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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638699
• 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.

多年来，大型金属切割机(如铣床和车床)的刀具状态监测和振动分析一直是一个活跃的研究课题，但在手持式、中小型和机器人切割机(HMRC)领域的研究一直被忽视。HMRC有许多应用，包括用于陶瓷/花岗岩/金属的手持式钻床、磨床和旋转刀具、高速/低速医疗/牙科软硬组织切割手持设备、珠宝设计机、建筑钻机、小型计算机数控(NC)、小型铣床和六自由度机器人加工系统。本研究项目的目的是为设计下一代智能加工和原型系统而对HMRC中的刀具-工件交互作用有一个基本的了解。这一研究项目的目标是提高HMRC系统的精度和效率的日益增长的需求。虽然LMCM和HMRC有几个共同的特征，但后者有许多LMCM文献中没有解决的额外挑战。例如，HMRC通常比LMCM轻几个数量级，因此，它们的振动更显著(可能导致振动白指症)。此外，与LMCM不同，HMRC用户可能不知道切割材料，也可能在加工过程中发生变化，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确定HMRC的特征，并开发能够提高准确性、效率和安全性的智能系统。拟议的方案将通过两个目标实现这一目标：(A)对HMRC中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。该程序具有几个独特、新颖和创新的功能，包括：(I)对复杂的切割过程进行建模，(Ii)创建一个新型的切割过程仿真平台，该平台可以生成更好的刀具设计、刀具磨损检测和了解刀具与工件相互作用所需的所有过程变量，(Iii)设计新型定制的自适应隔振系统和切割速度控制器，旨在提高工作质量，以及(Iv)开发实时识别/识别切割过程中的工件材料的智能系统(甚至可以应用于LMCM)。对于前三项贡献，该方案将侧重于开发促进该领域发展所需的一般知识和工具。在最后一篇文章中，选择了牙齿填充(修复)过程中的实时牙齿材料识别/识别作为一个例子。这主要是因为这个案例提出了一系列需要解决的复杂挑战(牙齿由牙釉质、牙本质、牙髓、载体和汞合金、复合材料等填充材料组成)，因此预期研究成果将更平稳/更快地过渡到其他HMRC应用。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，该项目既是多学科的(机电一体化-计算科学)又是跨学科的(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。预计研究成果将使加拿大和国际上的许多工业、医疗和小企业部门受益。