Methods and systems for generating an instantaneous quote of any part without toolpathing
AssigneeProtolabs, Inc.
InventorStefan Emilov Atev
Life of this patent
Estimating the term…
Abstract
Methods and In an aspect a method of generating an instantaneous quote of any part without toolpathing, the method includes receiving, using a computing device, a geometric model of a part, constructing, using the computing device, at least a rotation-invariant feature as a function of the geometric model, predicting, using the computing device, a manufacturing time as a function of the at least a rotation-invariant feature and a manufacturing time machine learning model, selecting, using the computing device, a stock as a function of the at least a rotation-invariant and a stock selection machine learning model feature, and estimating, using the computing device, a quote as a function of the manufacturing time and the stock.
Claims16
A method of generating an instantaneous quote of a computer numeric control (CNC) part without toolpathing, the method comprising:
- receiving, using a computing device, a geometric model of a part to be manufactured using subtractive manufacturing comprising CNC manufacturing;
- constructing, using the computing device, at least a rotation-invariant feature as a function of the geometric model, wherein:
- the at least a rotation-invariant feature is in a form adapted for use in a machine learning process and comprises a co-occurrence histogram representative of redundant or co-occurring data in the geometric model of the part to be manufactured;
- and
- the co-occurrence histogram comprises a co-occurrence matrix including a plurality of elements comprising at least a distance to occluding geometry which is evaluated over a surface of the geometric model of the part to be manufactured;
- predicting, by the computing device, a manufacturing time for the part by:
- receiving, from a client device, a computer-aided design (CAD) file storing the geometric model of the part;
- generating a tessellated surface representation of the geometric model;
- constructing the at least one rotation-invariant feature by generating the co-occurrence histogram in matrix form that represents at least one of surface orientation, part thickness, and the distance to occluding geometry;
- and
- providing the co-occurrence matrix to a manufacturing time machine-learning model stored in memory and comprising a first artificial neural network (ANN) to output the manufacturing time, wherein the manufacturing time comprises a predicted time to perform at least a milling operation;
- selecting, using the computing device, a stock as a function of the at least a rotation-invariant feature, including the co-occurrence matrix, and a stock selection machine learning model comprising a second ANN;
- and
- estimating, using the computing device, a quote as a function of the manufacturing time, including the time to perform the at least a milling operation, and the stock for CNC manufacturing of the part to be manufactured without generating a toolpath for a CNC machine tool during the prediction of the manufacturing time, the selection of the stock and the estimation of the quote.
A system for generating an instantaneous quote of a computer numeric control (CNC) part without toolpathing, the system comprising a computing device configured to:
- receive a geometric model of a part to be manufactured using subtractive manufacturing comprising CNC manufacturing;
- construct at least a rotation-invariant feature as a function of the geometric model, wherein:
- the at least a rotation-invariant feature is in a form adapted for use in a machine learning process and comprises a co-occurrence histogram representative of redundant or co-occurring data in the geometric model of the part to be manufactured;
- and
- the co-occurrence histogram comprises a co-occurrence matrix including a plurality of elements comprising at least a distance to occluding geometry which is evaluated over a surface of the geometric model of the part to be manufactured;
- predict a manufacturing time for the part by:
- receiving, from a client device, a computer-aided design (CAD) file storing the geometric model of the part;
- generating a tessellated surface representation of the geometric model;
- constructing the at least one rotation-invariant feature by generating the co-occurrence histogram in matrix form that represents at least one of surface orientation, part thickness, and the distance to occluding geometry;
- and
- providing the co-occurrence matrix to a manufacturing time machine-learning model stored in memory and comprising a first artificial neural network (ANN) to output the manufacturing time, wherein the manufacturing time comprises a predicted time to perform at least a milling operation;
- select a stock as a function of the at least a rotation-invariant feature, including the co-occurrence matrix, and a stock selection machine learning model comprising a second ANN;
- and
- estimate a quote as a function of the manufacturing time, including the time to perform the at least a milling operation, and the stock for CNC manufacturing of the part to be manufactured without generating a toolpath for a CNC machine tool during the prediction of the manufacturing time, the selection of the stock and the estimation of the quote.
Dependencies are inferred from the claim text.