CloudNC CAM Software Features Estimating, Quoting Capabilities | Modern Machine Shop

CloudNC introduces the resource-efficient Cycle Time Estimator function for its AI-powered CAM Assist solution, available for Mastercam, Autodesk Fusion and Siemens NX CAM users.

CloudNC announces the launch of CAM Assist Cycle Time Estimator, a tool designed to accelerate the CNC machining estimation process and eliminate bottlenecks in the quoting workflow. 

By automating the estimating and quoting process for multiple parts simultaneously, Cycle Time Estimator enables users to turn around estimates faster.

As a result, estimators using CloudNC’s CAM Assist solution within Mastercam or Autodesk Fusion can quickly generate accurate machining times and tool path strategies in bulk for three-axis and 3+2 axis parts. They can then export that data seamlessly and integrate it with their estimation workflow — enabling them to quote for and win more work, while using their resources efficiently. 

By using CAM Assist’s machining algorithms and tool selection to carry out estimates, Cycle Time Estimator can reduce human inconsistencies in the estimating process, improving and standardizing quote accuracy. The solution also provides meta-data for each part, including part volume, stock volume and other relevant data such as machining efficiency and number of operations, enabling the estimates to be easily integrated into existing workflows. 

CloudNC’s CAM Assist solution is also available as an AI-enabled add-on for Siemens’ NX software for product engineering and manufacturing, aiding users to efficiently program toolpaths and machine parts.

“Estimating and quoting for new work is one of the biggest bottlenecks in machining, not only because it’s an expert-level task that’s critical for winning new business, but also a huge time suck,” says Theo Saville, chief executive and co-founder at CloudNC. “Our solution accelerates that process while also enabling users to execute it on many parts at once, turning around accurate estimates much faster than is manually possible, even for a genius-level estimator.”

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