Empowering Automation

Over the next decade, manufacturers are expected to replace workers with industrial robots due to the latter’s falling cost, according to a report by Reuters in Feb 2015. In the US automotive sector for instance, the cost of deploying a spot-welding machine is US$8 per hour, compared to paying US$25 for a human operator. Robots currently carry out about 10 percent of manufacturing tasks that can be performed by machines. The Boston Consulting Group predicts that this will rise to about 25 percent by 2025. 

Need for vision

The production of vehicles is a complex task that involves the manufacture and assembly of various parts and components. From the external bodywork to intricate mechanisms like transmission assemblies, machine vision serves as ‘eyes’ for the robots that put them together.

Traditionally, manual labour is required for welding car side panels together. Metal sheet parts both big and small, must be loaded into the robotic welding cell with precision. After welding is complete, the number of welds and their locations are manually checked by visual inspection. Finally, an operator removes the side panel from the inspection station and places it into a transport frame. These manual handling and visual inspection operations have been difficult to automate. This was because there were challenges for a robot to locate the bulky side panels accurately enough, to pick them up.

With the help of a 3-D vision system however, automated racking, de-racking and inspection of side panels in a supply rack is now possible. The system locates part position, identifies part defects, and sends the data to the robot controller. The robot controller utilises this real-time data from the vision system to adapt the robot’s approach to the part’s position in 3-D space. One robot collects small parts from a component magazine. A second one then picks up large components from a transport rack. The two robots move to the welding station and place the parts in the correct positions on the turntable.

Once the welding process is completed by the spot welding robot, the first and second robots inspect the spot welds using a camera. In this manner, the number, location and accuracy of the spot welds are accurately determined. Finally, the second robot removes the welded side panel from the welding station and delivers it to a transport frame.

Thorough checking

In the manufacture of automotive transmissions, assembly lines have to accommodate a variety of different parts. These include clutch packs, valve bodies carriers, constant velocity joints, seals, pistons and snap rings. Accuracy is vital, as a missing part or an assembly defect could impact vehicle performance and/or result in an accident. Each assembly step therefore, requires inspection to ensure that the correct parts have been used; and that they adhere to tolerance requirements.

To meet these needs, machine vision can be deployed to perform transmission assembly checks. The In-Sight vision system is equipped with powerful patented vision tools and does not require programming for configuration. Instead, it uses EasyBuilder, a graphical configuration environment that makes gauging, inspection, guidance and identification applications simple to set up and deploy. The vision system can be integrated into production line automation systems with ease.

Essential communications

Connectivity is essential in facilitating data sharing and supporting decision making. Networking allows the system to transmit pass/fail results to PCs for analysis, or for direct communication with Programmable Logic Controllers (PLCs). PatMax software is also used. With its advanced geometric pattern matching technology, parts can be reliably and accurately located. Even under extremely challenging conditions, the software can significantly reduce or eliminate the need for fixturing.

One major challenge in terms of vision system deployment however, is the conditions of the production environment. Oil, dust, moisture, and vibration are all variable factors that could affect the vision system’s performance. To deal with these issues, robust camera enclosures, periodic lens cleaning and the selection of high flex cables (if the camera is moving) may be required.

Fortunately, In-Sight vision systems are designed to operate in harsh industrial environments. They are housed in rugged die-cast aluminium cases, stainless steel enclosures and have sealed M12 connectors. These together with their IP67 and IP68 rated lens covers, allow the vision system withstand vibrations and to provide the necessary protection against dust and moisture.

Lightening the load

Manually torqueing lug nuts that attach a wheel to a Wheel Hub Assembly (WHA) is a usually a difficult manual operation. Nut runners are typically used to apply torque and tighten the nuts; and two wheels are completed in about 40 seconds. Since nut runners are normally heavy and large, this application has been challenging to automate. To further complicate matters, vehicles that are sent to the torquing area do not always arrive in the same positions; and the wheels are allowed to rotate and tilt.

With the help of machine vision however, robots are able to locate and fasten the nuts without any intervention by an operator. The vision system first determines the positions of the nuts in five different axes. PatMax pattern matching technology is then applied to quickly locate the wheel. A circle finder tool is utilised to precisely determine the location of the centre of the axle. A crosshair on each wheel is created by two laser lines and images of each crosshair are acquired from multiple angles.

The variance in positions of the two crosshairs is calculated to determine the angle at which the wheels are turned and tilted. Once this is achieved, the robot moves the nutrunner to tighten the nuts.