The Cost of Poor Magnet and Sensor Designs
Poor magnet and sensor designs are like a slowly leaking boat. If you're lucky, you can keep bailing and manage to keep afloat. If you're not lucky, the whole thing can sink.
There is only one key idea to understand. Do things right the first time. If you do appropriate research and planning for a design before you implement it, you stand a much better chance of having a trouble-free successful design.
Simulation is one tool that will help you avoid some problems. Is thousands of dollars of simulation work expensive? Yes. However, it could be very cheap compared to problems it might help you avoid.
Catch Problems Before They Become Disasters
As a project goes on, the amount of time and resources invested increases. The two graphs are typical of what might be expected to occur for various sensor projects as they move forward. The further a project goes on, the more costly problems become.
Some simulation or experimental testing at the beginning might help save you from severe setbacks and losses. Finding out a particular design needs to be changed during production can be extremely expensive both from retooling costs but the wasted time and perhaps reputation. Finding out a particular design will likely have problems before you do much work will allow you to find a good design before you invest significant time and resources.
Catching a design problem in the pre-production or production stages is costly. Engineering and management time, delays, retooling costs, revalidation costs, repeating work that was done once, customer frustration, lost sales, and recalls can be costly. The financial impact of such things could range from a few thousand dollars to hundreds of thousands of dollars depending on the scope of the application. The time impact of such things could range from a few weeks to months of delays and repeated efforts. There is also the lost opportunity cost: time spending fixing things was not spent on new products.
Avoid Problems Before You Start
Feasibility testing is the best place to catch problems. This is the initial testing stage where you decide if it is possible to use a magnet and sensor for a particular application. Decide if this project is even worth pursuing. If not, at worst you've probably only wasted a day or so of work and had relatively small costs. If you pursue a project for which a magnetic sensor is a poor choice, you run the real risk of never having a functional device for production. You also run the risk of having a barely functional device that fails in the field when it is in production.
Experiments and simulation are the primary two tools to use for feasibility testing. Note however that experiments often do not appropriately account for production variation. The sooner you can get an estimated range of performance of a design, the sooner you will have an idea if the design might work well in the field. Simulation can be a useful tool for doing this for some applications.
Avoid Designs That Are Likely to Have Problems
Building prototypes is the best place to determine a good design. Using a combination of simulation and experiments, you should plan ahead and create a design that will meet all possible design contingencies. If you cut corners here, you run the real risk of having a design fail before production starts, or even worse, failing in the field once production has started.
A good first step is to start with good simulation and analysis work. For many applications, simulation can give good estimates on the effect of manufacturing variation. This allows you to avoid designs that would probably have severe problems in production. One of the benefits of this is that you can get a good idea of the range of magnet shapes and sensor types that have a good chance of being successful designs. This means the first set of magnets and sensor you acquire will be closer to your final design than if you start randomly experimenting with magnets and sensors.
There is another good reason to do good simulation work at the beginning stages of a design. By assuming that bad things will happen, you can throw bad things into the simulation before they happen. For example, if you are unsure if your magnet supplier can really hold the promised +/-1% tolerance on magnet strength, you could put a +/-10% tolerance into the simulation and see what happens. Perhaps you do not know what the final assembly tolerances will be. You could take your best guess and then double it and see what the effect is on the simulation. Sometimes a very minor design change would result in the final design being able to trivially handle large production variations. Sometimes there is a particular parameter that will be critical to control. Simulation at the beginning of the design work can help you better understand these things.
A mandatory second step is to verify and validate the design with experimental work on real magnets and sensors. This work will give a good indication of how useful the simulation work is. It also will provide the basis for later quality testing of parts in production. Note that your validation and verification should include as much of the manufacturing variation and final field condition variation as possible.
Catch Design Problems as Quickly as Possible
Having done good design work is not a guarantee there will no problems. It will however have helped you avoid some problems. Sometimes a good design will be trouble-free throughout its production life. Other times, unexpected problems will arise.
As you work through pre-production planning and start production, unexpected things happen. The customer may change some mounting material without telling you. A change from an Aluminum bolt to a steel bolt might add magnetic interference. Such a change might move the activation point of a device by a millimeter or two. The lowest bidder your purchasing department forced you to use ends up not meeting their promised tolerances. Many things can happen.
The sooner you can analyze unexpected situation and understand them, the sooner you can arrive at a series of options to solve the problem. Experiments and simulation can both be good tools for quickly understanding what is happening. Depending on the application, sometimes one or the other will give the quickest results. Sometimes simulation can give quick insights that can lead to easy solutions. Other times a series of systematic experiments is necessary.