Fortune 200 Orthopedic Implant Manufacturer

Machining Modeling A Key Tool in the Predictive Engineering Arsenal

The Fortune 200 Orthopedic Implant Manufacturer needed to be prepared for an expected rise in the demand for orthopedic implant fixation tools. To meet this increase in demand, the manufacturer had to improve the efficiency of their current processes. 

The Predictive Engineering Team was tasked with reducing cycle time, increasing capacity and improving tool life. To solve the critical tool life issue, they chose to forgo the traditional trial and error approach, and instead turned to Production Module for its force prediction models.


To improve the efficiency of the cell producing orthopedic implant fixation tools, the Predictive Engineering Team targeted the tool life improvement for the 4mm endmill. This endmill was a significant contributor to the consumables cost and its untimely breakages on the machine were causing disruptions in production. These disruptions reduced the effective throughput of the cell. 

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The Predictive Engineering Team’s first step was to identify the root cause of the tool breakage problem. To determine the problem, they first ran Production Module analysis to identify the peak load for the tool and the exact instance where this peak load occurs. Data from Production Module was then used for analysis in CREO to better understand the Max Principal Stress. The stress concentration at the exact point of observed tool failure validated the analysis. The Predictive Engineering Team was then able to identify the stress limit and the corresponding load limit that the tool could be subjected to. Using Production Module, the team optimized the toolpath to ensure the load stayed below the acceptable limit. 


  • Tool life increased by 8 times. This reduced the demand for the end mill from 214 to just 20 per month. Demand reduction for the tool directly translated to significant cost savings. 
  • The reduced disruptions with fewer tool changes allowed additional capacity for the anticipated increase in demand. 
  • The Predictive Engineering process removed any guesswork from the analysis and helped the team arrive at a solution quicker than the traditional trial and error approach. 

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