Let us begin with the 5%. Paradoxically, the key to success here has been the adoption of a 'low-tech' rather than a 'high-tech' approaches to solving vision problems. Significantly, this strategy is based on the crucial need for highly-robust - highly-accurate, highly-repeatable, and highly-reproducable - functionality. This results in the so-called and widely-adopted KISS approach to system development: Keep It Simple, Stupid. That said, it is of course fundamentally important to have strong expertise and deep experience of these 'low-tech' techniques. The KISS approach, while improving the likelihood of successful application of machine vision, inherently limits the scope of this application to the simpler problems. The consequence is that, if we are going to deploy more complex vision techniques to solve more difficult problems, then it is a necessary condition that they exhibit the requisite robustness. This in turn makes the adoption of a deep strategy for testing, characterization, and benchmarking of algorithms and systems an imperative (and this, of course, has been a recurring theme throughout this report).
Moving on to the 95%, the critical success factors include:
The adoption of an adaptive approach to strategic activities whereby business opportunities are closely monitored and tactical actions are taken to seizing them. This results in a highly-focussed portfolio of expertise in specific application areas (e.g. measurement of device position or measurement of solder fillet profile).
The opportunity to outsource several distinct activities, such as the manufacture of the mechanical chassis of the inspection stations or the integration of system components such as X-Y stages and handling robots with controller hardware and software, to external market-leading companies. However, it is very important to maintain strict control of the quality of out-sourced components.
The existence of an incubation period where skill-sets and strategies are boot- strapped without having to remain in profit. In some instances, this is achieved when corporations inject capital into an embryonic division, or when university and national R&D centres are co-funded prior to full commercialization.
The availability of a small number of staff with a solid grounding in both the technology of machine vision and the discipline of entrepreneurship and business administration.
The existence on a highly multidisciplinary team with expertise in:
- Mechanical engineering
- Software engineering
- User support
- System installation
- System assembly
- Project management
- Focussed product marketing
Conclusion
There is but one important conclusion which must be drawn. It is that the deployment of industrial machine vision in the electronics industry can be both successful and profitable. The long-term prognosis is excellent, given the present growth in electronic-based consumer and information technology products. However, there are significant challenges, the chief of which is the need to deploy real-time (i.e. line production rate) 3-D analysis at much improved resolutions but ensuring that these techniques exhibit the requisite (and stringent) accuracy, repeatability, and reproducability performance characteristics.
Ackhowledgments
The author would like to acknowledge the very considerable contribution of Mr. Sean O'Neill, CEO, MV Technology, Dublin, Ireland, to the content of this short report |
