Prediction and improvement of part quality in multi-station machining systems applying the Stream of Variation model

Resumo

Recent research efforts have been aimed toward deriving mathematical models to relate manufacturing sources of variation with part quality variations in multi-station machining systems in order to integrate design and manufacturing knowledge. Such integration would make it possible to create a large number of applications to improve product and process design and manufacturing in areas such as fault diagnosis, best placement of inspection stations, process planning, dimensional control and process-oriented tolerancing. However, nowadays there are still important limitations on the development of these models and even some of their potential applications have still not been studied in detail. The comprehensive research work described in this dissertation contributes to overcome some of the current limitations in this field. The dissertation is divided into three parts. The first part presents a comprehensive literature review of machining sources of error that produce macro and/or micro-geometrical variations on machined surfaces, and the 3D manufacturing variation models (the Stream of Variation model – SoV – and the Model of the Manufactured Part – MoMP) applied in the literature to analyze the propagation of those variations in multi-station machining processes. The second part of the dissertation highlights the current limitation of the SoV model through an experimental study where fixture- and machining-induced variations are analyzed. To overcome this limitation, the extension of the SoV model is formulated by modeling and adding machining-induced variations. The third part of the dissertation presents some potential applications of the SoV model and its extended version for part quality improvement. The first application shows how to apply the SoV model together with sensor-based fixtures when there are CNC machine-tools in the multi-station machining system in order to modify the cutting-tool path and partially compensate the expected part quality error. The second developed application deals with the evaluation and improvement of manufacturing process plans by integrating the SoV model and historical shop-floor quality data. Finally, the third application shows the use of the extended SoV model for the improvement of process-oriented tolerancing in multi-station machining processes.