A New Scheme for Monitoring and Diagnosis of Multistage Manufacturing Processes Using Product Quality Measurements

Hossein Davari Ardakani, Brian Phillips, Behrad Bagheri, and Jay Lee
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phmc_15_055.pdf442.86 KBAugust 24, 2015 - 12:25pm

The development of robust monitoring systems for assuring the consistency and stability of multistage manufacturing processes necessitates the implementation of add-on sensors and advanced data collection, storage and analysis platforms to deal with the high-dimensional data collected from machines and products in multiple stages of the process. In many cases, such an approach may not be feasible due to high implementation costs and challenges of obtaining the process parameters and analyzing them effectively. This paper proposes an alternative approach for health monitoring of diagnosis of multistage manufacturing processes based on product quality measurements in a sensor-less environment. In the presented work, the available data consists of product quality parameters measured from multiple product types along with the manufacturing route associated with each product. A Gamma distribution is fit to the data for each parameter within a moving time window. Using the distribution fits, a metric is developed to represent the performance of each machine in a stage compared to its peers producing the same product. This metric is then aggregated across all the products produced by the machine to generate the final metric reflecting the overall performance of the machine. This performance metric is first calculated for the machines in the last stage. After flagging the under-performing machines in the last stage, the samples from under-performing machines in the last stage are removed from the data set and the remaining samples are used to calculate the similar metric for the prior stage. The suggested approach assumes the random distribution of products from one stage to next stage to facilitate the implementation of a comparison-based approach. This approach is tested on a data set collected from a manufacturing plant. The results demonstrate the effectiveness of such approach for monitoring and diagnosis of multistage manufacturing processes when the data is not available from within the process.

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Submission Keywords: 
Smart Manufacturing; Manufacturing Process Monitoring; Process Control
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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