Recent Visitors

Dr. Arvind Raman, Purdue University

Dr. Sachin Goyal, Mechanical Engineering, University of Michigan

Prof. Brian Mann, Mechanical & Aerospace Engineering, University of Missouri

Robert J. Webster III, Ph.D. Candidate in Mechanical Engineering, Johns Hopkins University

Prof. Scott David Kelly, Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign

Dr. S. C Saxena, Director, IIT Roorkee

Jan 2007-Professor Devendra P. Garg honored in New Delhi, India, by being presented the Hind Rattan (Jewel of India)
Award by the Non-Resident Indians (NRI) Welfare Society of India.

Oct 2006 - Acquired 8 Khepera II robots from K-team corp.

Fall 2006 - Abhishek receives his MS degree in Mechanical Engineering

Fall 2005 - Abhishek joins the RAMA Lab as a MS student and Research assistant

Jan 2005 - Brian Dieckmann joins the RAMA Lab as Pratt Undergraduate Fellow

Nov 2004 - Dr. Manish Kumar presented three jointly coauthored research papers at the 2004 IMECE in Anaheim, CA

Nov 2004 - Dr. Manish Kumar joined the RAMA Lab as a Post doctoral Associate

Oct 2004 - Dr. Garg received the Scientific Research and Leadership Award at the 2004 Heritage India Festival

Sept 2004 - Dr. Garg delivered invited lectures on robotics at the Indian Institute of Technology/Roorkee and Indian Institute of Technology/ Delhi, India

Aug 2004 - Ram Parimi recieved his M.S. degree in Mechanical Engineering

July 2004 - Paul Nesline is the new webmaster for the RAMA LAB

June 2004 - Dr. Prem Vrat, Director of Indian Institute of Technology Roorkee visits the lab

June 2004 - Dr. Masayoshi Tomizuka, Director of the Dynamic Systems and Control Program at NSF visits the lab

Summer 2004 - Nsi Obotetukudo from Case Western Reserve University joined the RAMA Lab for 10 weeks under the NSF/REU Program

Summer 2004 - Adam Schmelzer joined the RAMA Lab as Research Assistant

May 2004 - Manish Kumar received his Ph.D. degree in Mechanical Engineering

May 2004 - Congratulations to Brian Schaaf and Chris Dillenbeck on their graduation with BSE degrees

April 2004 - William Chandler Salinger joins the lab as an Intern for four weeks

April 2004 - Ram Parami presented "Intelligent Control for Mobile Robot Navigation" in the Graduate Seminar Series

Flexible Workcell Simulation

The goal of manufacturing systems today is to fulfill the ever growing demands of customers, which requires a greater amount of flexibility in the manufacturing process. Innovative production approaches are needed that will allow companies to deliver a large variety of products at a low cost and with short production cycles. Flexible manufacturing work cells provide excellent opportunities for enhancing both efficiency and productivity in an automated manufacturing environment.

Flexible manufacturing work cells have minimal downtime and maintenance, maximum product family range, the ability to adapt to variability in materials and process conditions and the ability to handle increasingly complex designs and technologies with minimum disruption and at reduced cost levels. Such cells typically integrate robots, a wide variety of machine tools, material handling equipment, packaging devices, sensors, actuators, controllers, and similar other hardware.

Flexible work cells use high level distributed data processing and automated material flow, which is enabled by highly flexible, computer controlled material and information processors within an integrated, multi-layered feedback architecture. In order for flexible work cells to become economically viable they must 1) reduce the Manufacture Lead Time, (MLT), or total time required to manufacture a given part, and 2) the partially completed inventory. Ideally, a flexible work cell is aimed to minimize four specific key idle times that comprise the MLT:

i) the setup time for machines to adapt manufacturing from one part to another,

ii) the process (or manufacturing) time of those machines operating on the part being machined,

iii) the move time from one machine to another, and

iv) the waiting time for a part to be taken up to the next process.

Maintaining high cost performance is important because flexible systems are apt to generate idle machining time. The speed at which various cells operate and the close scheduling of processes are crucial in minimizing machine idle time. The flow of work materials for automatic manufacturing systems, with small lot sizes is not fixed, while that of mass production is; hence, the idle time is a variable that can be optimized.

Recent advances in hardware and software have made flexible manufacturing systems capable of handling relatively complex tasks with a modest and affordable investment. Much of the credit for this development lies with the advent of fast microprocessors, and sophisticated robotic manipulators and end-effectors. The use of multiple robots and their coordination via intelligent control strategies has expanded the capabilities of flexible manufacturing to satisfy a wide spectrum of industrial applications. A response to changing market requirements, product designs, and technological developments can be easily accommodated.

Moving parts from one place to another and loading and unloading machines automatically has been an important and crucial aspect in flexible manufacturing systems. Currently, it is feasible to handle the movement of parts during manufacturing very efficiently using the existing technology. As the motion of parts progresses along the manufacturing line, the vision sensor system identifies them via a bar code or a magnetic tag attached to the part. By the time the part reaches a machine tool, it is picked up by a robot and is properly positioned to be worked upon by the tool. The currently available vision systems are quite versatile. They can easily accommodate identification of fast moving parts, misoriented or mispositioned parts, or parts yielding blurry images.

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Any questions about Duke Robotics? Email Abhishek