This article touches on search a bit, but it doesn’t quite capture how useful I’ve found its capabilities as a hobbyist.
For example, I needed to replace a bent gear axle for my lathe during the height of supply chain issues in the past few years. The part was proprietary to the vendor, who did not have any spares on hand and was awaiting an order from their manufacturer in China.
The top search results on McMaster for “axle” yielded both axle stock and pre-fabricated axles that would likely be great solutions in other scenarios, but ultimately neither of those categories contained a part that would solve my issue.
However, third on the list of results was a category for “axle bolts”, which makes sense based on my use of one of those strings in my search. But “axle bolts” weren’t a category of things you can find by manually navigating the menus to barrow down your search. Following the link to that category actually presented you with a list of products under the category of “shoulder screws”. Within these, I found a part that fit the bill for a temporary fix!
I found it incredibly useful that it helped me navigate through industry terms that I wasn’t familiar with. It almost seemed akin to Netflix’s highly specific “shadow” categories such as “90s sitcoms with female leads” or similar.
Actually (and perhaps it already can), a ChatGPT model that could help me design projects with specific tolerances by recommending parts from sources like McMaster would be very useful.
For example, I want to build a simple hoist with a working maximum weight of 1 ton. Which grade of steel bolts should I buy that are most effective for that application? What thickness of steel square bar do I need?
There are fairly straightforward ways of calculating what a material is capable of and modeling its performance using simulation software, but a passing grade doesn’t tell me that my choice of grade 12.9 bolts is less cost effective than grade 10.9 bolts, which are significantly cheaper.