Machine Vision Product Focus Study April 2003

Among those who specify, recommend, or buy machine vision products, 77% do so for in-plant requirements, while 39% do so for OEM needs. A slight majority (55%) do not consider machine vision too costly nor complex to implement.

By Control Engineering Staff March 29, 2004

Executive Summary

  • Among those who specify, recommend, or buy machine vision products, 77% do so for in-plant requirements, while 39% do so for OEM needs. A slight majority (55%) do not consider machine vision too costly nor complex to implement.

  • More than two-thirds of respondents use machine vision products for inspection. The next most popular applications are for robotic equipment (33%), motion control (32%), and bar code reading (32%).

  • Ethernet TCP/IP and RS-232 are currently the most widely used communication protocols for machine vision products. When looking ahead to the next year, 73% of those surveyed say they plan to install more or at least some Ethernet TCP/IP networks for such products. RS-232 is the second most popular choice, with 51% of respondents indicating similar plans.

  • Forty-two percent of respondents have used smart vision sensors. Ninety percent of this segment report the sensors met their requirements.

  • Twenty-one percent of control engineers surveyed currently use vision integrators, and another 12% plan to use them in the next year.

  • DVT sold machine vision products to 32% of respondents over the past year. Cognex and Keyence round out the top three companies in terms of market penetration, by virtue of sales to 20% and 19% of survey participants respectively.

  • Results indicate the average respondent purchased 6 machine vision products in the past year. This equates to average spending of $59,925 per respondent during that time. Results further suggest machine vision purchases will grow over the next 12 months.

  • Performance is the most important factor to control engineering professionals when selecting a machine vision product. Capital budget limitations are the most significant impediment to increased use of machine vision.


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