Brian W. Heil, Vice President – Sales
ISRA Surface Vision
4357 Park Drive Suite J, Norcross, GA USA
www.isravision.com
This article explores opportunities to maximize the value of automated on-line film inspection. Examples are presented of implemented inspection systems that provide critical real time process and converting information to maximize yield, improve raw material quality, and strengthen customer relationships. A solution for automated re-wind control for defect removal that increases converting throughput is discussed. The implementation of this technology offers its users a variety of methods to achieve value. The user realizes the maximum value when all of the possible methods are implemented.
INTRODUCTION
Implementation…this is the difference between success and failure of any plan. The best plans, ideas, technologies, strategies, directives, and mandates are useless unless they are implemented. The top performing companies are top performers because they spend much effort on new ideas and technologies and much more effort in implementing these ideas and technologies. This comes from the top of the organization and sets an example of follow through. We can all think about a capital project that was a failure, a piece of equipment that “never worked” or the “person left the company” that had the project, or the “operators never used it”. Occasionally this can be the result of a bad idea, but in an overwhelming majority of cases, good ideas and technology failed because they were never implemented.
This stated, we arrive at the topic of this paper – The Real Value of Web Inspection. We will explore how web inspection systems can be implemented to achieve maximum value. When defects occur in an extrusion process they can be just a nuisance and have no cost ramifications or they can be catastrophic in terms of causing product performance or aesthetic issues. An example of nuisance defects would be small gels and carbon specks in plastic grocery sacks. If your process only creates nuisance defects you are fortunate in this respect, however, these products usually are high volume, low margin commodity products that are purchased primarily on price and involve high customer turnover. <
Of greater concern and cost are defects that affect the aesthetics and/or the functionality of the extruded product. Examples would be carbon specks that affect the appearance and holes that affect the function of flexible food packaging. Functional defects are always catastrophic by causing the product to fail its intended use or required specification. Aesthetic defects can sometimes be catastrophic by prompting customers to return the product or change vendors. When aesthetic and functional defects are not controlled, returns occur, customers are lost, yields shrink, manufacturing costs rise, and the ability to compete in the market place is compromised. The cost of identifying and removing defective product from finished rolls is significant. The cost of failing to accomplish this is also significant. Inspection or lack thereof carries a high cost. Various opportunities exist to turn this cost into a profitable investment.
LIMITATIONS OF STATISTICAL SAMPLING
Controlling extrusion related defect occurrences is sometimes attempted by statistical sampling. A small piece of the film from the end of each roll is analyzed in the lab and the roll is considered to be acceptable or unacceptable. This strategy has many limitations. The occasional catastrophic defect cannot be controlled and real time process improvement is not possible as many rolls can be produced before the lab results are analyzed. In addition, rapidly changing process variations are missed, as the lab sample represents only a snapshot in time equating to as little as 0.001 percent of the produced product.
Figure I shows the variation of gel counts for a single roll of blown film. The data was collected with an on-line ISRA SURFACE INSPECTION SYSTEM that classifies, maps, and trends defect counts. The system counts gel, speck, and gels containing specks per square foot of inspected film. The trend graph in Figure I shows defect counts for each size class and type of defect per square foot of film. Within a five minute interval the total gel counts range from 2.5 to 20 gels per square foot of film and every ten minutes the counts are similar. In this case, statistical sampling, in terms of analyzing the gel count of a piece of film from the end of this roll, would provide erroneous results that would initiate the wrong process control action.
By itself, statistical sampling may provide a low cost alternative to inspection, but its limitations result in higher risks with regards to missed catastrophic defects and higher costs due to reduced process efficiency.
Figure I – Trend graph of defect counts per square foot of inspected film showing short term variation of gel counts
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