## 6 Emerging Cognitive Approaches for Self-Optimizing Machines
Furthermore, the inline measurements form the input of real-time controllers to perform accurate control tasks, such as axis positioning. Considering the machine characteristics and measured process deviations, real-time controllers are constantly refined. Further details and examples can be found in [PERSON] et al. [104]. Essential prerequisites for SOM are developments in data fusion, process modelling and real-time control mentioned in the previous sections. In particular, the implementation of suitable measurement devices is crucial for the subsequent process steps. Only if the machine state is captured properly by accurate and robust measurements can valid process models be generated and subsequent optimal actions selected. Key enabling technologies as artificial intelligence and machine learning are promising approaches to process and analyse sensor data. These techniques are necessary to build cascaded process control loops based on comprehensive process observations, accurate process models and the knowledge about quality-influencing factors. Even if there have been promising advancements in various applications, there is still a lack in industrial implementations of comprehensive SOM solutions. A general overview of AI applications in manufacturing is given by [PERSON] [105]. [PERSON] et al. [106] presented an adaptive self-learning control approach to compensate for thermal errors on five-axis machine tools. Based on data captured during the process, a thermal autoregressive with exogenous input (ARX) model is identified and updated during the process to reduce thermal errors. [PERSON] et al. [107] presented a self-optimizing process planning approach for five-axis milling to automatically compensate for tool deflections. Combining a simulation model with support vector regression enabled the prediction of shape errors. The subsequent automated toolpath adaption resulted in a shape error reduction by 50%. The overall promising benefits of SOM are becoming more and more realistic with recent progresses in computational power, sensor techniques and AI algorithms. Autonomous selection of data sampling points enables an efficient generation of process models [64]. Based on these models, integrated optimization processes pave the way for autonomous self-tuning of parameters to guarantee a constant product quality. Figure 8: Self-optimizing manufacturing machine [104]. To enable further widespread in industrial applications, faster and data-scarce simulation and analysis methods will be needed. AI algorithms are promising techniques to generate fast models and to handle process uncertainty, while updating the model during manufacturing processes. However, these approaches suffer from low data efficiency and reliability of its predictions and are therefore still critical regarding safety requirements. Further challenges are the process dynamics, requiring fast optimized control values to control the process. Further developments are needed to cope with the complexity and dimensionality of the optimization problems. Moreover, more research will be needed to integrate the human interaction and transparency of the decisions made and an interactive machine user interface.