While developing some blueprints with beaconed and accelerated assembly machines, I noticed unexpected speed differences for stack inserters. I investigated further and found out that the speed heavily depends on the belt layout. It's no deal with slower setups, but speed boosted assembling machines with fast recipes push the insertion speed to the limit, especially if you try to completely consume or completely fill blue belts.
It's not only dependent on belt speed, but almost everything: curves, underground entry, underground exit, or if the belt ends after the inserter or continues. And especially if items are pulled from the far lane, or from the near lane, or if the inserter can choose from which lane (surprise: this is slowest!). I guess I'm telling no news for the veteran players, but I'm no veteran player.
If it is of any interest, this is my research result for perpendicular belt-to-inserter-to-chest.
(you might need to open the images in a new browser tab to better read the numbers.)
Near lane:
belt throughput near lane.jpg (233.22 KiB) Viewed 2795 times
Far lane:
belt throughput far lane.jpg (239.04 KiB) Viewed 2795 times
Both lanes:
belt throughput both lanes.jpg (291.21 KiB) Viewed 2795 times
Blueprint:
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The meters are from the
Nixie Tubes mod . Decimal point is between the 2 segments. Unit is items per second. To make your own measurements, to reset and restart the counters toggle the "R" constant combinator on/off ("reset").
The most surprising for me, and this was why I started this measurement series, are variants 1 and 2. Although the relevant belt piece is AFTER the inserter, it changes the inserter speed before it. In the blueprint I was developing, the 11% speed difference to variant 4 was the difference between "not feasible" and "working perfectly". I assume it is because the belt curve changes the item positions slightly for earlier belt tiles and the inserter has a better reach somehow.
Also very interesting is variant C. Here, it seems the inserter tries to collect from both lanes if items are available, and considerably slows down in comparison to collecting from one lane only. This happens on other variants as well, but this is the most visible.