Error-propagation Based Geometrical Quality Prediction and Control Strategy for Complex Manufacturing Processes Using Parallel Kinematic Machines

英国制造业占国内生产总值的13%，出口的50%，直接雇佣了250万人。并联机床（PKM）是一种新型的机床，由于其优越的动态性能、灵活性和通用性，适用于大型零件加工，已被确定为填补计算机数控机床和工业机器人空白的关键技术。PKMs的使用在制造过程中创造了更多的灵活性和灵活性，同时实现了高精度和高速度。通过提高效率，减少产品缺陷，节省时间/金钱/能源，这对经济做出了重大贡献。PKM集成制造系统不可避免地会由于系统中部件的刚度和运动而引入误差。这些误差将在整个生产链中积累，并影响被加工零件的几何质量。基于PKM制造过程中的错误传播预测零件质量代表了管理生产过程的一个步骤变化，因为它消除了当前繁琐的试错过程，并使生产系统能够快速重新配置。其他好处包括减少20%的零件缺陷和返工，从而显著节省成本。零件质量是制造系统和加工过程相互作用的结果，加工误差及其在多种操作、机床、夹具和夹具中的传播相互交织。目前，还没有一个强有力的工业或国际标准来评估PKM刀具的加工能力。目前基于试错的方法需要大量的时间、材料和能源，是不可持续的，适合未来的智能工厂以可重构性来满足频繁的变化。因此，迫切需要新的分析方法。本研究通过揭示制造系统误差与零件或装配质量之间的关系，为基于PKM的柔性制造过程创造了一种新的质量预测能力。这导致了有效的误差判别控制策略，以实现更好的过程控制，同时确保所需的产品质量。通过研究PKM的刚度特性在影响加工过程中的作用，探索生产过程中的误差传播。这将导致铣削和钻孔在特定加工过程中的加工负荷模型的发展。实验将在QUB的PKM实验室和KCL PKM实验室进行，并通过建模和测试创建误差和零件质量之间的映射。这将增强对错误及其传播机制的理解，从而导致识别减少单个、传播和残余错误的潜在策略。一个由运动学/动力学分析模块、动静态模型、CAD模块和FEM模块组成的综合验证系统将在虚拟环境和制造现场实施。该项目将获得世界领先集团在先进PKM加工工艺方面的专业知识。该研究在其将学术前沿研究与复杂PKM制造过程中遇到的实际问题联系起来的性质上具有高度变革性。在AMRC， MTC和天津大学的支持下，通过合作工作，培训和研讨会，产生关键成果并揭示基础科学。该研究将使制造和机器人领域的学术界以及工业部门受益，他们将获得减少错误的知识，特别是与pkm集成的制造过程中的传播错误。

UK manufacture accounts for 13% of GDP, 50% of exports and directly employs 2.5 million people. Parallel Kinematic Machines (PKM) are a new type of machine tools and have been identified as a key technology that fills in the gap between computer numerical controlled machines and industrial robots due to their superior dynamic performance, flexibility and versatility to large-scaled parts machining. The use of PKMs creates more flexibility and dexterity in manufacturing processes while achieving high precision and high speed. This contributes significantly to the economy by improving efficiency, reducing product defects, and saving time/money/energy. The PKM integrated manufacturing system would inevitably introduce errors due to stiffness and motion of the components in the system. These errors will be accumulated through the production chain, and influence the geometrical quality of the machined parts. Predicting part quality based on error propagation in the PKM manufacturing processes represents a step change in managing production processes, as it removes the current cumbersome trial-and-error processes and enables rapid reconfiguration of production systems. Other benefits would include 20% reduction of part defects and rework, leading to a significant cost saving.Part quality resulted from interaction of manufacturing systems and machining processes, with intertwined machining errors and their propagation through multiple operations, machine tools, and fixtures and jigs. At the moment, there is no robust industrial or international standard to evaluate machining capability of PKM tools with these errors. Current trial-and-error based approach that requires a large amount of time, materials and energy, is not sustainable and suitable for future smart factories to meet frequent changes with reconfigurability. Therefore new analytical methods are urgently needed.The proposed research is adventurous in creating a new quality prediction capability for PKM based flexible manufacturing processes by revealing the relationship between manufacturing system errors and part or assembly quality. This leads to an effective error discrimination control strategy to achieve a better process control while ensuring the required product quality. Error propagation in a production process is to be explored by investigating the role of stiffness characteristics of a PKM in influencing the machining process. This will lead to the development of machining load-models in both milling and drilling on a specific machining process. Experiments are to be implemented at QUB's PKM laboratory and KCL PKM laboratory, and a map between errors and part quality is to be created through modeling and testing. This will deliver an enhanced understanding of errors and their propagation mechanism thereby leading to the identification of potential strategies for reducing individual, propagated, and residual errors. An integrated validation system that consists of a kinematic/dynamic analysis module, kinetostatic model, CAD module, and FEM module will be implemented in a virtual environment and in a manufacturing site. The project will access expertise from world-leading groups in advanced PKM machining processes.The research is highly transformative in its nature of connecting academic cutting-edge research to the practical issues encountered in complex PKM manufacture processes. Key results are to be generated and fundamental science is to be revealed in the collaborative work, training and workshops with support of AMRC, MTC and Tianjin University. The research will benefit the academic community in manufacture and robotics, and industrial sectors who will gain knowledge for reduction of errors particularly propagated errors in manufacturing processes integrated with PKMs.