Quality prediction and compensation in multi-station machining processes using sensor-based fixtures

New fixture technologies, such as sensor-based fixtures, will significantly improve part quality through cutting-tool path compensations in multi-station machining processes (MMPs). Successful application of sensor-based fixtures depends on the development of new variation reduction methodologies to predict part quality in MMPs and detect the critical machining stations whose critical manufacturing variations can be estimated by installing a suitable sensor-based fixture. In this paper, a methodology is proposed to facilitate the implementation of sensor-based fixtures in MMPs. This methodology involves three key steps: (1) an identification of station-induced variations; (2) a sensor placement optimization method for designing sensor-based fixtures; and (3) a compensability analysis. A case study is conducted to demonstrate the effectiveness of the methodology.

► We study the unaddressed problem of sensor-based fixture application for part quality compensation.
► Sensor-based fixtures can notably improved part quality through tooling compensation in FMS.
► Optimal number of sensors is critical on compensability performance.
► Optimal distribution of sensors is critical on compensability performance.
► Sensor precision is critical to define number of sensors and optimal/non-optimal distributions.