Manufacturing optimization using a multi-tenant machine learning platform

Patent metadata

Field	Value
Publication number	US-11520322-B2
Application number	US-202016883487-A
Country	US
Kind code	B2
Filing date	May 26, 2020
Priority date	May 24, 2019
Publication date	Dec 6, 2022
Grant date	Dec 6, 2022

Abstract

Techniques for manufacturing optimization using a multi-tenant machine learning platform are disclosed. A method for manufacturing optimization includes: obtaining physical sensor data, by a manufacturing device associated with a tenant of a multi-tenant machine learning platform; determining, by a machine learning spoke system associated with the tenant, a machine learning parameter based on at least the physical sensor data; preventing exposure of the first physical sensor data of the first manufacturing device to any other tenant of the multi-tenant machine learning platform; transmitting the machine learning parameter from the machine learning spoke system to a machine learning hub system of the multi-tenant machine learning platform; and updating, by the machine learning hub system, a multi-tenant machine learning model based at least on the machine learning parameter.

First claim

What is claimed is:

1. A method comprising:
obtaining first physical sensor data, by a first printing machine associated with a first tenant of a multi-tenant machine learning platform;
comparing a print command sent to the first printing machine with a resultant material printed by the first printing machine;
determining, by a first machine learning spoke system associated with the first tenant, a first machine learning parameter based on at least the first physical sensor data, the first machine learning parameter comprising information pertaining to the comparison of the print command sent to the first printing machine with the resultant material printed by the first printing machine;
preventing exposure of the first physical sensor data of the first printing machine to any other tenant of the multi-tenant machine learning platform;
transmitting the first machine learning parameter from the first machine learning spoke system to a machine learning hub system of the multi-tenant machine learning platform; and
updating, by the machine learning hub system, a multi-tenant machine learning model based at least on the first machine learning parameter.

2. The method of claim 1 , further comprising:
obtaining second physical sensor data from a second manufacturing device associated with a second tenant of the multi-tenant machine learning platform;
determining, by a second machine learning spoke system associated with the second tenant, a second machine learning parameter based on at least the second physical sensor data;
preventing exposure of the second physical sensor data of the second manufacturing device to any other tenant of the multi-tenant machine learning platform;
transmitting the second machine learning parameter from the second machine learning spoke system to the machine learning hub system; and
updating, by the machine learning hub system, the multi-tenant machine learning model based at least on the second machine learning parameter.

3. The method of claim 2 , further comprising:
executing the multi-tenant machine learning model to determine a manufacturing optimization; and
adjusting the second manufacturing device, associated with the second tenant of the multi-tenant learning platform, based at least on the manufacturing optimization.

4. The method of claim 3 , further comprising:
transmitting the manufacturing optimization from the machine learning hub system to the second machine learning spoke system associated with the second tenant,
wherein adjusting the second manufacturing device is performed by the second machine learning spoke system responsive to receiving the manufacturing optimization from the machine learning hub system.

5. The method of claim 3 , wherein the second tenant is subscribed to the multi-tenant machine learning platform to obtain the manufacturing optimization without contributing any machine learning parameter to the machine learning hub system.

6. The method of claim 1 , wherein obtaining the first physical sensor data comprises obtaining data from a laser sensor of the first printing machine.

7. A system comprising:
a first one or more processors; and
a first one or more non-transitory computer-readable media storing instructions that, when executed by the first one or more processors, cause a first machine learning spoke system associated with a first tenant of a multi-tenant machine learning platform to perform operations comprising:
obtaining first physical sensor data from a first printing machine;
comparing a print command sent to the first printing machine with resultant material printed by the first printing machine;
determining a first machine learning parameter based on at least the first physical sensor data, the first machine learning parameter comprising information pertaining to the comparison of the print command sent to the first printing machine with the resultant material printed by the first printing machine;
preventing exposure of the first physical sensor data of the first printing machine to any other tenant of the multi-tenant machine learning platform; and
transmitting the first machine learning parameter to a machine learning hub system of the multi-tenant machine learning platform,
wherein the machine learning hub system is configured to update a multi-tenant machine learning model based at least on the first machine learning parameter.

8. The system of claim 7 , further comprising:
a second one or more processors; and
a second one or more non-transitory computer-readable media storing instructions that, when executed by a second one or more processors, cause a second machine learning spoke system associated with a second tenant of the multi-tenant machine learning platform to perform operations comprising:
obtaining second physical sensor data from a second manufacturing device;
determining a second machine learning parameter based on at least the second physical sensor data;
preventing exposure of the second physical sensor data of the second manufacturing device to any other tenant of the multi-tenant machine learning platform; and
transmitting the second machine learning parameter to the machine learning hub system of the multi-tenant machine learning platform,
wherein the machine learning hub system is further configured to update the multi-tenant machine learning model based at least on the second machine learning parameter.

9. The system of claim 7 , further comprising:
a second one or more processors; and
a second one or more non-transitory computer-readable media storing instructions that, when executed by a second one or more processors, cause a second machine learning spoke system associated with a second tenant of the multi-tenant machine learning platform to perform operations comprising:
receiving a manufacturing optimization, generated using the multi-tenant machine learning model, from the machine learning hub system; and
adjusting a second manufacturing device based at least on the manufacturing optimization.

10. The system of claim 9 , wherein the second tenant is subscribed to the multi-tenant machine learning platform to obtain the manufacturing optimization without contributing any machine learning parameter to the machine learning hub system.

11. The system of claim 7 , wherein the first one or more processors and the first one or more non-transitory computer-readable media are components of the first printing machine.

12. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause a machine learning hub system of a multi-tenant machine learning platform to perform operations comprising:
receiving a first machine learning parameter from a first machine learning spoke system associated with a first tenant of the multi-tenant machine learning platform, the first machine learning parameter having been generated by the first machine learning spoke system based at least on first physical sensor data obtained from a first printing machine;
preventing exposure of the first physical sensor data of the first printing machine to any other tenant of the multi-tenant machine learning platform; and
updating a multi-tenant machine learning model based at least on the first machine learning parameter.

13. The system of claim 12 , the one or more non-transitory computer-readable media further storing instructions that, when executed by the one or more processors, cause the machine learning hub system to perform operations comprising:
receiving a second machine learning parameter from a second machine learning sp

Assignees

• Markforged Inc

Inventors

• Mark Gregory Thomas
• Walsh Corey Hazeltine
• Jones Bruce David

Classifications

• G05B19/41885 Primary

  characterised by modeling, simulation of the manufacturing system · CPC title

• B22F12/90

  Means for process control, e.g. cameras or sensors · CPC title

• B29C64/386

  Data acquisition or data processing for additive manufacturing · CPC title

• B33Y50/00

  Data acquisition or data processing for additive manufacturing · CPC title

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title