Machine Vision 4 Users
Machine Vision and Image
Microscan Tips and Tricks
My View by Andy Wilson
Vision Systems Spain
It seems as though everyone in our business has seen a flurry of activity within the first quarter of 2016. Our business is up across technology segments, vertical markets, and geographies. 2016 is shaping up to be a banner year for Microscan and the automation market overall. It's an incredible time to be offering and selling automation products and solutions...
The Marketing team loves seeing our Partners recognized for their innovative and successful solutions to customer challenges. I wanted to call out eSolutions for recently being published in Vision Systems Design for their out-of-the-box approach to forestry automation. Pike Lumber asked eSolutions to develop a vision system for automatic log diameter measurement of trees that have already been cut down but still need to be cut into lengths and have their limbs removed.
In this example, eSolutions developed a way for operators to automatically measure timber logs without leaving the inside of the loader cabin. A 5-megapixel Visionscape CCD GigE Camera was mounted to the exterior of the Tigercat tree loader which gives the operator the board feet measure reading (the length and diameter and thus how many boards can be manufactured from a given log). In the past this measuring process was done by hand and took a lot of time and heavy manual labor, and wasn’t always accurate.
Insights about OCR Vision Applications with CIJ Printers (Part 3 of 3)
This article is the third of three articles about OCR vision applications with continuous inkjet printers (CIJ). In this third article, you will learn about the different vision solutions possible with Microscan vision technology. AutoVISION Software incorporates many possible vision tools to choose from, including OCR algorithms with trainable characters, as well as vision tools that are simpler but can be highly effective in the inspection of CIJ print. So how do we know which strategy to follow? In some cases, simple pixel-based algorithms can solve many of the common printing problems of a CIJ printer setup. In some cases pixel-based tools are more effective than OCR tools.
Later on I’m going to describe the machine vision concepts of each approach. I will also point out the advantages and disadvantages of each method. I will provide guidelines for when to use each strategy, and when one strategy may be more effective than another strategy. Once you have this knowledge you will be able to create powerful vision inspection systems for high-speed production lines with CIJ printers using simple components that allow for a rapid yet highly effective design process.
Below are listed various potential vision solutions that are used to solve inline inspection applications challenges of CIJ-printed characters at high speed. They are divided into three primary groups:
This is the second of three articles about OCR vision applications that use CIJ printers. In the first article I discussed the typical print output achieved with CIJ printers at high speed. I showed real images of print examples and showed the high degree of variability among similar characters printed by the same printer head.
Once you understand the nature of printer output on high-speed production lines, and have noticed the high variation between one print to the next print, you can begin to identify possible solutions. But before you design your OCR vision solution, you should become familiar with the common defects that are relevant to the specific line you intend to automate. Factory quality managers, technical staff, printer technicians, and any other factory staff who keep records of production errors can help you to identify the common defects on any given production line. The defect identification stage is the most critical. Your goal is to understand the real needs of the customer. Many times the customer needs professional help in order to know what real problems he or she is facing on the production line. It’s not always immediately easy to pinpoint defects and other problems, but there are reliable ways to do so.
Some helpful questions to ask quality managers or other technical staff during this critical defect identification stage are:
In industrial manufacturing, last thing you want to deal with is unreadable barcodes interfering with production. Decoding failures may require downtime for diagnostics and equipment adjustments, which results in unforeseen costs, lost opportunities to meet business objectives, and a lot of frustration. After all, using barcodes in production is supposed to streamline operations, not complicate them! But the fact is that even after you go through the checklist of standard to-dos to prepare your operations with quality barcodes and ideal barcode-reading conditions, some circumstances may be beyond your control and unpredictable no reads may still result.
But help is available. Instead of focusing on the condition of your barcode or the setup of your operations, you may be able to attack barcode readability issues from the barcode reader itself by using more powerful decoding algorithms. In this blog post, I’ll offer some advice about how you can dedicate less of your limited resources and valuable time to barcode reading and more to getting business done.
Insights about OCR Vision Applications with CIJ Printers (Part 1 of 3)
One Monday morning I find this email in my mailbox sent by a customer:
‘Hi Eldad, how is it going? Can you help me with this application: I have a CIJ printer installed on a high speed production line. Several lines of text are printed on each product. I need a vision system for inline inspection of the printed characters. I try using OCR tool, then compare the data read by the camera with a data string sent from the printer and finally generate a warning output signal when mismatch occurs. I find it hard to train the system with one font library to solve all products. I still have lots of false rejects, where the camera rejects good products. I’m on it for several days and still cannot make it work – can you advise?’
As technology advances, manufacturing is becoming more and more automated. Robots are becoming the standard in most manufacturing lines that require fast, repetitive, precise placement of components. Many other types of automated equipment are being used for inspection to ensure components are placed in specified locations, check for missing components, and ensure fluid levels are at the exact level. Traceability information is collected by reading barcodes on parts in production so Operations knows exactly where every product in the manufacturing plant is at any given time and where each product has been. In order to achieve this type of automation we embed devices like machine vision cameras to give equipment eyes for visual inspection, and auto ID imagers and laser scanners to allow equipment to trace product through the manufacturing process. When developing your automated equipment it is imperative to choose a machine vision system, auto ID imager, or laser scanner that fits your precise requirements. There are five things to consider when choosing an embedded machine vision camera, auto ID imager, or laser scanner for an application: barcode type and orientation, inspection parameters, application speed, integration space, and data communication needs.