Comment by londons_explore
9 years ago
I think your best bet to minimize human effort is this:
* Run the machine over a sample of raw parts, just counting what it sees. Use that estimate to predict which sets can be completely made with high certainty.
* Next choose 6 types of set which can be made, and program the machine to put into 6 bins the correct parts with the correct ratio to form the sets. Anything uncertain or not within the ratios goes in the junk box.
* Now rerun the contents of each box, this time programming the machine to deposit one complete set into each bucket, again discarding parts which don't have a high probability of being correct. Make it beep when it believes a set is complete.
* Now you have an operator weigh the complete set, and if it matches the correct set weight, bag and ship it (self sealing padded bag and auto-printed label), and if not, throw it all back into the input hopper.
Total human time per set should be sub 30 seconds, so assuming a large enough market, it sounds profitable. Assuming 300 pieces per set and 5 parts sorted per second with a 50% failure rate, you would need 4 machines to keep a human busy for the 2nd part of the process. The first part is slower, but could run without supervision.
Assuming you have a 95% part recognition accuracy first time round, and 99.9% the 2nd time (since you are selecting from what is already believed to be 95% the correct part mix for the job, and it's fine to have a very high reject rate), most sets of <1000 parts will be all correct. The weighing step will then probably weed out >75% of errors, and the remaining errors are likely only color related and not so serious.
With this method, you should be able to get major (ie. not colour) errors in sets of 300 to below 3%. Make that lower still by including a couple of extras of common and cheap parts.
For the remaining 1%, just send out a new pack entirely if the customer complains. It adds 1% to your costs. Big deal.