{"id":167,"date":"2016-11-07T10:52:49","date_gmt":"2016-11-07T15:52:49","guid":{"rendered":"https:\/\/cecas.clemson.edu\/~egallag\/?page_id=167"},"modified":"2019-09-07T17:42:11","modified_gmt":"2019-09-07T21:42:11","slug":"research","status":"publish","type":"page","link":"https:\/\/cecas.clemson.edu\/manufacturing-lab\/research\/","title":{"rendered":"Research"},"content":{"rendered":"
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Human Integration to the Internet of Things<\/h3><\/span>
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The scientific theme of this research program is\u00a0investigating connecting the human element with the\u00a0emerging digital manufacturing enterprise (i.e.<\/em>,\u00a0Industrial Internet of Things), quantifying physical and\u00a0mental human behaviors and their uncertainty, and\u00a0investigating the effect of human-integrated digital\u00a0technologies as both information generators and\u00a0feedback mechanisms on attitudes and performance in\u00a0the manufacturing enterprise.<\/p>\n

The objectives are to first\u00a0understand the quality of information that becomes\u00a0available through human-based data capture and\u00a0analysis technologies, including uncertainty\u00a0distributions, then investigate means of feeding back\u00a0digital information to human agents in order to assess\u00a0the effect on human performance and the overall\u00a0manufacturing system metrics.<\/p>\n<\/div>

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Energy-Assisted Forming and Joining<\/h3><\/span>
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This research addresses limitations of current forming, machining, and joining processes through energy augmentation. Electric current supply is incorporated to equipment and fixture designs, and controls developed to actively modulate metallic material properties during processing. Results are the ability to process thicker, stronger materials at a higher rate with improved quality control.<\/p>\n<\/div>

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\"Okuma<\/a><\/span><\/div>
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Machine Tool Planning and Control<\/a><\/h3><\/span>
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In this area we model process parameters\u2019 effect on wear and cutting forces, and the subsequent cumulative effect on the plastic deformation zone depth (which we term the Machining Affected Zone<\/em>, MAZ<\/em>) through a stochastic representation approach, whereby model parameters are represented not as scalar constants but rather as random-variable belief distributions. We use optimal state estimators and a Bayesian updating approach for modifying the model beliefs based on limited observational data. The resultant models are used in a numeric milling simulation to predict the shape and evolution of tool wear, and stochastic beliefs on stability. The sum of the knowledge generated is applied to deriving robust tool paths for improving the consistency and performance of the process.<\/a><\/p>\n

A unique aspect to the experimental validation side of the investigation is our approach termed contrived tool wear<\/em>. With this approach, the nominal wear shape under a given set of conditions is ground into a new set of cutting tools, so repeatability of the worn tool force and MAZ depth model can be decoupled from the tool wear variability itself. Through this approach, we gain insight into the force, machining affected zone, and stability progression independent from the tool wear variability.<\/a><\/p>\n<\/div>

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Research Philosophy<\/h3><\/span>
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The SmartState Lab’s research centers on estimation of manufacturing process states and the inclusion of physical and phenomenological models of manufacturing processes to process control. In exploring this line, I focus on control of processes, control of systems, associated sensing to gather system information, and development of new processes.<\/p>\n

A key area of interest under this theme is in integrating the human element to the emerging Internet of Things. Novel sensors, feedback systems, and signal and image processing approaches can effectively make the human part of the control loop of manufacturing, rather than an external observer.<\/p>\n

Finally, the lab uses data and models from experimentation to understand limitations of a process and how that restricts the ability of designers to be creative. We initiated in 2013 the Manufacturing for Design methodology, which essentially uses product designer creativity to motivate new approaches in manufacturing. This has led to process augmentation such as Energy-Assisted Manufacturing, and new assembly architectures.<\/p>\n<\/div><\/div><\/div>

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