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Feature Articles

Web Scanners

by Nello Zuech, Contributing Editor - AIA


Web scanners to inspect unpatterned webs have been in widespread use for many years. As far back as the mid-1960s several companies had internally developed this capability and not too long after, several companies were offering commercial products. These early systems used lasers in a flying spot scanning mode. Today one finds line-scan based systems and area-camera based systems competing with laser-scanner based systems.

Web scanners (sometimes referred to as web imaging systems) are routinely deployed in primary metals, paper, photosensitive film, plastic, ‘‘green’‘ state electronics, textiles, nonwovens, tire fabric and glass industries; thirty-five discrete industries altogether. The litany of conditions detected by these systems includes holes, spots, scratches, stains, streaks, inclusions, bubbles, pitting, inhomogeneities, indentations, coating bubbles, coating omissions, etc.

Concerns detected can be described as either geometric flaws (scratches, bubbles, etc.) or reflectance (stains, etc.). The capabilities of systems vary. Some arrangements are only able to detect high contrast flaws such as holes in the material with back lighting, or very dark blemishes in a white or clear base material. Some arrangements are transmissive, others reflective, others a combination.  Some arrangements are only able to detect flaws in a reflective mode that are geometric (scratches, porosity, bubbles, blisters, etc.) in nature; others only those based on reflectance changes. Often systems are based on either light field or dark field lighting arrangements.

Some systems only have detection capability, others have ability to operate on flaw image, and develop descriptors and, therefore, classify the flaw. These latter systems lend themselves to process variable understanding and interpretation and ultimately automatic feedback and control.  As machine vision techniques improve in image processing and analysis speeds, there will be opportunity to substitute these more intelligent systems for those earlier installed with only flaw detection capability.

The actual resolution required for defect detection in web imaging-based systems is dependent on the characteristics of the defect and background. It is generally agreed that subpixel resolution is of no consequence to detection of flaws but related to ability to measure flaw size. The size flaw one can detect is a function of contrast developed and ‘‘quietness’‘ of background. Under special conditions it may be possible to detect a flaw smaller than a pixel but one cannot measure its size.  Nyquist sampling theorem, however, suggests reliable detection requires a flaw be greater than two photosites across in each direction.  Using this ‘‘rule of thumb’‘ alone usually results in unsatisfactory performance. 

The problem is that flaws do not conveniently fall across two contiguous pixels but may partially occlude several neighboring pixels, and in fact only completely cover one pixel.
Significantly, flaws that exhibit low contrast under one set of lighting conditions can be frequently exaggerated under another set of lighting conditions. The result is that often more than one detector/lighting arrangement is required to approach a comprehensive inspection capability. The speeds of the webs further compound the compute requirements.  The more computation required, inevitably the more expensive is the system.

Additional detector/lighting arrangements are also often required because both sides of a material have to be scanned, or the web is so wide it requires more than one scanner head to inspect the entire web with the resolution demanded by the application. All these considerations exacerbate the pricing.

There are several issues that strongly interact: ability to uniformly illuminate wide areas, sensor/data acquisition, compute power and requirements in terms of being able to measure and classify in addition to detect.  To see smaller detail in the same sized product requires sensors with more pixels. To handle higher speeds requires faster data acquisition. This in turn effectively reduces the photosite signal which, especially when low contrast changes have to be detected, results in poorer signal-to-noise.  In other words, the quality of the input going into the computer is less reliable. Hence, as line speeds increase, brighter lighting arrangements are required.

Line scan cameras are now available with up to 12,000 pixels and systems using line scan cameras with 8000 pixels are being routinely deployed for web scanner applications. Even 8000 pixel line scan TDI cameras are available. CCD and CMOS versions are now available. Multi-tap versions offer pixel readout throughputs upwards of 160 MHz. Speeds are a function of pixel count. Up to 12-bit outputs are available. Color and monochrome versions are available. Interfaces available include Gigabit Ethernet, Camera Link and LVDS formats. CCD and CMOS area cameras are also now available in megapixel resolutions – up to 4096 x 4096 are in widespread use. Again color and monochrome versions are available as well as versions with Gigabit Ethernet, Camera Link, IEEE-1394 (a) and (b) as well as USB 2.0 interfaces.

Concurrent with the advances in camera technology has been advances in image processors that often have the capacity to handle appropriate algorithms compatible with web speeds up 2000 FPM. Several companies now offer the equivalent of a general-purpose machine vision system for web scanner applications in a smart camera implementation format. Embedding FPGAs, DSPs and microprocessors, they can execute the critical algorithms on the fly. They also have the ability to effectively operate on images at far greater resolutions than display technology can handle.

As the technology has advanced, systems are now available that not only detect flaws of one type or another but also classify them. In some industries the nature of a flaw is related to a process breakdown. Classification results in increased value resulting as it does in the ability to make corrections to the process quickly to minimize scrap. Linear array and area array-based systems can operate on the stored image of the defect and use a combination of geometric and photometric descriptors as the basis for defect classification. Ultimately, better classification should yield gains in the ability to correlate defects to process breakdowns that in turn should yield better process control.

Commercially available area and line scan smart cameras and high end image processing boards make it possible to enter the web scanning market with a relatively low entry cost. This has made it possible for system integrators to enter the market competing head-to-head with companies who offer turnkey systems. The challenge with independent merchant system integrators is their lack of process experience. So it is an interesting dichotomy. While the technology available today can provide robust solutions, the lack of industry familiarity and specific application experience can frustrate a customer who inevitably buys mostly on price.

As the technology and application know-how improve, applications can be satisfied more comprehensively and/or more applications can be considered feasible. The products on the market today include low cost line scanner based machine vision systems that sell for under $10K and can be adapted to an application by either an internal or external systems integrator. These systems can generally reliably see onlyhighcontrastdefects(>20% changes from the background).

The next level of product is an application specific integrated solution that again only sees high contrast defects. These sell for $25K-$50K.  These generally have a limited ability insofar as classification is concerned.

The next level of product generally has the ability to perform some computationally intensive processing and generally a little more robust classification and/or inspect textured surfaces or surfaces with irregular formation patterns. These generally sell for $150K+ with price depending on web widths, speed, etc.

The main reason given for the adoption of web scanner technology is to improve quality by way of better process control. As customers become more demanding and are frequently willing to pay more for a better grade, the ability to detect concerns quickly so as to assure delivery of a better grade means a better bottom line. This is especially important where the end use application of the web product has a safety concern.

In some cases, such as coating operations, there is an opportunity to inspect the web before and after coating; before to avoid adding value to an inferior product or first cutting out bad product and after to verify the integrity of the coating process.

Twenty-seven companies identified as selling web scanner products in the United States are:
Twenty-seven companies were identified as selling web scanner products in the United States.  The following table1 depicts where these companies purport to have experience according to their websites.

The ability to perform a comprehensive inspection and defect classification on webs is dependent on specific industry requirements and in some cases may be still limited.  Each industry/application has product/process specific requirements.  Available technology bases defect detection largely on contrast changes.  Frequently, special lighting/sensor arrangements will exaggerate the presence of what would otherwise be low contrast flaws.  Often flaws that are visible to the eye are essentially buried in the signal.  Extracting such low contrast flaws in busy backgrounds requires computationally intensive algorithms.



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