Amada Weld Tech has launched the IIoT-ready WM-200A Networked Resistance Weld Monitor.
The WM-200A enhances resistance weld monitoring capability by simplifying data capture, storage, and analysis on a networked platform, paving the way to next generation artificial intelligence and machine learning features.
The WM-200A monitors all aspects of the resistance welding process, providing visual and statistical feedback during research and development as well as production environments. High-resolution data capture is critical for artificial intelligence and machine learning algorithms. It also provides immediate feedback to the weld station by monitoring key aspects of the process and sending good/no good information to the process controller instantaneously during production.
Connected to a standalone resistance welding station or implemented in a fully automated system, the WM-200A enables users to collect large amounts of high-resolution process data to be used for manufacturing traceability, statistical data analysis, equipment efficiency and health. Data can be stored on an on premise or cloud based server and viewed or downloaded from a remote location using the remote graphic user interface.
The WM-200A monitored inputs include current, voltage, displacement, and force. The monitor features an intuitive user interface and quick access to view waveform and numeric data. Configurable monitoring screens enable custom viewing. The WM-200A offers high resolution data capture (up to 200 kHz sample rate) and can simultaneously monitor eight different inputs and up to four distinct windowed process limits per primary channel.
The WM-200A communicates with direct I/O and TCP/IP communication protocols; optionally, it can be configured with an EtherNet/IP field bus to allow automated systems to communicate over a single communications cable. Typically, WM-200A is connected to a large display on a personal computer for easy setup and viewing of waveforms and data via WM-Inspect software GUI. Stored data is accessible by third party software suites for analysis for SPC, OEE, and equipment health. The data is also available for current and future AI/ML software algorithms.