We retrofitted the test benches, with IoT gateways and connected sensors. This enabled existing machines to communicate and perform sensor-based monitoring of the testing medium. Together with our customer, we created a rule-based method to monitor the ISO cleanliness level of the oil. It is now possible to continuously monitor the processing units and use an automated system for maintenance tickets.
With this approach, our customer was able to transform a manual process into an automated process.
The benefits in detail:
- Lower maintenance costs
- Less complex manual testing
- Increase in overall equipment effectiveness (OEE)
2. Quality management of the pressing process
Airbag control units are manufactured by mechatronic presses that assemble every component by mating. To gain a better understanding of these mating processes and how process parameters and product quality relate, we extracted the process data from the proprietary press control system. That way we were able to monitor and demonstrate the force and position of the pressing processes.
We used the data to define a template process which then served as a reference for each press in the production. This allowed a direct evaluation of every single pressing process, based on the raw data. In the past this was only possible with a downstream quality assessment.
The benefits in detail:
- More process transparency
- Increase in product quality
3. Harmonization of tightening processes
When production lines are scattered across the world, it is important that products should be of the same quality, regardless where they are manufactured. One of our customers faced this issue when it came to improving the overall quality of tightening processes.
Our approach in this Industry 4.0 project was to connect the nut runners in different production locations to centralized software. This made it possible to apply the same quality standards to processes in different locations.
The benefits in detail:
- More process transparency
- Better product quality
4. Centralized monitoring & automated ticket allocation
In this Industry 4.0 use case, we developed a centralized monitoring solution in collaboration with a manufacturer. Because there was no central management system for the machines, error messages were only displayed locally on the human-machine interface (HMI). This meant that workers had to wait by the machine and might overlook other important issues.
We decided to set up one centralized system to record and display all machine data. We also established a system that automatically creates maintenance tickets and allocates workers depending on the situation. The workers access this system using an Android app with push notifications.
The benefits in detail:
- An optimized maintenance process
- Cutting the cost of failures by implementing a transparent and consistent troubleshooting process
5. EDM cycle time monitoring
The challenge here related to the monitoring of an electrical discharge machining (EDM) process: The data on process parameters were stored in a database which was checked only once a day. We wanted to evaluate the data using a manufacturing execution system (MES). But since the machines were old, the expense of connecting them to the MES was not a cost-effective option.
Instead, we used a software connector to read out and display the data directly from the machine. That way we were able to evaluate the status and notify associates when deviations occurred. This also allowed us to check and service machines on demand.
The benefits in detail:
- Early detection of cycle time deviations
- Increase in output
6. Cycle time monitoring of CNC machines
Monitoring the status of a CNC machine is particularly difficult. One of the biggest challenges which many large manufacturing facilities are facing is not being able to see what is going on at a granular level. In other words, monitoring is labor-intensive and often only provides limited insight into operations. Employees normally check the vibrations of the machines at predefined intervals. This fixed schedule means that a problem may go undetected for some time. It is also hard to calculate the cycle time of various machines and pinpoint where underperforming machines are affecting production.
We decided to connect sensors to each machine to collect information on its operating status and performance. These parameters were then centrally processed and displayed. This allowed us to analyze information in close to real time. We were thus able to trigger standardized machine checks to optimize production parameters.
The benefits in detail:
- Reduced machine downtime
- Fault prevention thanks to an early warning system
- Continuous improvement based on machine benchmarking and condition monitoring
We attached temperature and flow sensors to the cooling pipes to generate data. Then we defined limits for cooling power and flow.
The benefits in detail:
- Advance warning of pipe blockages
- Less need for pump maintenance
- Less plant downtime
Using a digital refractometer, we were able to measure the concentration of cutting fluid. If air bubbles formed in the cutting fluid mixture, this rendered the measured values useless. These values then had to be deleted.
The benefits in detail:
- Long-term documentation, which can be used to improve tool life and surface finish quality
- Cost savings resulting from lower dosing and consumption of water-soluble cutting fluids
- Compliance with cutting fluid tolerance
By integrating the IoT gateway into the process, we enabled our project partner to collect production data. This helped identify three parameters of relevance to product quality: temperature, humidity and paint consumption. We then defined threshold values enabling alerts to be issued when quality parameters were out of spec.
The benefits in detail:
- Faster response time in case of quality deviations
- Better product quality
10. Vibration monitoring for milling machines
Another Industry 4.0 use case is vibration monitoring in milling machines. By positioning sensors close to the machine it is possible to measure vibration and gather information about specific vibration patterns during cutting operations like milling or drilling. In this way process data is collected on a large scale and provides a unique “digital fingerprint” for every milling process. By comparing the measured roughness with the individual fingerprints you can see how the two sets of data correlate.
The benefits in detail:
- Alert system when milling process gets out if spec
- Quick response time should a problem occur