Literature DB >> 31555021

Observations on Developing Reliability Information Utilization in a Manufacturing Environment with Case Study: Robotic Arm Manipulators.

Michael Sharp1.   

Abstract

Manufacturing environments face many unique challenges in regards to balancing high standards of both product quality and production efficiency. Proper diagnostic health assessment is essential for maximizing uptime and maintaining product and process quality. Information for diagnostic assessments, and reliability information in general, can come from a myriad of sources that can be processed and managed through numerous algorithms that range from simplistic to hyper complex. One area that typifies the assortment of information sources in a modern manufacturing setting is found with the use of industrial robotics and automated manipulators. Although several monitoring methods and technologies have been previously proposed for this and other assets, adoption has been sporadic with returns on investment not always meeting expectations. Practical concerns regarding data limitations, variability of setup, and scarcity of ground truth points of validation from active industrial sites have contributed to this. This paper seeks to provide an overview of barriers and offer a feasible action plan for developing a practical condition monitoring information utilization program, matching available capabilities and assets to maximize knowledge gain. Observations are made on real world case study involving industrial six Degree of Freedom (DOF) robots actively deployed in a manufacturing facility with a variety of operational tasks.

Entities:  

Keywords:  Diagnostics; Machine Learning; Maintenance, Manufacturing; Monitoring; Operations Management; Robotics

Year:  2019        PMID: 31555021      PMCID: PMC6760008          DOI: 10.1007/s00170-018-03263-z

Source DB:  PubMed          Journal:  Int J Adv Manuf Technol        ISSN: 0268-3768            Impact factor:   3.226


  1 in total

1.  A review of diagnostic and prognostic capabilities and best practices for manufacturing.

Authors:  Gregory W Vogl; Brian A Weiss; Moneer Helu
Journal:  J Intell Manuf       Date:  2016-06-09       Impact factor: 6.485

  1 in total

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