Methods and software for manufacturing a discrete object from an additively manufactured body of material including a precursor to a discrete object and a reference feature(s)

1. An automated manufacturing system for generating a graphical representation of a discrete object to be manufactured from an additively manufactured body of material, including a precursor to a discrete object and at least a reference feature, at a computing device, the automated manufacturing system is designed and configured to:

receive a graphical computer model of the at least a precursor to the discrete object and a graphical computer model of the discrete object;

identify at least a first feature in the graphical computer model of the discrete object, and recommended joining data;

selecting a correlated dataset containing a plurality of data entries wherein each dataset contains a geometric description of at least a stored critical-to-quality shape description and a correlated shape that requires manufacture to the first tolerance;

training a machine-learning model with the correlated dataset; and

automatedly determine, at the machine-learning model, that the at least a first feature includes at least a critical-to-quality feature in the graphical computer model of the discrete object, wherein automatedly determining at the machine-learning model comprises:

determining, at the machine-learning model, the geometric description of the at least a surface feature matches the geometric description of the correlated dataset as a function of comparing the geometric description of the at least a surface feature to the correlated dataset, wherein the machine-learning model is trained by the correlated dataset;

automatedly generate, at the machine-learning model, a graphical representation of the at least a reference feature on the graphical model of the at least a precursor to the discrete object as a function of the at least a critical-to-quality feature, at least a locating feature in a support, and the recommended joining data, wherein the at least a critical-to-quality feature corresponds to a shape that requires manufacture to a first tolerance based on the correlated dataset, and wherein the support comprises a substrate for deposition of layers in an additive process; and

automatedly determine a second feature as a non-critical to quality feature to be manufactured to a second tolerance which is lower than the first tolerance, wherein the non-critical to quality feature comprises surface finish or fit.

2. The system of claim 1 , wherein automatedly determining the at least a first feature includes the at least a critical-to-quality feature further comprises extracting a geometric description of at least a surface feature depicted in the graphical computer model.

3. The system of claim 1 , wherein automatedly determining that the at least a first feature includes the at least a critical-to-quality feature further comprises detecting at least a join locus.

4. The system of claim 3 , wherein:

the graphical computer model depicts a second object mating with the graphical representation of the at least a precursor part; and

detecting the at least a join locus further comprises identifying a graphical depiction of a surface feature mating with the second object.

5. The system of claim 3 , wherein detecting the at least a join locus further comprises:

extracting a first geometric description of at least a first surface feature depicted in the graphical computer model;

selecting a second correlated dataset containing a plurality of data entries wherein each dataset contains a geometric description of the at least a surface feature and at least a correlated second surface feature of a graphical representation of the second object; and

determining, at the machine-learning model, the at least a first surface feature and the second correlated dataset are complementary forms as a function of comparing the first geometric description to the second correlated dataset, wherein the machine-learning model is trained by the second correlated dataset.

6. The system of claim 3 , wherein detecting the at least a join locus further comprises:

extracting a geometric description of at least a surface feature depicted in the graphical computer model;

selecting a third correlated dataset containing a plurality of data entries wherein each dataset contains a geometric description of the at least a surface feature and a correlated geometric description of at least a fastening feature shape description; and

determining, at the machine-learning model, the geometric description of the at least a surface matches the geometric description of the at least a fastening feature shape description as a function of comparing the geometric description of the at least a surface feature to the third correlated dataset, wherein the machine-learning model is trained by the third correlated dataset.

7. The system of claim 1 , wherein the at least a critical-to-quality feature includes a plurality of critical-to-quality features, and further comprising:

presenting the plurality of critical-to-quality features to a user via a user interface; and

receiving, via the user interface, a user selection of a critical-to-quality feature from the plurality of critical-to-quality features.

8. The system of claim 1 , wherein receiving the graphical representation of the at least a reference feature further comprises generating the graphical representation of the at least a reference feature as a function of at least a locating feature at a subtractive manufacturing device.

9. The system of claim 1 , wherein the automated manufacturing system is further designed and configured to transmit the graphical representation of the at least a reference feature on the graphical model of the at least a precursor to the discrete object to a remote device.

