What is DSSP and why does it matter?

You have probably come across the topic of digital shape sampling and processing or DEE ES ES PEE ... DSSP is about using a semiconductor laser as a light source with sensors with small digital cameras as coordinated measurement sensors to accomplish a noncontact measurement method based upon optically measured light wave displacements from an object's reflection to give a very accurate three dimensional depiction of relative distance. We could go into detail describing the physics of the light sensors, the development of laser optics, DSP systems-on-a-chip and the the error-corrrection and coordination algorithms that make it all possible, but the point is, it works, it works well, it works VERY well and it is proven technology.

That puts where we are today with DSSP ... the technology has been around a long time – most of the early patents have expired ... the early adopters were trying this stuff in the 80s ... the half life of a patent 10 years is somewhat significant ... by the time the patent expiration is closer than the when the patent is granted, it's time to move on to the next LITTLE thing ... technology is really a matter of what have you done for me LATELY ... and if the idea is any good and has lead to more patents, the technology is more or less proven and it's also time for widespread adoption by the unwashed masses. We are those unwashed masses ... the reason that we want to adopt the technology is for it's use in our bread and butter, ordinary everyday business of develop world class products ... technology feeds on technology!

Not every one spends a lot of time thinking about DSSP but it's amazing at how ubiquitous the subject is when you look at cable, TV, technical publications ... maybe it's not that amazing when you consider how incredibly useful the technology is – it is truly life-changing technology ... perhaps you have seen a show about how the technology is used in digital movie production on the Discovery channel or a maybe you have seen a science program on PBS about the use of the technology for historically-accurate reproductions like a dimensionally-perfect casting of the Liberty Bell [without the crack] or maybe you have seen the use of technology in a crime forensics evaluation on CSI as you were channel surfing in your motel room on some business trip. Maybe you do not watch television, but perhaps you have seen interesting articles in trade publications or you have seen a demonstration that may have peaked your interest.

Since the technology is relatively old by now, maybe 15 to 20 years old depending on your definition, perhaps you have thought about using digital shape sampling and processing in the projects that you are working on. For example, I can think of lots of folks in manufacturing who want to scan and qualify manufactured parts and possibly reverse engineer as-built parts that someone working on the project has fabricated. Of course, they also want to study the competition and understand what they are doing – they am especially interested in knowing whether the competition is copying what they am doing. It may seem a bit paranoid – but when you believe you are the best at what you do, you really do need to know when the competition is gaining on you – you really need to know if they have copied you. If you are not paranoid about the competition and being better than they are, you are probably not in business any more ... or it's only a matter of time ...

As you start to explore this world in more detail, you probably will come to the conclusion that DSSP technology has significant implications for your business. For example, what does it mean to be able to get several million reasonably accurate data points in a shape point cloud to define the complete surface profile for a component? It means that design and development time for critical components of a new product can be decreased by a factor of 5X or 10X. That seems like an outrageous claim until one understands the root cause of design and development time being a matter of complete and accurate communication about working prototypes of components, manufacturability and measurability.

First of all, it means that you do not have to spend as much time releasing and reviewing early prints or worrying about a dimensioning, tolerancing and measurement scheme until after you have scanned prototype components, fitted prototype assemblies and studied the shape data versus the performance data – i.e. complete digital scans of surfaces means that we will work in a world with complete data; complete data means that we do not have to make educated guesses about what might be important or what might need to be measured. Everything is measured in a tiny fraction of the time that it took to measure a tiny fraction of what we could measure with a CMM (even with stitching programs, DCC controls and appropriate CAD/CAI software).

Secondly, the speed of shape sampling means that more parts can be measured to give a multi-dimensional understanding of mfg variation – including a 3D understanding of variance and co-variance of different points on the part. With this understanding it is possible to optimize a measurement and QA/QC strategy. It becomes possible to develop production fixturing that serves as a check gage because it uses convenient features [with a known correlation to other critical features]. Slower and costly, but more accurate CMMs can be utilized in a more optimal fashion to focus on a much narrow range of points where accuracy is key.

Finally, a multi-million point shape cloud can be processes to visually illustrate the conformity of an entire surface – it is not necessary to design engineers and others reviewing layout data to go back and forth between a print and layout chart. We can use the power of mature software (release 10 or more) coupled with the computing power of our desktop workstations to understand how the actual as-built terrain of a component compares with the design model. Since design model was realized with CAM software to replicate the model, we can use the variance between the realized components and the theoretical model to re-calibrate the model for the materials and processes that will be used. In other words, by understanding the root cause of the variation we can improve our tools ... so that we can become more efficient next time ... rather than blaming differences on a mistake in construction, an incapable processes or lack of cooperation.