A recent review with Yigal Ziv resulted in exploring practical methods to improve the productivity of robotic cells.
Two facts to note about automated machining cells:
- The introduction of automation includes adding sensors (switches, cameras, etc.) and controls (PLC or robot control) that are required for automation to work safely. Each of these could potentially fail, and thus require attention.
- No matter how automated the robotic cell/line is, it must interface with humans for various activities.
Here are some practical suggestions that could be applied and drive an increase in productivity:
Yigal Ziv points to the fact that robotic cells must be surrounded by a fence with an entry door. The robot must stop when an operator or another person opens the fence door, machines in the cell will complete their current cycle but parts will not be unloaded by the stopped robot and production will stop.
These interruptions must be tracked, and if there are too many cell entries, this might suggest there are operational problems in the cell.
To uncover these one must collect additional data from the cell to realize which cell component keeps failing and triggers excessive cell entries that interrupt production. Cell activities are controlled by either a PLC or in the robot control. Signals from all sensors in the cell including machine tools, proximity switches, cameras, conveyors, gauges, robots, etc. are sent to that cell control and should be tracked. Machine tools typically only send a bit to the cell control, to signal they have an error, but there are no error details, these details are available from the machine tool and should be tracked as well.
Yigal Ziv: “we saw repeated alarms from fixture sensors that detect if the part seats correctly, from the automatic gauge in the cell, etc.
Failures that happen often should be addressed and should result in lowering the number of cell interruptions, increasing productivity”
Operators are engaged with the cell to perform various tasks that are required for its operation. According to Yigal Ziv, examples include replacing expired tools, part inspection, etc. and some of these tasks are performed away from the cell (tool crib, coolant, and more).
When cutting tools expire machines stop and wait for the operator to replace them, when a part should be inspected, the machine may need to wait until the operator confirms the part is good. Every machine stop requires operator action, and busy operators may leave their machines idle longer than the minimum time, thus losing production time.
An effective solution could be a smart display that shows the operator the remaining time to the next machine stop due to any of the above reasons. With this information operators could manage their tasks to tend to the machine when it stops and be ready (new tools, etc.) to get it going again, minimizing downtime and increasing productivity.
Fig. 2 Smart display shows 3 hrs. 16 min. remaining before tools will need to be replaced (left machine) and 18 min. remaining for the next quality inspection (right machine).
Management response time
Management is typically informed of production results with a time lag, either after the end of a shift or even the following day. This is after the fact and late, leaving management to wonder why results are as they are.
In Fig. 3 the chart shows the target capacity for a cell and max. the capacity is capable of.
The difference of 95 parts between the target and maximum capacity is the excess capacity of the cell. It can also be considered the reserve capacity of the cell that is available to recover any loss of parts production due to cell stops.
According to Yigal Ziv tracking the changing levels of the reserve capacity in real-time during the shift will show if the cell will meet the target number of parts since when it becomes negative (max. capacity – target = -X) the cell will not meet the target.
The cell may reach the point of negative reserve capacity at any time during the shift, sometimes even as early as the first or second hour.
A process could be adopted where a threshold is set and an alert is sent while there is still positive reserve capacity, allowing the shop to activate a plan B, and pro-actively take steps to meet the shift target.
Fig. 4 Cell 50 is approaching the reserve capacity of 10 parts, triggering an alert to management before the cell loses the ability to meet target output.
Additional practices The practices described above, writes Yigal Ziv, represent a few ways how data collected from cells could be applied to take control of cell outputs.