A method of identifying dental consumables including at least one of a dental blank (2) and a dental tool (3) equipped into a dental tool machine (1). The method includes: a step of colliding the dental tool (3) with the dental blank (2) or a dental blank holder of the dental tool machine (1) and a step of detecting a signal indicative of the collision. The method also includes a step of analyzing the detected signal through trained artificial intelligence; and a step of identifying the type and/or condition of at least one of the dental consumables based on the analysis.

Robotic systems and methods for robotic arthroplasty. The robotic system includes a machining station and a guidance station. The guidance station tracks movement of various objects in the operating room, such as a surgical tool, a humerus of a patient, and a scapula of the patient. The guidance station tracks these objects for purposes of controlling movement of the surgical tool relative to virtual cutting boundaries or other virtual objects associated with the humerus and scapula to facilitate preparation of bone to receive a shoulder implant system. The virtual objects are located based on density data of the bone such that, when one or more shoulder implants are fully seated in the bone, distal portions of the implants are located in a first region of the bone having a density characteristic greater than an adjacent second region of the bone.

Robotic systems and methods for robotic arthroplasty. The robotic system includes a machining station and a guidance station. The guidance station tracks movement of various objects in the operating room, such as a surgical tool, a humerus of a patient, and a scapula of the patient. The guidance station tracks these objects for purposes of controlling movement of the surgical tool relative to virtual cutting boundaries or other virtual objects associated with the humerus and scapula to facilitate preparation of bone to receive a shoulder implant system. The virtual objects are located based on density data of the bone such that, when one or more shoulder implants are fully seated in the bone, distal portions of the implants are located in a first region of the bone having a density characteristic greater than an adjacent second region of the bone.

A surgical robotic system and method involve a manipulator including a plurality of links and joints and a tool coupled to the manipulator. Controller(s) generate a first tool path to remove a first portion of material from the bone and control the manipulator to position the tool for movement along the first tool path to remove the first portion. The controller(s) sense interaction between the tool and the bone during movement of the tool along the first tool path and generate a second tool path to remove a second portion of material from the bone. Generation of the second tool path is based, at least in part, on the sensed interaction between the tool and the bone during movement along the first tool path. The controller(s) control the manipulator to position the tool for movement along the second tool path to remove the second portion.

A method of operating a robotic system to efficiently remove material from a workpiece based on a density distribution of the material of the workpiece. The density distribution of the material of the workpiece is determined from a three-dimensional representation and evaluated by classifying the plurality of points or voxels into a first density classification and a second density classification. A navigation computer generates a first tool path and a second tool path for the tool based on the evaluated density distribution. The first tool path is associated with the first density classification, and the second tool path is associated with the second density classification. The position of the tool relative to the workpiece is tracked with a navigation computer and controlled with a manipulator controller based on the generated tool path to remove material along the first tool path, and remove material along the second tool path.

A design application generates feasible engineering designs that satisfy criteria associated with a particular engineering problem. The design application receives input that outlines a specific engineering problem to be solved, and then synthesizes a problem specification based on this input. The design application then searches a database to identify different classes of approaches to solving the design problem set forth in the problem specification. The design application then selects one or more such classes of approaches, and generates a spectrum of potential design solutions for each such approach. The generated solutions may then be evaluated to determine the degree to which the problems specification has been met.

A computer-implemented method for predicting material properties in an Additive Manufacturing (AM) process is provided. The method comprises: receiving sensor data during the build of a metallic component using the AM process wherein the sensor data includes time-series temperature data of a surface of the metallic component recorded by a photodiode and time-series temperature data of a surface of the metallic component recorded by a pyrometer; receiving ICME (Integrated Computational Materials Engineering) model output data for building the component wherein the ICME model output data includes predicted melt pool dimensions time-series data, predicted melt temperature time-series data, and predicted defects forming as a result of melt pool evolution and movement; and estimating using the received sensor data and the received ICME model output data one or more material properties associated with the metallic component using a material property prediction module configured to predict one or more of the material properties.

A design application is configured to determine design problem geometry and design criteria associated with a design problem to be solved. Based on this information, the design application identifies one or more design approaches to creating a custom material having specific material attributes needed to solve the design problem. The design application then executes the design approaches to create material designs that reflect one or more custom materials. With these designs as input, a manufacturing machine may then construct physical instances of those custom materials. A given custom material may have a unique combination of material attributes potentially not found among existing materials. Additionally, a design fabricated from a custom material may better satisfy the design criteria than a design fabricated from a known material.

A computer-implemented method for predicting material properties in an Additive Manufacturing (AM) process is provided. The method comprises: receiving sensor data during the build of a metallic component using the AM process wherein the sensor data includes time-series temperature data of a surface of the metallic component recorded by a photodiode and time-series temperature data of a surface of the metallic component recorded by a pyrometer; receiving ICME (Integrated Computational Materials Engineering) model output data for building the component wherein the ICME model output data includes predicted melt pool dimensions time-series data, predicted melt temperature time-series data, and predicted defects forming as a result of melt pool evolution and movement; and estimating using the received sensor data and the received ICME model output data one or more material properties associated with the metallic component using a material property prediction module configured to predict one or more of the material properties.

In some examples, a computing device may receive, from a user device, inputs specifying a performance parameter and at least one of a material or manufacturing process. The computing device may determine one or more manufacturing processes corresponding to the inputs, and may determine at least one of a machine learning model or a simulation model corresponding to at least one manufacturing process related to the inputs. The computing device may input information related to a plurality of candidate materials into the machine learning model or simulation model to determine a predicted property of the respective candidate materials related to the performance parameter. In addition, the computing device may compare the predicted properties with each other to select at least one of a selected material or a selected manufacturing process, and may send, to the user device, information related to the selected material or manufacturing process.