Methods and systems for generating an instantaneous quote of any part without toolpathing
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.
1 . 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.
2 . The method of claim 1 further comprising classifying, using the computing device, a quotation state as a function of the at least a rotation-invariant feature and a go/no-go classifier machine learning model.
3 . The method of claim 2 , further comprising training, using the computing device, the go/no-go classifier machine learning model according to an F-measure function and a desired false positivity rate.
4 . The method of claim 1 , further comprising training, using the computing device, the manufacturing time machine learning model as a function of manufacturing time training data comprising at least an element of data comprising a logarithmic representation of manufacturing time.
5 . The method of claim 1 , wherein the at least a rotation-invariant feature comprises a surface curvature feature.
6 . The method of claim 1 , wherein the at least a rotation-invariant feature comprises a convex hull feature.
7 . The method of claim 1 , wherein the at least a rotation-invariant feature comprises a decomposition of patches.
8 . The method of claim 1 , wherein predicting the manufacturing time further comprises predicting the manufacturing time as a function of the stock, the at least a rotation-invariant feature and the manufacturing time machine learning model.
9 . 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.
10 . The system of claim 9 wherein the computing device is further configured to classify a quotation state as a function of the at least a rotation-invariant feature and a go/no-go classifier machine learning model.
11 . The system of claim 10 , wherein the computing device is further configured to train the go/no-go classifier machine learning model according to an F-measure function and a desired false positivity rate.
12 . The system of claim 9 , wherein the computing device is further configured to train the manufacturing time machine learning model as a function of manufacturing time training data comprising at least an element of data comprising a logarithmic representation of manufacturing time.
13 . The system of claim 9 , wherein the at least a rotation-invariant feature comprises a surface curvature feature.
14 . The system of claim 9 , wherein the at least a rotation-invariant feature comprises a convex hull feature.
15 . The system of claim 9 , wherein the at least a rotation-invariant feature comprises a decomposition of patches.
16 . The system of claim 9 , wherein predicting the manufacturing time further comprises predicting the manufacturing time as a function of the stock, the at least a rotation-invariant feature and the manufacturing time machine learning model.