Next Generation Small Intelligent Machining Systems

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Tool condition monitoring (TCM) and vibration analysis in large metal cutting machines (LMCMs), such as milling and lathe machines, have been an active research topic for many years; however, research in the area of handheld, medium/small sized, and robotic cutting machines (HMRC) has been neglected. HMRCs have many applications including, handheld drills, grinders, and rotary cutters for ceramic/granite/metal, high/low speed medical/dental soft/hard tissue cutting handpieces, jewelry design machines, construction drills, small computer numerical controlled (CNC), small milling-lathe machines, and 6-DoF robotic machining systems. This research program aims to develop a fundamental understanding about tool-workpiece interaction in HMRCs for designing the next generation of intelligent machining and prototyping systems. This research project targets the growing demand for enhancing the accuracy and efficiency of HMRC systems. Although LMCMs and HMRCs have several features in common, the latter have many additional challenges that have not been addressed in LMCM literature. For instance, HMRCs are generally orders of magnitudes lighter than LMCMs and, as such, their vibrations are more significant (may cause vibration white finger syndrome). Also, unlike LMCM, the cutting material might be unknown to the HMRC user before or may change during the process, or the material may be multi-layered (bone, tooth). The ultimate goal of this research program is to characterize HMRCs and develop intelligent systems that can improve accuracy, efficiency and safety. The proposed program will achieve this goal through two objectives: (a) modeling the cutting process in HMRCs, and (b) developing methods for condition monitoring and vibration suppression in HMRC operations. The program has several unique, novel and innovative features including: (i) modeling the complex nature of the cutting process, (ii) creating of a novel cutting process simulation platform that can generate all process variables needed for better tool designs, tool wear detection, and understanding of tool-workpiece interactions, (iii) designing a novel customized adaptive vibration isolation system and a cutting speed controller, which are intended to enhance the quality of work, and (iv) developing of an intelligent system that identifies/discriminates workpiece material during the cutting process in real-time (can even be applied to LMCM). For the first three contributions, the program will focuses on the development of general knowledge and tools required for the advancement of the field. For the last contribution, real-time tooth material identification/discrimination in dental filling (restoration) procedures has been selected as an example. That is primarily because this case presents a complex range of challenges to be tackled (tooth is composed of enamel, dentine, pulp, carries, and filling material such as amalgam, composite) so there is an anticipated smoother/faster transition of the research outcomes to other HMRC applications. Four graduate and ten undergraduate co-op students will be trained in this research program in a variety of disciplines and methods including: dynamic systems modeling, nonlinear systems analysis, mechanical and mechatronic systems control, numerical and computational modeling, smart materials, artificial intelligent systems, and experimental techniques. Moreover, the program is both multidisciplinary (mechatronics-computing science) and interdisciplinary (mechatronics-dentisty) and collaboration with experts with diverse background and expertise will provide a unique environment for HQP training. It is expected that the research outcomes will benefit many Canadian and international industrial, medical and small business sectors.