# CAD-Based Feature Recognition for Process Monitoring Planning in Assembly
## 2 Literature Review
### Process Monitoring as Part of Inspection Planning  
According to DIN 9000:2015 [6], quality control is part of quality management. Here, the focus is on achieving quality, which is defined as \"[the] degree to which a set of inherent characteristics of an object [meets] requirements\" [6]. To determine the quality of products, quantitative and qualitative characteristics, called inspection characteristics, must be determined [6]. Quality control is defined by variables that affect processes and lead to changes in product characteristics. These variables need to be monitored to diagnose the state of the machine and process [7; 8]. Various quality control strategies exist for assembly, such as process monitoring at several stations, the monitoring of individual processes (e.g., during joining), and product quality testing [1; 6]. Depending on the scenario resulting from the availability of sensor data and the complexity of production, some strategies are more favorable than others. Process monitoring as efficient runtime data acquisition and information provision, as well as data analysis and the recognition of patterns in quality-relevant process data, leads to increasingly better insights into the production process and its internal and external influences [1; 9; 10]. Direct and indirect process monitoring methods can be used to directly or indirectly monitor the relevant quality characteristics [5]. As an example, a force-torque sensor of a robot serves as a direct monitoring method for detecting the accuracy of a joining process. The evaluation of the sensors in the individual axes of the robot based on current deviations is an indirect monitoring approach. Direct monitoring methods have a higher accuracy because the sensor and sensor data are specifically designed to monitor the process. Indirect monitoring methods, on the other hand, can be more cost-effective and industrially applicable by using existing sensor data and correlating them with process quality [5; 11]. The increasing complexity of planning is based on a large number of possible process alternatives and factors that have to be taken into account due to the various possibilities of reconfigurable production systems. Greater variations in process parameters and technologies, as well as different alternatives, lead to an increased complexity not only in monitoring planning but also in process and assembly planning in general. Decision support systems can facilitate the planning phase by suggesting suitable alternatives according to predefined criteria (e.g., a high required accuracy or a low number of required reconfigurations) [12; 13; 14; 15]. However, before decision support systems can be used for planning, the data required for process monitoring planning are needed. In this case, quality requirements need to be identified that can later be monitored with appropriate resources (i.e., sensors). An overall decision support system capable of matching requirements with the monitoring skills of an existing production system and its resources was presented in previous works [16; 17; 15]. The aim of our work is to determine the quality requirements necessary for process monitoring semi-automatically. This provides a decision support system for the user, who is still able to interact with the system. The motivation for this work lies in the consideration that a new product variant with its existing assembly sequence and assembly plan can be analyzed with a reduced manual effort and lower required expert knowledge to determine the quality requirements, making the planning of process monitoring for small batch production more efficient. Multi-variant production on a flexible/reconfigurable production system requires multiple iterations of process planning. The approach in this work was therefore to identify monitoring requirements as part of the inspection planning. In the process planning phase, this can be classified as a subsequent phase of process or assembly planning [18].