Pictorial Information Management in Manufacturing Systems

  • Rajiv Mehrotra
  • William I. Grosky
Part of the Advanced Manufacturing Series book series (ADVMANUF)


The data acquired by the ever-increasing utilization of a variety of sophisticated sensors in the automation of industrial and manufacturing tasks have attracted attention of the scientific community to the problems of storage, analysis and management of nontraditional data. Sensors can be and are being used for acquiring data necessary for automatic and intelligent control, analysis and decision-making for a number of industrial/manufacturing operations. Some industrial tasks that need sensor data include monitoring the progress of operations, inspection and recognition of tools/products, eye-hand coordination in assembly robots, automatic handling and sorting of material, monitoring wear and tear of tools during operation, automatic setting of machines (loading and unloading tools), diagnostics and maintenance of equipments, and autonomous vehicles, to name a few. In a large number of sensor data-based industrial operations, the collected sensor data are required to be stored for further analysis. For instance, sensor data related to tools’ wearing and tearing and the product inspection data can be used to investigate the relationships among the tool defects and the product defects. Similarly, the sensor data gathered by monitoring a metal cutting operation can be used to study the chip curling and breaking behavior, which could lead to better metal cutting techniques. In some cases, the acquired sensor data are needed to retrieve stored data for determining the task or a sequence of tasks required to be performed. For example, in a completely flexible and automatic assembly system, sensor data are used to identify and determine the poses of one or more specific parts on the conveyor belt.


Query Processing Query Image Textual Data Database Management System Intelligent Manufacture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Rajiv Mehrotra
  • William I. Grosky

There are no affiliations available

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