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This profile was last updated on 12/20/2014 and contains contributions from the  Zoominfo Community.

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Adil Shafi


Advenovation Inc.

HQ Phone:  (248) 236-3000

Direct Phone: (734) ***-****direct phone


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I agree to the Terms of Service and Privacy Policy. I understand that I will receive a subscription to ZoomInfo Community Edition at no charge in exchange for downloading and installing the ZoomInfo Contact Contributor utility which, among other features, involves sharing my business contacts as well as headers and signature blocks from emails that I receive.

Advenovation Inc.

3939 West Hamlin Road

Rochester, Michigan,48309

United States

Company Description

ADVENOVATION, Inc. specializes in the new frontier of robotic bin picking as well as traditional vision guidance and inspection applications. A range of products and services are available such as feasibility studies, programming, process development and turnk...more

Background Information

Employment History


Shafi Inc


Robotic Industries Association

Board Member



Computer Science and Electrical Engineering

Michigan Technological University

Web References(94 Total References)

How Robots Create Jobs [cached]

by Adil Shafi , President, ADVENOVATION, Inc.

Machine Vision for Robot Guidance [cached]

"Vision guided robotics systems matured in the two-dimensional arena first," says Adil Shafi, President of ADVENOVATION Inc. (Rochester Hills, Michigan).
Integrators, robot end-users, teachers, students, newcomers and experts, can learn about vision-guided robotics through a free webinar hosted by RIA and Shafi on Thursday, June 7 at 12:00 PM EDT. The Robots: Vision Guidance Technology (2D) webinar will cover basic concepts, good applications, product examples, flexible tooling, hard tooling, vacuum, vision and other related topics. "I will show application videos and a PowerPoint presentation to provide a background of two-dimensional vision guidance applications," says Shafi.

Vision for Service Robots is researched and written for Vision Systems Design by Adil Shafi, president of Advenovation and well-known authority on vision-guided robotics.
It is based on his extensive research as well as interviews with leaders in robotics at Waseda University, Stanford, Carnegie Mellon and MIT; researchers from Fraunhofer IPA, NIST and the Volpe Center of the US Department of Transportation; and experts at Willow Garage, iRobot, Intuitive Surgical, Hoaloha Robotics, RoadNarrows Robotics, Sony, Kuka and Qinetiq.

Robotics Featured Articles - Robotics+Vision at a Glance: The Do... [cached]

2D VGR AutoRacking for spot welding truck bed inners (Courtesy of Adil Shafi)
According to the inventor and installer, Adil Shafi, this was one of the first AutoRacking solutions in the automotive industry and is still in production today. "It was implemented at the Chrysler Twinsburg Stamping Plant in Ohio during the summer of 2001," says Shafi, President of ADVENOVATION Inc., a vision guided robotics innovation and systems integration firm in Rochester Hills, Michigan. "Since then, hundreds of similar solutions have been implemented in the industry." The application incorporates six ceiling-mounted Cognex In-Sight cameras (visible in the photograph) and a Nachi robot to rack the 6-foot or 8-foot-long truck bed inners. Six cameras are used to perform rack validation checks and two cameras perform linked field-of-view offsetting for proper part placement. "We had to integrate this solution in six weeks," says Shafi. "The challenges included having the cameras mounted 12 feet above the parts to avoid welding sparks, while viewing the 8-foot-long parts within 2-mm repeatability." Shafi says the system has been running for 12 years now, and over 50 more have been implemented in another 10 Chrysler plants.

Adil Shafi is President of ADVENOVATION Inc., a systems integrator in Rochester Hills, Michigan.
He says cycle time is one of the most important financial justifications in the business case for robotics implementation. "Every fraction of a second that something can be made faster translates directly into dollars," says Shafi. "Time is money." He also contends that optimizing cycle time should be a preemptive measure. "Cycle time is done well by design, not after the fact," says Shafi. "You have to be proactive." Selecting the right robot, strategically laying out the cell, optimizing robot movements and end-of-arm tooling design, and using the latest simulation techniques all provide a tactical advantage. Choose Your Robot Wisely Shafi says robot selection is critical. Bigger is not always better. While an ill-advised small payload robot Two articulating robots work in tandem to optimize cycle time in a bin picking application (Courtesy of ADVENOVATION, Inc.) may end up costing more in the end. "If a robot is struggling to pick up a part because it doesn't have enough strength or if it's oscillating when you're trying to place a part at high speed, the process will suffer," says Shafi. "On the other hand, you don't want to buy a robot that is so big and strong that you're spending time trying to overcome inertia when accelerating, or wasting time trying to settle it when it's moving at high speed." On scale with payload capacity is selecting the right type of robot for the mission. In broad terms, Shafi recommends the following: "If it's a tabletop pick and place application, then a SCARA is the right way to go. If the reach or tooling issues are such that a SCARA robot can't do it, then an articulated arm may be appropriate. If you're dealing with a flat conveyor tracking application, then maybe a spider (delta-style or parallel-link) robot makes sense." He adds, "The geometry of the application drives the choice of the robot." Shafi cites standard cycle time as a point of reference. 'Standard cycle time' is the time it takes for the robot arm to travel up 1 inch, go across 1 foot, travel down 1 inch, and come back through the same path. "It was a big deal in the '80s and early '90s when robots could do that in under one second," says Shafi. "Now, it's quite commonplace. He notes that some delta robots can achieve up to 500 standard cycles per minute. "You can't even see the robot, it's going so fast." "It's important to minimize the Z height differentials between pick and place points, so that the robots don't spend extra time traveling," adds Shafi. "It's not just an artificial set of motions presented graphically on a screen," says Shafi. "Simulation now takes into consideration the actual mechanical characteristics of the robot and actual mechanical inertia, speed, reach and payload capacity." "What you can expect from simulation is very close to actual performance, within less than five percentage points when you run the system," says Shafi. "Because robots are not exactly symmetrical and they have nonlinear inertial characteristics, moving the robot even an inch or two in either direction can give you better cycle time." He adds, "Simulation studies will prescribe and recommend the best location for the robot relative to a feeder, conveyor or some other delivery system in order to get the best cycle time." "The important thing is to fly through the air very quickly and loosely, but when you're actually picking or placing, that's when you want to get a little slow and grasp precisely without shaking the robot," says Shafi. "A lot of companies use two types of speeds: program and monitor speeds," explains Shafi. "You want lightweight materials like graphite or aluminum rather than bulky steel," says Shafi. "A fancy gripper more than pays for itself in cycle time savings," says Shafi. "That's why people spend money on cycle time optimization, even with existing systems," says Shafi. "If they can eke out more throughput per unit time, that's substantial." Substantial, indeed. Milliseconds that add up to significant time savings, better throughput and higher profits. For more ways to speed up your cells, check out Adil Shafi's free webinar on cycle time optimization airing on December 12.

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