A tried-and-true axiom regarding “systems integration” in industrial automation says that for any project or application it is the point at which “someone has to ‘make it work.’” One might reasonably expand on that by adding that the application also has to “work” correctly and reliably. Depending on your discipline, industry, and the size or scope of the application, systems integration may be approached in a wide variety of ways. However, no matter what the project is, or whether you are working on a system for internal use or providing integration as a service, success comes from thorough planning and competent execution. Integration for machine vision and support for related technologies admittedly sometimes may be a more constrained task or subset of a larger project. Nonetheless, in the rapidly changing and expanding landscape of imaging hardware components and software solutions, the job of systems integration is as important as ever regardless of project scope. Here are some of the key elements of integration in this context and how they help ensure success now and in the future, when utilizing both mature and cutting-edge vision technologies.

A common critical mistake is to design a solution around a technology instead of designing based on the needs of the application. The problem with this approach is that the solution becomes constrained by that prejudgment, and the final system may either not be able to support the application requirements or may end up being more costly or take more time than necessary. A typical scenario is that your company or end-user customer presents an application and states that the solution must utilize AI, or a smart camera, or a cobot, or some other specific technology. Or, as the integrator you immediately start considering certain components before digging deeply into the needs and nuances of the project, or perhaps one relies on a vendor recommendation after only a cursory explanation of the proposed system. Where then does one start?