Precision and ultra-precision surfaces are crucial for many products – quality optics, joint & cranial implants, turbine blades, and industrial moulds & dies, to name a few. Automation in this context is distinct from standard procedures in industry, where the identical sequence of operations can be repeated over and over again. Ultraprecision tolerances may be tens to hundreds of times tighter, and this is compounded by the hundreds of diverse substrate materials in use. Even with modern computer numerically controlled (CNC) machines, skilled craftspeople are needed to plan a process-chain for a new material or geometry. Processes working at these tight tolerances, fall short of being fully-deterministic, so repeated process-metrology iterations are required. Surface-correction loops may be automated, but expert assessment should be performed at each step to check for unexpected anomalies. The ultimate goal of importing a part, processing autonomously, and delivering a finished part to an “optical” specification with no human intervention, is still a long way off. This paper describes the challenge and why it is important. It then melds together process-monitoring, psychology, artificial intelligence and robotics, to take a far-sighted view of how the ultimate goal can be realised.

中文翻译：

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完全自动化的精细光学制造-为什么如此艰难，我们在做什么？

精密和超精密表面对于许多产品至关重要-仅举几例，它们是高质量的光学器件，关节和颅骨植入物，涡轮机叶片以及工业模具。在这种情况下，自动化与行业中的标准程序不同，在工业中，相同的操作顺序可以重复一遍又一遍。超精密度公差可能要紧几十到数百倍，而使用的数百种不同的基板材料又使这种情况更加复杂。即使使用现代计算机数控（CNC）机器，也需要熟练的技术人员来计划新材料或几何形状的工艺链。在这些严格的公差范围内工作的过程无法完全确定，因此需要重复进行过程度量迭代。表面校正循环可以是自动的，但是应该在每个步骤上进行专家评估，以检查是否存在意外异常。导入零件，自主处理并在没有人工干预的情况下将成品零件交付到“光学”规格的最终目标还有很长的路要走。本文介绍了挑战及其重要性。然后将过程监控，心理学，人工智能和机器人技术融合在一起，以远见卓识地了解如何实现最终目标。