Machine vision tops sensors in flexibility for Ford body panel selection
Inside Machines: Ford improved flexibility by switching from sensors to machine vision for body panel inspection, increasing reliability, and improving quality and information flow. The noncontact machine vision inspection system avoids significant maintenance required with sensor replacement, easily accommodates new models and design changes, and reduces overall inspection costs.
Many threaded copper studs are used during final assembly to attach components to automobile body panels such as wheel wells. Automotive body panel inspections and bar code reading work better with machine vision than with a sensor array. Traditionally, the panels are inspected for the presence of studs by using a robot to present the part to an array of proximity switches. But the low mean time between failure of the switches and the need to install more switches whenever there is a new model or design change results in heavy maintenance costs. Ford has improved on the conventional method by using machine vision to inspect for the presence of studs and also read barcodes on the body. Machine vision systems are highly reliable and can handle new models or design changes with a quick change in the program.
The machine vision system Ford selected for this application offers templates for interfacing with popular programmable logic controllers (PLCs) and robots, easy programming, and a compact and rugged design well suited to the production environment at an affordable cost.
47 mpg rating
Ford invested $555 million in its Flat Rock Assembly Plant to build a state-of-the-art, fully flexible body shop capable of producing multiple vehicles. Ford added 1,200 jobs at the plant tied to production of the Ford Fusion and will continue to produce the Ford Mustang there. Ford is also upgrading the plant’s paint shop with an environmentally friendly 3-Wet paint process. The next generation Fusion offers a broad selection of fuel-efficient powertrains in the midsize car segment—two EcoBoost-powered gasoline engines, a normally aspirated four-cylinder engine, a hybrid, and a plug-in hybrid. The new Fusion Hybrid’s unprecedented 47 mpg EPA rating makes it America’s most fuel-efficient, nonrechargeable sedan. With each new major plant program, Ford is significantly increasing the flexibility of its equipment and facilities to build multiple vehicles at one location. By 2015, Ford will be able to produce 25% more derivatives per plant than 2011 globally.
As part of the drive to increase the flexibility of the Flat Rock Assembly Plant, Ford closely examined its current inspection methods. It’s critical to ensure that all studs are in place on body panels before they are attached to the vehicle body because assembling a panel with missing studs makes it necessary to interrupt the assembly process while the faulty panel is removed for repairs. The copper studs are assembled to the panels by stud welding guns that hold the studs in place and draw an arc between the stud and the body panel.
By 2015, Ford will be able to produce 25% more derivatives per plant than 2011 globally.
Proximity switches used in the past to inspect the studs had a relatively high failure rate because the studs on each body panel coming down the line can potentially bump the switches as part of the inspection process. Different models, variants, and design changes often use different stud layouts, so additional proximity sensors must be added for each layout. The traditional approach required considerable time from maintenance staff to replace failed proximity sensors and to add new sensors in response to design changes and new models and variants.
More flexibility, less maintenance cost
“We decided to switch to machine vision on this application to improve flexibility and reduce maintenance expenses,” said Scott Vallade, controls engineer for Ford. “We have many body panel inspection applications for the Fusion, so our goal was to find an economical solution that would address all of these applications. With the large number of applications, we were also interested in reducing implementation time by finding a tool that’s easy to program and can be customized with a standard input/output scheme that will work with all of the plant’s robots and programmable logic controllers to enable the integrators setting up each application to focus on the vision problem. We wanted an economical solution that could survive in the plant environment.”
The cameras selected are “the best match for our body panel inspection applications,” Vallade said, supporting many communication protocols. The vendor “set up a custom template that communicates with all the equipment in our plant so that our integrators can focus on programming the vision application.”
The vision systems include preconfigured drivers, ready-to-use templates, and sample code to accelerate system setup and ensure smooth communication with factory automation robots and controllers. Included are drivers, templates, and sample code for open standard industrial Ethernet communications protocols, such as MC Protocol, EtherNet/IP, and Profinet for connection to a wide range of PLCs and other automation devices from Mitsubishi, Rockwell Automation, Siemens, and other manufacturers. Preconfigured drivers, ready-to-use templates, and sample code are available for robots by ABB, Denso, Fanuc, Kawasaki, Kuka, Motoman, and Staubli.
|Search the online Automation Integrator Guide|
Case Study Database
Get more exposure for your case study by uploading it to the Control Engineering case study database, where end-users can identify relevant solutions and explore what the experts are doing to effectively implement a variety of technology and productivity related projects.
These case studies provide examples of how knowledgeable solution providers have used technology, processes and people to create effective and successful implementations in real-world situations. Case studies can be completed by filling out a simple online form where you can outline the project title, abstract, and full story in 1500 words or less; upload photos, videos and a logo.
Click here to visit the Case Study Database and upload your case study.