Data Collection

When we talk about data what we actually mean is events. The first task when installing CIMCO MDC-Max is to consider what machine and operator events you want to collect information about.

Machine events (Automatic)

Machine events can be collected automatically by CIMCO MDC-Max and most installations will include events such as Cycle Start, Cycle Stop, and Part Complete as a minimum. When these events occur at a machine CIMCO MDC-Max is automatically notified and data about the event is stored.

Operator events (Manual)

Operator event data is sent manually by machine operators. Using a smartphone, tablet, barcode scanner or PC an operator at a machine can send status information to CIMCO MDC-Max indicating specific events. Usually you want to collect downtime reasons such as Waiting for Setup, Waiting for Maintenance, Scrapped Part, Inspection, etc. CIMCO MDC-Max can be customized to collect specific information on any event.

The following diagram illustrates how Machine- and Operator events can be collected and stored by CIMCO MDC-Max. In this example the CNCs transmit events through a wireless network, however, MDC-Max can be integrated with any existing wireless, Ethernet or RS-232 serial network.

Basic setup for
Machine Data Collection

The majority of companies only want to know if a particular machine is running and producing parts or if it is stopped. With a basic data collection setup MDC-Max can show the amount of time the machine has been in production and the amount of time allocated to downtime.

Advanced setup for
Machine Data Collection

With the advanced data collection option MDC-Max can provide accurate reporting of machine tool efficiency on any job. MDC-Max can show the total percentage of downtime for each type of machine stoppage such as Tooling, Setup, Machine Maintenance, etc. This additional information is provided by the operator using a tablet or a barcode scanner. The operator uses the tablet or simply scans a barcode to indicate the downtime reason. This information can then be used to pinpoint exactly what is causing a loss of production.