Enhance overall equipment effectiveness to boost productive efficiency
Since the advent of Industry 4.0, a number of challenges have been stacking up at production lines. These challenges include high- or low-quantity production, an increased demand for customized products, zero-error production, and mixed-model production lines. Consequently, these challenges have set in motion a number of changes in factories, culminating with the introduction of new technologies and concepts.
As optimal production is pivotal in the age of Industry 4.0, overall equipment effectiveness (OEE) has emerged as the key performance index (KPI) in the manufacturing industry. OEE benchmarks the percentage of manufacturing time that is truly productive. An OEE score of 100% shows that you are manufacturing only good parts, as fast as possible, with no downtime. OEE’s KPI formula incorporates three elements: availability (no downtime), performance (the speed of production), and quality (percentage of products up to standard).
During production, machines generate different types of data, such as machine vibration, motor current, and coolant level. Based on this data, machine maintenance engineers schedule maintenance tasks (predictive maintenance) to avoid any unexpected machine downtime. However, the data presents itself in different forms. One is streaming data, which is transmitted in big volumes and requires preprocessing before it is sent to a back-end system. The other is status data, which is transmitted in small volumes and via a transparent method without any preprocessing. Thus, the system has to use different methods to collect both status and streaming data. For status data, the best way is to use transparent data collection. For streaming data, the best way is to use front-end data processing to downsize the data to provide valuable information to the back-end system. Downsizing is necessary because there will be too much streaming data to transfer all the raw data to a back-end system.
Most PLC networks rely on the Internet Group Management Protocol (IGMP) for multicast transmissions. For example, implicit communications, usually used to check the availability of systems that connect PLCs and devices, are based on multicast.
Together with Moxa’s Turbo Ring solution, Moxa offers its pioneering technology, V-ON, which enables a physical network, unicast traffic, and multicast traffic to recover within milliseconds when there is a failure on a network. V-ON technology ensures that the system keeps running and maximizes productivity.
In addition, Moxa’s pioneering industrial network management mobile application, MXview ToGo, provides real-time notifications to reduce system downtime. It sends real-time notifications about the network status, provides the ability to check detailed information right away, and also provides an easy-to-use device-locating function to help field engineers find the right device in a network.
High customization is a significant value of Industry 4.0, prompting regular changes in the programming of computer numerical control (CNC) machines. However, because a CNC buffer is limited, and programs are relatively big, CNC machines always take a long time to download new programs. Therefore, the stability of the data transfer is very important, especially in old machines with serial interfaces. A serial-to-Ethernet device server with a flow-control feature is normally used to stop incoming data to prevent data drop; otherwise, the dropped data has to be resent. Spending more time on data transfers means less manufacturing time.
With Moxa’s on-chip flow-control feature, Moxa’s device servers (NPort) are able to process the stoppage (Xoff) directly from a UART, which eliminates data loss when a program is changed.
Mixed-model production entails a high degree of variation that includes thousands of different options and combinations to produce customer-specified components and multiple product types on the same assembly line. To increase the productivity of machines and operators, Moxa’s ioPAC 8500 programmable controller helps categorize the production tasks, ensuring that the task sequence can be automatically retrieved from an MES (Manufacturing Execution System). For example, tools are identified by an RFID tag, and an ioPAC retrieves the production method from the system and notifies all the relevant machines via an industrial protocol, such as Modbus. The production information is then sent back to the MES via an IT protocol.
Measurements and calibrations are important for CNC-machine applications. Production data is the key to help you understand the situation. Two types of data are applicable: stable data (on/off status and the volumes of data are small) and temporary data (generated over short periods and needs to be recorded without missing any parts).The generation of temporary data indicates that production quality is being affected. Therefore, it is important to collect the data precisely.
Moxa provides a ruggedly designed IIoT data gateway to help customers collect precise data in harsh environments so that the line manager stays up-to-date with regard to the status of all devices in the field. Moreover, the ioPAC uses an open platform, and customers can implement their program to do data preprocessing in order to retrieve valuable information for predictive maintenance from raw data and reduce traffic between the IIoT gateways and back-end servers. The ioPAC also provides Azure cloud-ready connectivity. Customers can push their data to the Azure cloud and leverage third-party software to do data mining or data analysis.
Understanding machine status to reduce downtime and improve network availability
Reducing the changeover time to increase the productivity of machines or operators
Achieving zero defects and providing early warnings