Methods and systems for predicting a price of any subtractively manufactured part utilizing artificial intelligence at a computing device

1. A method for predicting a price of any 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 further comprises at least an element of user mechanical part data;

parsing, by a language processing module, the manufacturing request datum for the at least an element of user mechanical part data by performing a dependency parsing process;

selecting, by the computing device, a correlated dataset containing a plurality of data entries wherein each dataset contains at least a datum of mechanical part data and at least a first correlated compatible part element as a function of the manufacturing request datum;

generating, at a clustering unsupervised machine-learning model operating on the computing device, at least a second correlated compatible part element as a function of the manufacturing request datum and the correlated dataset, wherein the unsupervised machine-learning model includes an anomaly detection model, wherein the anomaly detection model detects and examines data outliers in the mechanical part data;

receiving, at a supervised machine-learning model operating on the computing device, a first training set as a function of the at least an element of user mechanical part data and the at least a second correlated compatible part element;

training the supervised machine-learning model as a function of the first training set to generate a trained supervised machine-learning model, wherein a correlation between the at least a first correlated compatible part element and the at least a second correlated compatible part element is combined with a result of the training of the supervised machine-learning model to add new criteria for a new supervised process to derive a relationship;

generating, at the trained supervised machine-learning model as a function of the first training set operating on the computing device, an external price output as a function of relating the manufacturing request datum to at least a part element, wherein the at least a part datum is provided to the trained supervised machine-learning model as an input to output the external price output;

determining a price ratio output, by the computing device, 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.

2. The method of claim 1 , wherein the at least an element of user mechanical part data further comprises at least one of:

an element of part count data;

an element of part face count data; and

an element of part material data.

3. The method of claim 1 , wherein receiving a manufacturing request datum further comprises:

storing the manufacturing request datum in an internal database.

4. The method of claim 1 , wherein the method of predicting a price further comprises:

calculating, by a graphic processing unit, the volume removed by each tool of the plurality of tools for the at least a correlated compatible part element as a function of an internal database.

5. The method of claim 1 , wherein the method of predicting a price further comprises:

calculating, by a graphic processing unit, the material removal rate of each tool of the plurality of tools for the at least a correlated compatible part element as a function of an internal database.

6. The method of claim 1 , wherein the method of predicting a price further comprises:

calculating, by a graphic processing unit, the surface area finished by each tool of the plurality of tools for the at least a correlated compatible part element as a function of an internal database.

7. The method of claim 1 , wherein the method of predicting a price further comprises:

calculating, by a graphic processing unit, the area removal rate for each tool of the plurality of tools for the at least a correlated compatible part element as a function of an internal database.

8. The method of claim 1 , wherein the first training set further comprises the correlated dataset.

9. The method of claim 1 , wherein generating an external price output further comprises:

calculating the external milling time as a function of relating the manufacturing request datum to the material removal rate by each tool of the plurality of tools and to the area removal rate for each tool of the plurality of tools.

10. The method of claim 1 , wherein the part specific variable further comprises at least one of:

an element of tool selection datum;

an element of cutting parameter datum; and

an element of tool path strategy datum.

11. A system for predicting a price of any subtractively manufactured part utilizing artificial intelligence at a computer device, the system comprising:

at least a processor;

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 further 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, wherein parsing the manufacturing request datum further comprises performing a dependency parsing process;

select a correlated dataset containing a plurality of data entries wherein each dataset contains at least a datum of mechanical part data and at least a first correlated compatible part element as a function of the at least a manufacturing request datum;

generate, at a clustering unsupervised machine-learning model, at least a second compatible part element as a function of the manufacturing request datum and the correlated dataset, wherein the unsupervised machine-learning model includes an anomaly detection model, wherein the anomaly detection model detects and examines data outliers in the mechanical part data; and

receive, at a supervised machine-learning model, a first training set as a function of the at least an element of user mechanical part data and the at least a second correlated compatible part element;

train the supervised machine-learning model as a function of the first training set to generate a trained supervised machine-learning model, wherein a correlation between the at least a first correlated compatible part element and the at least a second correlated compatible part element is combined with a result of the training of the supervised machine-learning model to add new criteria for a new supervised process to derive a relationship;

generate, at the trained supervised machine-learning model, an external price output as a function of relating the manufacturing request datum to at least a part element, wherein the at least a part datum is provided to the trained supervised machine-learning model as an input to output the 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.

12. The system of claim 11 , wherein the at least an element of user mechanical part data further comprises at least one of:

an element of part count data;

an element of part face count data; and

an element of part material data.

13. The system of claim 11 , wherein the computing device is further configured to:

store the manufacturing request datum in an internal database.

14. The system of claim 11 , wherein the computing device is further configured to:

calculate the volume removed by each tool of the plurality of tools for the at least a correlated compatible part element as a function of the at least an internal request datum.

15. The system of claim 11 , wherein the computing device is further configured to:

calculate the material removal rate of each tool of the plurality of tools for the at least a correlated compatible part element as a function of an internal database.

16. The system of claim 11 , wherein the computing device is further configured to:

calculate the surface area finished by each tool of the plurality of tools for the at least a correlated compatible part element as a function of an internal database.

17. The system of claim 11 , wherein the computing device is further configured to:

calculate the area removal rate for each tool of the plurality of tools for the at least a correlated compatible part element as a function of an internal database.

18. The system of claim 11 , wherein the first training set further comprises the correlated dataset.

19. The system of claim 11 , wherein generating an external price output further comprises:

calculating the external milling time as a function of relating the manufacturing request datum to the material removal rate by each tool of the plurality of tools and to the area removal rate for each tool of the plurality of tools.

20. The system of claim 11 , wherein the part specific variable further comprises at least one of:

an element of tool selection datum;

an element of cutting parameter datum; and

an element of tool path strategy datum.