MachineMetrics Predictive: A Case Study Interview with BC Machining

When you make scrap parts, you're wasting money

The Problem

BC Machining was producing large quantities of scrap parts on its 24-hour CNC machines. To hit their production goals, they had been running machines at 200% capacity, resulting in broken endmills.

The reason for this is that BC Machining had no insight into when a tool was worn or when tool breakage was imminent. This resulted in significant scrap costs and broken tooling.

BC Machining began looking for a solution that would prevent tool failures and scrap parts while ensuring tool life could be maximized.

The Solution

BC Machining enlisted the help of MachineMetrics Tool Monitoring, an adaptive tooling technology that enables manufacturers to diagnose, predict, and automatically prevent the machine tool failures that lead to broken tools, scrap parts, and costly downtime.

MachineMetrics Tool Monitoring solution was able to identify the signals on BC Machining's Star SR-20s before catastrophic tool failures occurred. Parts made during these elevated load periods were scrapped, costing BC Machining time and money.

A predictable pattern quickly emerged, indicating with 99% accuracy when a machine tool would be likely to fail. MachineMetrics Tool Monitoring was trained to diagnose, predict, and prevent these machine tool failures by autonomously implementing a feedhold on BC Machining’s STAR machines that effectively halted the machines when part failure was imminent so that the tool could be replaced proactively, before generating scrap and unplanned downtime.

MachineMetrics Tool Monitoring