FPGA Accelerated Digital Image Correlation For Clamping Force Measurement

Abstract: Digital image correlation is a contactless optical method used for displacement and strain measurement which has become increasingly popular in the field of experimental mechanics. A specialized use case for the algorithm is to measure the clamping force in bolted joints, a crucial metric when considering the longevity and reliability of the constructs. However, in order to be able to measure the clamping force in real-time, the digital image correlation has to be carried out rapidly as the tightening of the bolts can happen in milliseconds. One approach to increase the speed of the process is hardware acceleration. This thesis presents and evaluates multiple variations of an Field Programmable Gate Arrays (FPGA)-accelerated digital image correlation framework. The goal of the project is to accelerate the image correlation to sufficient speeds so it can be used for highly dynamic and continuous tightenings, which can take 20 to 200 ms and 200 to 1000 ms or more to finish respectively. A baseline implementation was created based on an innovative digital image correlation framework. Strain calculation was altered for the specialized use of clamping force determination. Afterward, different parts of the framework were selected and optimized for hardware acceleration. The parts include both preprocessing and correlation steps. The targets for acceleration were optimized using techniques such as quantization and pipelining. The accelerators were created using high-level synthesis and the resulting implementations utilize both the processor and FPGA parts of a Zynq-7000 system-on-chip. Results show that all accelerators reduce the total execution time of the framework by varying degrees. Accelerators targeting the preprocessing parts such as Gaussian and B-spline filtering proved to be the most effective in speeding up the process achieving a 1,56 and 1,12 times speedup for the fixed-point and a 1,2 and 1,07 times speedup for the double floating-point versions respectively. A combined version containing multiple accelerators resulted in a 1,9 times average speedup. It can be concluded that the presented approach is not fast enough for all highly dynamic tightening processes, as the fastest execution speed achieved is above 100 ms, but could be used for continuous tightening depending on constructs.