Fault detection and prognosis of assembly locating systems using piezoelectric transducers

Fixture faults have been identified as a principal root cause of defective products in assembly lines; however, there exists a lack of fast and accurate monitoring tools to detect fixture fault damage. Locating fixture damage causes a decrease in product quality and production throughput due to the extensive work required to detect and diagnosis a faulty fixture. In this paper, a unique algorithm is proposed for fixture fault monitoring based on the use of autoregressive models and previously developed piezoelectric impedance fixture sensors. The monitoring method allows for the detection of changes within a system without the need for healthy references. The new method also has the capability to quantify deterioration with respect to a calibrated value. Deterioration prognosis can then be facilitated for structural integrity predictions and maintenance purposes based on the quantified deterioration and forecasting algorithms. The proposed robust methodology is proven to be effective on an experimental setup for monitoring damage in locating fixtures. Fixture wear and failure are successfully detected by the methodology, and fixture structural integrity prognosis is initiated. J. L. Rickli · J. T. Dreyer · S. M. Pandit Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA J. A. Camelio (B) Grado Dept. of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 250 Durham Hall, Blacksburg, VA 24061, USA e-mail: [email protected] Introduction Increased global competition has resulted in the necessity to be on the forefront of product innovations and in improving product quality to reduce production costs. A specific area of research with the goal of improving product quality and process productivity is variation reduction on critical product and process characteristics. Variability is inherent in every manufacturing system and difficult to isolate to a single component or assembly station. As a result, variation propagation through assembly processes produces out-ofspecificationproducts that require component level solutions. In-line detection, diagnosis, and prognosis methods aim to monitor the individual components that are most responsible for product dimensional variation. Automotive sheetmetal assembly stations provide an ideal example of processes that are subject to variation propagation. Variation in geometric dimension quality impacts gaps, flushness efforts of closure panels, wind noise levels, and water leaks. Dimensional variation is introduced when joining several hundred sheet metal parts in multiple assembly stations. Variability in sheet metal assembly processes is attributed to multiple critical sources; non-nominal parts (part variation), non-nominal location or deteriorated condition of tooling (fixture variation), and imperfect welds. Early detection of fixture damage and prediction of fixture service life will result in an increase in product quality and production throughput. The ability to accurately and quickly monitor, diagnose, and predict assembly locating fixture integrity is essential for early damage detection in locating fixtures. Locating fixtures (Fig. 1) have been identified as a primary root cause of poor product quality and account for 70% of all assembly quality problems (Ceglarek and Shi 1995). In-line and real time monitoring of locating fixtures eliminates extensive production downtime required to investigate,