Wenzhen Huang

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WENZHEN HUANG1,∗, TIRAWAT PHOOMBOPLAB2,3 and DARIUSZ CEGLAREK2,3 1Department of Mechanical Engineering, University of Massachusetts, Dartmouth, MA 02747, USA E-mail: whuang@umassd.edu 2Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA 3International Digital Laboratory, WMG, University of Warwick,(More)
A discrete-cosine-transformation (DCT) based decomposition method is proposed for modeling part form error, which decomposes the error field into a series of independent error modes. Compression, which ensures a compact model, is achieved by correlation reduction or mode truncation based on good energy compaction property of DCT. The part error related to(More)
Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of processrelated faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new(More)
Coordinate measurement systems (CMSs) dominate the dimensional control and diagnostics of various manufacturing processes. However, CMSs have inherent errors caused by the lack of a tracing ability for some of the measured part features. This is important for product inspection and process variation reduction in a number of automated manufacturing systems,(More)
A stream-of-variation analysis (SOVA) model for three-dimensional (3D) rigid-body assemblies in a single station is developed. Both product and process information, such as part and fixture locating errors, are integrated in the model. The model represents a linear relationship of the variations between key product characteristics and key control(More)
Recent developments in modeling stream of variation in multistage manufacturing system along with the urgent need for yield enhancement in the semiconductor industry has led to complex large scale simulation problems in design and performance prediction, thus challenging current Monte Carlo (MC) based simulation techniques. MC method prevails in statistical(More)
Yield-based sensitivity analysis methods and algorithms are developed for process capability evaluation in this paper. Yield, the conformity to product specifications, is subject to critical design parameters such as dimensions, tolerances, and specification limits. The uncertainties in determining these parameters in design and manufacturing affect the(More)
Dimensional integrity has a significant impact on the quality of the final products in multistation assembly processes. A large body of research work in fault diagnosis has been proposed to identify the root causes of the large dimensional variations on products. These methods are based on a linear relationship between the dimensional measurements of the(More)
Domain adaptation learning (DAL) investigates how to perform a task across different domains. In this paper, we present a kernelized local-global approach to solve domain adaptation problems. The basic idea of the proposed method is to consider the global and local information regarding the domains (e.g., maximum mean discrepancy and intraclass distance)(More)