ficonTEC Service (Achim, Germany) and Adapdix Corporation (Pleasanton, CA) entered into a strategic agreement to implement the Adapdix EdgeOps (trademarked) platform technology within ficonTEC’s advanced photonics production, packaging, and assembly systems. With the integration of an artificial intelligence/machine learning (AI/ML) layer to monitor, predict, and appropriately adapt operation-critical process steps, overall system reliability and performance is improved, creating a further level of sophistication and differentiation for ficonTEC’s machine systems. More information is available by contacting ficonTEC or Adapdix directly.
The EdgeOps AI/ML platform and software engine works by tapping into the wealth of data generated by ficonTEC’s software control interface called Process Control Master (PCM). The PCM software automatically logs real-time positional, vibrational, environmental, and physical/optical performance data for all photonic system modules and customer process steps. Predictive analytics realizes the full potential of this data for optimization purposes.
According to Torsten Vahrenkamp, CEO at ficonTEC, “Through the addition of EdgeOps [trademarked] into our software we integrate access to predictive maintenance technology for the customer. This helps streamline real-time monitoring, greatly simplifies the establishment of process and machine metrics, and enables the development of low-latency adaptive maintenance capability--in particular vital for installations operating at the Edge.”
ficonTEC customers will be able to trial and ultimately select subscription-based add-ons through the company’s newly launched ‘Performance Services’. Tailored packages variously targeted at collation and visualization of machine-level operational data serve predictive analytics and facilitate self-optimization. ficonTEC says the software underlines and strengthens its position in the photonics manufacturing space and establishes a significant growth path for ficonTEC production systems in modern volume manufacturing sectors (Industry 4.0, IIoT, sensors, and more).
Anthony Hill, CEO at Adapdix, adds, "We have already successfully deployed multiple systems for key device manufacturers, demonstrating a measurable reduction in downtime and increased production yield. By handing control to the systems operators in this way, they can expect a tractable improvement in operational performance, leading to lower total-cost-of-ownership [TCO]. Taking all aspects together, device manufacturers themselves can better leverage their own competitive edge in the market."
SOURCE: ficonTEC; https://www.ficontec.com/machine-learning-for-advanced-manufacturing/
