Case Study
Officine Forgiarini
The Problem
Officine Forgiarini, specialized in subcontracted mechanical machining of large-scale structures and precision bases, faces a crucial challenge: monitoring tool wear. In machining unique, large-sized workpieces, the sudden breakage of a tool can lead to prolonged machine downtime, significantly impacting production time and costs. The adoption of advanced predictive wear monitoring systems, based on sensors and artificial intelligence, represents a strategic solution to enhance process reliability, prevent unexpected failures, and optimize tool management.
The Solution
The solution to the problem of tool wear in large-scale machining operations stems from collaboration with Engineer Massimo Audrito, from which the WearSense project took shape. Through an in-depth phase of consulting and development, an advanced predictive monitoring system was designed, based on audio, vibration, and power sensors.
The key innovation lies in the use of Machine Learning algorithms and neural networks, capable of analyzing acquired data in real time and accurately predicting tool wear before breakages occur. A distinctive element of the solution is the design of spiral antenna arrays, developed to capture signals with high sensitivity and ensure precise monitoring.
Thanks to this technology, workshops can optimize tool management, reduce machine downtime, and improve the reliability of the production process, achieving significant savings in terms of costs and processing time.
Thanks to this technology, workshops can optimize tool management, reduce machine downtime, and improve the reliability of the production process, achieving significant savings in terms of costs and processing times.