoam with sandpaper affects the robustness of the material, and significantlyimproved the output of the system with by reducing the noise level with 40%.