Due to the complexity of the operation, the size and type of threaded sleeves and tubing are varied, so no previous automation technology was used in the oil tool industry. Now JMP Engineering and a manufacturer in the oil industry have jointly developed a flexible automation program that uses two machine vision to guide the robot to handle a large number of workpieces, and is easy to configure and adapts to future changes without programming. .
The challenge of automated fuel tool production
The production process of oil tools usually produces a large number of workpieces, and the production capacity is relatively low, and often there are cases where the tolerance requirements for precise parts are not met. For these reasons, automation systems used in other industries are not the choice of the oil tool manufacturing industry. Flexible automation systems based on industrial robots have powerful capabilities, but face many challenges, such as the need to pick up unfixed parts, handle many different part numbers, and more. In the end, the current number of industrial robots used in the oil industry is extremely small.
JMP Engineering faces similar challenges in terms of automation processes. In this case, the thread protector is installed on the oil and gas pipeline to prevent damage during transportation. The output of the parts is relatively high, but the production of any one part has not yet reached the level that needs to be automated. Manufacturers want to assemble thread protectors at three speeds per minute. The task of installing the pipeline and the cap uses pneumatic tools, but the high torque generated poses a great challenge.
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Pick up parts from the basket
In the operation of picking up the components from the basket, the thread protectors are layered in the basket, and the baskets are separated by cardboard. The machine vision system is mounted on the robot arm for use. The vision system consists of a Basler Ace camera that uses the GigE Vision® standard for high performance industrial cameras and a frame grabber on Bechkoff industrial computers. The (LED) light inside the camera housing produces red light that overcomes the effects of ambient light on image acquisition.
JMP programmers use VB to write a graphical user interface for the workstation that can perform visual tasks by calling visual tools that may be in the VisionPro tool library. VisionPro offers pre-configured, highly integrated acquisition support for a variety of industrial camera and video formats. Cognex's QuickBuildTM application development environment enables configuration of acquisition tools, definition of visual tasks, and pass/fail determinations without programming. The VisionPro tool library includes tools such as PatMax ® , PatInspect ® , PatFlex ® , IDMax ® and OCVMaxTM to measure, guide, identify and inspect parts, even if the surface of the part is deviated due to manufacturing processes.
Traditional graphics matching techniques rely on pixel grid analysis programs, which we usually call the "normalized cross-correlation" method. This method finds the same degree between the grayscale model or reference pattern and a part of the object and the image, and determines the X/Y axis position of the object. Although this method is very effective in some cases, various surface changes often occur on the production line, such as the angle, size, and light of the object. In this case, the ability of this method to find a target is insufficient.
Cognex's PatMax geometry pattern comparison technique can learn the geometry of an object by learning a set of boundary curves that are not bound to the pixel grid, and then look for similar shapes in the image without relying on specific grayscale values. . This approach ultimately greatly increases the ability to accurately find targets in terms of size, angle, and light and shadow.
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