Next Generation Small Intelligent Machining Systems

下一代小型智能加工系统

• 批准号: RGPIN-2014-04526
• 负责人: Arzanpour, Siamak
• 金额: $ 1.75万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611087
• 关键词: Next; Generation; Small; Intelligent; Machining

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.

大型金属切削机床（LMCMs）（如铣床和车床）的刀具状态监测（TCM）和振动分析多年来一直是一个活跃的研究课题;然而，在手持式、中小型和机器人切削机床（HMRC）领域的研究一直被忽视。HMRC具有许多应用，包括用于陶瓷/花岗岩/金属的手持式钻头、研磨机和旋转刀具、高速/低速医疗/牙科软/硬组织切割手持件、珠宝设计机器、建筑钻头、小型计算机数控（CNC）、小型铣削-车床机器和6-DoF机器人加工系统。该研究计划旨在对HMRC中的工具-工件交互进行基本了解，以设计下一代智能加工和原型系统。该研究项目旨在提高HMRC系统的准确性和效率。虽然LMCM和HMRC有几个共同的特点，后者有许多额外的挑战，还没有在LMCM文献解决。例如，HMRC通常比LMCM轻几个数量级，因此，它们的振动更显著（可能导致振动白色手指综合征）。此外，与LMCM不同的是，HMRC用户之前可能不知道切割材料，或者在过程中可能会发生变化，或者材料可能是多层的（骨骼、牙齿）。该研究计划的最终目标是表征HMRC并开发能够提高准确性，效率和安全性的智能系统。 拟议的计划将通过两个目标实现这一目标：（a）在HMRC的切削过程建模，和（B）在HMRC操作的状态监测和振动抑制的发展方法。该计划有几个独特的，新颖的和创新的功能，包括：（i）对切削过程的复杂性质进行建模，（ii）创建新颖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-工件相互作用的理解所需的所有过程变量，（iii）设计新颖的定制自适应振动隔离系统和切削速度控制器，这是为了提高工作质量，和（iv）开发一个智能系统，识别/区分工件材料在切割过程中的实时（甚至可以应用到LMCM）。对于前三个贡献，该计划将侧重于发展该领域发展所需的一般知识和工具。对于最后的贡献，实时牙齿材料识别/牙科填充（修复）程序中的歧视已被选为一个例子。这主要是因为这种情况下提出了一系列复杂的挑战要解决（牙齿是由釉质，牙本质，牙髓，载体和填充材料，如汞合金，复合材料），所以有一个预期的研究成果顺利/更快的过渡到其他HMRC应用。 四名研究生和十名本科生将在本研究计划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，机械和机电一体化系统控制，数值和计算建模，智能材料，人工智能系统和实验技术。此外，该计划是多学科（机电一体化计算科学）和跨学科（机电一体化牙科），与具有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，医疗和小企业部门受益。