Cycle time monitoring in an assembly cell
Profile
An assembly cell was retrofitted to measure and optimize cycle times. Customizable textile wristbands are produced in the assembly cell.
Photos of the machines
Challenges
Lack of information about production performance
- Cycle times are unknown
- Bottleneck of the assembly cell cannot be identified
- No information about productivity of individual employees
- Piece counts are not documented
- No comparison between target and actual performance
Lack of transparency about downtimes
- Frequency and duration of downtimes of the assembly cell are not recorded
- Causes of downtime are often unknown and not documented
Connection of assembly cell to conventional systems not possible
- Sewing machines do not have machine controls that could be connected
Solution
Integration
TODO: #66 Add integration for assembly analytics
Installed hardware
factorycube
factorycube sends the collected production data to the server. See also [factorycube].
Gateways
Gateways connect the sensors to the factorycube.
Models:
- ifm AL1352
Light barriers
Light barriers are installed on the removal bins and are activated when the employee removes material. Used to measure cycle time and material consumption.
Models:
- ifm O5D100 (Optical distance sensor).
Proximity sensor
Proximity sensors on the foot switches of sewing machines detect activity of the process. Used to measure cycle time.
Models:
- ifm KQ6005
Barcode scanner
The barcode scanner is used to scan the wristband at the beginning of the assembly process. Process start and product identification.
Model:
- Datalogic PowerScan PD9531
- Datalogic USB Cable Straight 2m (CAB-438)
Implemented dashboards
The customer opted for a combination of our SaaS offering with the building kit (and thus an on-premise option). The customer decided to go for PowerBI as a dashboard and connected it via the REST API with factoryinsight.
Used node-red flows
With the help of Assembly Analytics Nodes, it is possible to measure the cycle time of assembly cells in order to measure and continuously improve their efficiency in a similar way to machines.
Here is an exemplary implementation of those nodes:
There are 2 stations with a total of 4 cycles under consideration
Station 1 (AssemblyCell1):
1a: Starts with scanned barcode and ends when 1b starts
1b: Starts with a trigger at the pick to light station and ends when station 1a starts
Station 2 (AssemblyCell2):
2a: Starts when the foot switch at the 2nd station is pressed and ends when 2b starts
2b: Starts when the quality check button is pressed and ends when 2a starts.
Assumptions:
- Unrealistically long cycle times are filtered out (cycle times over 20 seconds).
- There is a button bar between the stations to end the current cycle and mark that product as scrap. The upper 2 buttons terminate the cycle of AssemblyCell1 and the lower ones of AssemblyCell2. The aborted cycle creates a product that is marked as a scrap.
Nodes explained:
Assembly Analytics Trigger: Cycles can be started with the help of the “Assembly Analytics Trigger” software module.
Assembly Analytics Scrap: With the help of the software module “Assembly Analytics Scrap”, existing cycles can be aborted and that produced good can be marked as “scrap”.
With the help of the software module “Assembly Analytics Middleware”, the software modules described above are processed into “unique products”.
Here you can download the flow described above