Diagnosibility and sensitivity analysis for multi-station machining processes

Abstract

Dimensional variation is a major problem affecting product quality in discrete-part manufacturing. The stream of variation (SoV) methodology has been proposed as one of the systematic approaches to identify the root causes of process variation based on part measurements. This paper presents the results of the diagnosibility and sensitivity analysis study of the SoV methodology in a multi-station V8 cylinder head machining process used by a major domestic automotive manufacturer. The SoV model of dimensional machining errors has been derived based on the CAD description of the part and CAPP description of the process. Variation patterns of the final product were assessed based on the measurements of 20 automotive cylinder heads machined under normal process conditions, and the relative contributions of each machining station were assessed. In addition, one faulty product was observed and SoV model was used to identify the machining station that caused this quality problem. A station-level error decomposition method has been introduced and the SoV model correctly identified the culprit station. Furthermore, the sensitivities of dimensional features of the cylinder head to departures in fixture parameters away from their nominal values are evaluated based on the SoV model. Finally, four major issues arising from this implementation study of SoV in industry have been identified. Those are: non-diagnosibility of available measurements, vectorial representation of features, random sampling of parts at inspection and inadequate level of details in modeling of the process faults.