Next Generation Small Intelligent Machining Systems

大型金属切削机床（如铣床和车床）的刀具状态监测和振动分析是一个活跃的研究课题。然而，在手持式、中小型和机器人切割机（HMRC）领域的研究一直被忽视。HMRCs有许多应用，包括手持式钻头，磨床和陶瓷/花岗岩/金属的旋转切割机，高/低速医疗/牙科软/硬组织切割手机，珠宝设计机，建筑钻机，小型计算机数控（CNC），小型铣床车床和6自由度机器人加工系统。本研究计划旨在发展对HMRCs中工具-工件交互的基本理解，以设计下一代智能加工和原型系统。这个研究项目的目标是提高HMRC系统的准确性和效率的日益增长的需求。尽管LMCM和hmrc有几个共同的特征，但后者有许多额外的挑战，这些挑战在LMCM文献中没有得到解决。例如，hmrc通常比lmcm轻几个数量级，因此，它们的振动更明显（可能导致振动白指综合征）。此外，与LMCM不同的是，HMRC用户在切割之前可能不知道切割材料，或者在加工过程中可能会发生变化，或者材料可能是多层的（骨头、牙齿）。该研究计划的最终目标是表征HMRCs，并开发能够提高准确性、效率和安全性的智能系统。拟议的项目将通过两个目标来实现这一目标：(a)对HMRC的切割过程进行建模，以及(b)开发HMRC操作中的状态监测和振动抑制方法。该计划有几个独特，新颖和创新的特点，包括：(i)对切削过程的复杂性质进行建模；（ii）创建一个新的切削过程仿真平台，该平台可以生成更好的刀具设计、刀具磨损检测和刀具-工件相互作用所需的所有过程变量；（iii）设计一种新的定制自适应隔振系统和切削速度控制器，旨在提高工作质量；（iv）开发一种在切割过程中实时识别/区分工件材料的智能系统（甚至可以应用于LMCM）。对于前三个贡献，该计划将侧重于发展该领域进步所需的一般知识和工具。最后一个贡献是，以牙齿填充（修复）过程中的实时牙齿材料识别/识别为例。这主要是因为这个案例提出了一系列复杂的挑战需要解决（牙齿由牙釉质、牙本质、牙髓、牙托和填充材料（如汞合金、复合材料）组成），因此预计研究成果将更顺利/更快地过渡到HMRC的其他应用中。四名研究生和十名本科生将在这个研究项目中接受各种学科和方法的培训，包括：动态系统建模、非线性系统分析、机械和机电系统控制、数值和计算建模、智能材料、人工智能系统和实验技术。此外，该项目是多学科（机电一体化-计算科学）和跨学科（机电一体化-牙科），与不同背景和专业知识的专家合作将为HQP培训提供独特的环境。预计研究成果将使许多加拿大和国际工业、医疗和小型企业部门受益。

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.