Image analysis of topology changes for laser-based soft material processing

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Laser-based material processing is an emerging field, predominantly cutting, but also joining technologies such as welding, soldering, sintering and sewing or removal procedures like ablation and engraving. Application areas for laser-based soft material processing include fabric handling for textile industry, treatment of plants such as weeding in agriculture, as well as living tissue manipulation in biology and medicine. Further automation in these areas will save costs, optimize processes and may alleviate hunger and disease. The research in this Discovery Grant will focus on unsolved aspects for automation of laser-based soft material processing. Creation of a laser cut produces a topological change in soft material. For autonomous cuts, potential deformation and changes of the material need to be compensated for, including algorithmic consideration of the actual topology and its progression. In general, manipulation of soft material often results in changes of morphology and topology. While those morphological changes are equivalent with deformations, changes of topology can be understood as the development of holes inside the material. This research will detect and compensate topological changes induced by laser processing with 3D vision methods. In terms of practicability, laser processing has the advantage, that the area where the modification happens is not covered by larger objects (e.g. a knife blade). The overall hypothesis is that incorporating the change of topology in real-time will allow for a robust tracking of soft material deformation and lead to advanced results during autonomous laser processing. To prove the hypothesis, this Discovery Grant seeks in answering the following scientific key questions (SKQ): A -Can different types of topological changes be identified automatically? B -How can topological and morphological changes be differentiated computationally? C -Which computer vision methods can be leveraged to detect the change of topology and its spatial location on/inside the soft material? D -How to take morphological and topological changes into account while closing the loop between computer vision and laser processing methods? E -What is the accuracy and latency of detection of a topological change location with or without further deformation of the surrounding soft material? This project is sub-divided into four aims to answer the SKQ: Aim 1 cares about generation of a dataset on topological and morphological changes of soft material laser processing. Aim 2 include the core work of this proposal and result in algorithmic prototypes of the vision-based recognition of the topological and morphological changes and their specific locations. Aim 3 is translating theses algorithms into a closed-loop robotic system and Aim 4 quantifies the system characteristics and resulting accuracy in terms of the laser processing results.