10. A method of generating a graphical representation of a discrete object to be manufactured from an additively manufactured body of material including a precursor to a discrete object and at least a reference feature, the method comprising:

receiving, at a computing device, a graphical computer model of the at least a precursor to the discrete object and a graphical computer model of the discrete object;

identifying, at the computing device, at least a first feature in the graphical computer model of the discrete object and recommended joining data;

selecting a correlated dataset containing a plurality of data entries wherein each dataset contains a geometric description of at least a stored critical-to-quality shape description and a correlated shape that requires manufacture to the first tolerance;

training a machine-learning model with the correlated dataset; and

automatedly determining, at the machine-learning model operating on the computing device, that the at least a first feature includes at least a critical-to-quality feature in the graphical computer model of the discrete object, wherein automatedly determining at the machine-learning model comprises:

determining, at the machine-learning model, the geometric description of the at least a surface feature matches the geometric description of the correlated dataset as a function of comparing the geometric description of the at least a surface feature to the correlated dataset, wherein the machine-learning model is trained by the correlated dataset;

automatedly generating, at the machine-learning model, a graphical representation of the at least a reference feature on the graphical model of the at least a precursor to the discrete object as a function of the at least a critical-to-quality feature, at least a locating feature in a support, and the recommended joining data, wherein the at least a critical-to-quality feature corresponds to a shape that requires manufacture to a first tolerance based on the correlated dataset, and wherein the support comprises a substrate for deposition of layers in an additive process; and

automatedly determining a second feature as a non-critical to quality feature to be manufactured to a second tolerance which is lower than the first tolerance, wherein the non-critical to quality feature comprises surface finish or fit.

11. The method of claim 10 , wherein automatedly determining the at least a critical-to-quality feature further comprises extracting a geometric description of at least a surface feature depicted in the graphical computer model.

12. The method of claim 11 , wherein automatedly determining that the at least a first feature includes the at least a critical-to-quality feature further comprises detecting at least a join locus.

13. The method of claim 12 , wherein:

the graphical computer model depicts a second object mating with the graphical representation of the at least a precursor part; and

detecting the at least a join locus further comprises identifying a graphical depiction of a surface feature mating with the second object.

14. The method of claim 12 , wherein detecting the at least a join locus further comprises:

extracting a first geometric description of at least a first surface feature depicted in the graphical computer model;

selecting a second correlated dataset containing a plurality of data entries wherein each dataset contains a geometric description of the at least a surface feature and at least a correlated second surface feature of a graphical representation of the second object; and

determining, at the machine-learning model, the at least a first surface feature and the second correlated dataset are complementary forms as a function of comparing the first geometric description to the second correlated dataset, wherein the machine-learning model is trained by the second correlated dataset.

15. The method of claim 12 , wherein detecting the at least a join locus further comprises:

extracting a geometric description of at least a surface feature depicted in the graphical computer model;

selecting a third correlated dataset containing a plurality of data entries wherein each dataset contains a geometric description of the at least a surface feature and a correlated geometric description of at least a fastening feature shape description; and

determining, at the machine-learning model, the geometric description of the at least a surface matches the geometric description of the at least a fastening feature shape description as a function of comparing the geometric description of the at least a surface feature to the third correlated dataset, wherein the machine-learning model is trained by the third correlated dataset.

16. The method of claim 10 , wherein the at least a critical-to-quality feature includes a plurality of critical-to-quality features, and further comprising:

presenting the plurality of critical-to-quality features to a user via a user interface; and

receiving, via the user interface, a user selection of a critical-to-quality feature from the plurality of critical-to-quality features.

17. The method of claim 10 , wherein receiving the graphical representation of the at least a reference feature further comprises generating the graphical representation of the at least a reference feature as a function of at least a locating feature at a subtractive manufacturing device.

18. The method of claim 10 , wherein the method further comprises transmitting, by the computing device, the graphical representation of the at least a reference feature on the graphical model of the at least a precursor to the discrete object to a remote device.