Methods and systems for predicting a price of any subtractively manufactured part utilizing artificial intelligence at a computing device
A method for predicting a price of any subtractively manufactured part utilizing artificial intelligence at a computing device. The method comprises receiving a manufacturing request datum, wherein the manufacturing request datum further comprises at least an element of user mechanical part data, selecting a correlated dataset containing a plurality of data entries. The method further comprises generating, at a clustering unsupervised machine-learning model, at least a first correlated compatible part element, receiving, at a supervised machine-learning model, a first training set, generating, at a supervised machine-learning model as a function of the first training set, an external price output, and determining a price ratio output, wherein determining the price ratio output further comprises generating a loss function of a part specific variable and minimizing the loss function.
1. A method for predicting a price of a subtractively manufactured part utilizing artificial intelligence at a computing device, the method of predicting a price comprising:
receiving, by a computing device, a manufacturing request datum, wherein the manufacturing request datum comprises at least an element of user mechanical part data;
parsing, using a language processing module operating on the computing device, the manufacturing request datum for the at least an element of user mechanical part data;
selecting, by the computing device, a correlated dataset comprising a plurality of data entries wherein each data entry comprises at least mechanical part datum and at least a correlated compatible part element datum as a function of the manufacturing request datum;
generating, at the computing device, at least a compatible part element using a classifier machine-learning model as a function of the manufacturing request datum and the correlated dataset which further comprises:
training, iteratively, the classifier machine-learning model using the correlated data set;
generating the at least a compatible part element as a function of the trained classifier machine-learning model;
simulating, using a machining simulation at a graphic processing unit operating on the computing device, machining for at least a tool of a plurality of tools for the at least a compatible part element, wherein simulating machining for the at least a tool of the plurality of tools for the at least a compatible part element comprises:
rasterizing, using the machining simulation at the graphic processing unit, each side of a plurality of sides of a mechanical part for manufacture as a function of the at least an element of user mechanical part data of the manufacturing request datum;
calculating, using the graphic processing unit, an external milling time for the at least a correlated compatible part element as a function of the at least mechanical part datum corresponding to the at least a correlated compatible part element and the machining simulation;
calculating, by the computing device, an external price output as a function of the external milling time;
determining, by the computing device, a price ratio output as a function of the external price output, wherein determining the price ratio output further comprises:
generating a loss function of a part specific variable;
minimizing the loss function; and
determining, by the computing device, a predictive close rate as a function of the price ratio output and a determined threshold.
2. The method of claim 1 , further comprising:
calculating, using the graphic processing unit operating on the computing device, a volume removed by the at least a tool of the plurality of tools for the at least a correlated compatible part element.
3. The method of claim 1 , further comprising:
calculating, using the graphic processing unit operating on the computing device, a surface area finished by the at least a tool of the plurality of tools for the at least a correlated compatible part element.
4. The method of claim 1 , further comprising:
calculating, using the graphic processing unit operating on the computing device, a material removal rate of the at least a tool of the plurality of tools for the at least a correlated compatible part element.
5. The method of claim 4 , further comprising:
calculating, using the supervised machine learning model, the external milling time as a function of relating the manufacturing request datum to the material removal rate.
6. The method of claim 1 , further comprising:
calculating, using the graphic processing unit operating on the computing device, an area removal rate of the at least a tool of the plurality of tools for the at least a correlated compatible part element.
7. The method of claim 6 , further comprising:
calculating, using the supervised machine learning model, the external milling time as a function of relating the manufacturing request datum to the area removal rate.
8. The method of claim 6 , further comprising:
calculating, using the graphic processing unit operating on the computing device, the area removal rate of a side wall, horizontal wall and curved surface of the at least a tool of the plurality of tools for the at least a correlated compatible part element.
9. The method of claim 1 , further comprising:
z-buffering, using the machining simulation at the graphic processing unit, each side of the plurality of sides of the mechanical part for manufacture as a function of the at least an element of user mechanical part data of the manufacturing request datum.
10. A system for predicting a price of any subtractively manufactured part utilizing artificial intelligence, wherein the system comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive a manufacturing request datum, wherein the manufacturing request datum comprises at least an element of user mechanical part data;
parse the manufacturing request datum for the at least an element of user mechanical part data using a language processing module;
select a correlated dataset comprising a plurality of data entries wherein each data entry comprises at least mechanical part datum and at least a correlated compatible part element datum as a function of the manufacturing request datum;
generate at least a compatible part element at a classifier machine-learning model as a function of the manufacturing request datum and the correlated dataset which further comprises:
training, iteratively, the classifier machine-learning model using the correlated data set;
generating the at least a compatible part element as a function of the trained classifier machine-learning model;
simulate machining for at least a tool of a plurality of tools for the at least a compatible part element, wherein simulating the machining comprises:
rasterizing, using the machining simulation at the graphic processing unit, each side of a plurality of sides of a mechanical part for manufacture as a function of the at least an element of user mechanical part data of the manufacturing request datum;
calculate an external milling time for the at least a correlated compatible part element as a function of the at least mechanical part datum corresponding to the at least a correlated compatible part element and the machining simulation using the graphic processing unit;
calculate external price output as a function of the external milling time;
determine a price ratio output as a function of the external price output, wherein determining the price ratio output further comprises:
generating a loss function of a part specific variable; and
minimizing the loss function; and
determine a predictive close rate as a function of the price ratio output and a determined threshold.
11. The system of claim 10 , wherein calculating the external milling time further comprises calculating a volume removed by the at least a tool of the plurality of tools for the at least a correlated compatible part element.
12. The system of claim 10 , wherein calculating the external milling time further comprises calculating a surface area finished by the at least a tool of the plurality of tools for the at least a correlated compatible part element.
13. The system of claim 10 , wherein calculating the external milling time further comprises calculating a material removal rate of the at least a tool of the plurality of tools for the at least a correlated compatible part element.
14. The system of claim 13 , wherein the supervised machine learning model is further configured to calculate the external milling time as a function of relating the manufacturing request datum to the material removal rate.
15. The system of claim 10 , wherein calculating the external milling time further comprises calculating an area removal rate of the at least a tool of the plurality of tools for the at least a correlated compatible part element.
16. The system of claim 15 , wherein the supervised machine learning model is further configured to calculate the external milling time as a function of relating the manufacturing request datum to the area removal rate.
17. The system of claim 15 , wherein calculating the area removal rate comprises calculating the area removal rate of a side wall, horizontal wall and curved surface of the at least a tool of the plurality of tools for the at least a correlated compatible part element.
18. The system of claim 10 , wherein simulating the machining further comprises z-buffering, using the machining simulation at the graphic processing unit, each side of the plurality of sides of the mechanical part for manufacture as a function of the at least an element of user mechanical part data of the manufacturing request datum.