Stringent requirements are part and parcel of automotives manufacturing. Any inefficiencies in fulfilling them can result in delays in production, and/or waste that needlessly uses up material, human and financial resources.
Companies therefore, have mechanisms in place to perform inspections on work-in-progress and completed products. Often, human operators are deployed to manually inspect items on the production line for faults and problems.
The downside with this approach is that certain tasks can be extremely challenging for operators to carry out effectively. For example, the human eye is limited in its ability to identify tiny faults that are a fraction of a millimetre in size.
Besides, the quality of inspections tends to vary between individuals, due to factors like experience, age and fatigue. This makes it virtually impossible to maintain consistent standards for quality assurance.
A better alternative is for manufacturers to invest in reliable technology that can effectively root out mistakes and sub-par product early on in production. Machine vision offers such an avenue.
A factory that manufactures vehicles has four manufacturing departments. These are pressing, sheet metal, paintwork and assembly; along with eight departments and sections carrying out support functions.
At the pressing workshop, sheet metal parts are produced for making the body shell of the vehicles. It mainly comprises of cutting lines, assembly and sheet welding points, as well as sheet presses which make up the various bodywork components.
The pressing workshop supplies the sheet metal workshop, which carries out the assembly of the various parts of the body shell. These items are then moved to the paint shop before final assembly. Daily production capacity of the facility is 1,900 vehicles.
During production, defects can appear around the welds which need to be detected. While the various pressing operations — which give the part its shape — are being carried out, clear or partial breaks can occur. As the parts are stored manually at the end of the line, those parts showing such faults are easy to spot and remove.
If the faults are small holes however, these become more difficult to spot. Some of them measure less than three-tenth of a millimetre, which form along the bead of the weld. If the presence of these holes is not detected at the end of the line, and if the part is not removed, breaks can occur during the shaping process. This causes numerous problems which can have serious consequences on the productivity of the assembly line.
Sometimes, even minute holes can give the bodywork an unsightly appearance after the paintwork has been completed. Left undetected, such defects can lead to waste that is both expensive and inefficient. Being difficult to repair, there have been instances where the chassis had to be sent to the scrapyard.
In the case of a particular Cognex client, the checking of weldings was previously carried out by several operators who had to struggle to handle parts of a considerable size and weight: the sides of the body shell measure around 3.4 m in length, 1.6 m in width and weighs nearly 30 kg.
To carry out the operation, the operators used to place a light source on one side of the part and visually inspect the other side to determine if the light passed through. This would indicate breaks or holes in the part. This form of control, even when it was performed in the most optimum manner, could not identify holes that were of a very small diameter.
As a result, numerous defective parts passed through these checks. A solution capable of resolving this problem therefore had to be found— one that would carry out a continual and reliable check of the cars’ lower body shells.
To alleviate these issues, vision systems were considered as they were a better alternative to other control procedures. The technology is able to simplify and enhance the effectiveness of certain tasks that were normally carried out by operators. Unlike human-eye inspections, vision systems are not affected by fatigue and can operate continuously to ensure quality and improve productivity.
After looking into several vision systems, In-Sight vision sensors were chosen for the operation. A prototype was tested and tests carried out were 100 percent correct as the sensors detected all the faults.
Furthermore, the system demonstrated that it was more than capable of detecting holes with diameters three-tenth of a millimetre. The first test bench was put into service, while a second one with smaller dimensions was ordered later for checking the side frames of several vehicles on another line. Each test bench has four vision sensors.
The first test bench is a metal frame measuring about five metres long and two metres wide. It weighs five tonnes, with a five centimetre thick platform holding the structures on which the parts to be checked are placed.
The bench is equipped with a backlighting system with Light Emitting Diode (LED) indicators. The test bench is put in place by a bridge at the end of the pressing line each time the manufacturing process of the car’s lower body shell is started.
Four cameras, each placed in protective casing are positioned at the top, over the platform on the cross support beam. A control screen is fixed on one side of the test bench, in a box where the control system is also installed. A marking system for defective parts is fitted at the other end.
The vision system is tasked with checking the right and left sides of the body shell of cars. As each side of the body shell is made up of two parts which have been assembled by laser welding, the system therefore has to check four different parts.
The system automates the checking of welds, and the principle of the inspections relies on the backlighting of the area to be inspected. In this regard, the camera has to first determine if it can see rays of light passing through the welded area.
The tools for processing the images acquired must also be precise and reliable, so as to recognise the entire variety of different faults. This includes those that are difficult to detect with the naked eye. Another criterion is that it is able to carry out a 100 percent check without reducing the speed of production.
Operators initially had to teach the system to recognise the faults were to be identified. This task was progressively carried out as they familiarised themselves with the system. Once the system had memorised all types of faults, the recognition rate was 99.99 percent.
The main concern however was the effects of ambient lighting that had to be overcome. Because of the existing lighting in the workshop, the layout of the area (glazed surfaces) and the orientation, reflections appearing on the parts occasionally disrupted the operation and showed up as faults. Placing curtains at the end of the line eventually resolved this problem.
The body shell sides are put onto the supports on the test bench by a robot. There are two areas that need to be checked. The target areas measure approximately 10 cm by 5 cm with a covering. Two of the cameras are inclined at an angle of 45 degrees, the part forming a U-shape over the welded area.
The cameras are able to detect very low light levels coming from the backlighting system and passing through any holes, some of which measure only one tenth of a millimetre in diameter.
If the part is deemed acceptable, this information is displayed on the control screen. In the event that the part is defective, it is also displayed on the screen and a red light comes on at the end of the line for each fault found. The part is then marked by a jet of ink which indicates that it may not be used.
The speed of the Quality Control (QC) carried out on the line is an important determining factor of the project. All the operations—from positioning the part, capturing and analysing the image, to detecting and marking—should not slow down production.
The production rate is 420 parts per hour for the body shell sides and 850 parts per hour for the side frames. This is well within the capabilities of the system, which is able to perform at a rate of 900 parts per hour. It allows for higher production capacity, should the need arise in future.
The system is connected to a PC that is fitted with a hard drive for saving the photos of the detected defective parts. This procedure allows any problems encountered to be analysed at a later date.
It also helps to ensure the traceability of parts and the detailed monitoring of what goes on during each work shift. For example, it describes the nature of the problem that was detected by camera one on a particular day and time.
As a result of successful implementation, welding checks are now reliably conducted on the entire production process (body shell sides and side frames). Defective parts are swiftly identified and removed at the end of the pressing line.
With the success of the vision system, the facility no longer has to send entire body shells to the scrapyard. At the same time, the savings made at this stage alone can fully justify the investment. This method of checking welds with industrial vision tools is of interest to other sites and is likely to be implemented there as well.
The profitability of a production facility is often closely linked to the efficiency at which it runs. Through the use of effective vision technology, errors are quickly detected earlier on in the production cycle and before further processing can take place.
Through this non-human eye dependent method, every product on the manufacturing line is judged consistently and impartially to ensure that it meets the standards of the marketplace. As a result, waste is kept low while profitability increases.
At the same time, the industry can reap the rewards of better and safer products that benefit both manufacturer and consumer alike.