A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.

A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.

A method and an apparatus of a powder bed fusion additive manufacturing system that enables a quick change in the optical beam delivery size and intensity across locations of a print surface for different powdered materials while ensuring high availability of the system. A dynamic optical assembly containing a set of lens assemblies of different magnification ratios and a mechanical assembly may change the magnification ratios as needed. The dynamic optical assembly may include a transitional and rotational position control of the optics to minimize variations of the optical beam sizes across the print surface.

A method and an apparatus for collecting powder samples in real-time in powder bed fusion additive manufacturing may involves an ingester system for in-process collection and characterizations of powder samples. The collection may be performed periodically and uses the results of characterizations for adjustments in the powder bed fusion process. The ingester system of the present disclosure is capable of packaging powder samples collected in real-time into storage containers serving a multitude purposes of audit, process adjustments or actions.

Embodiments of the present disclosure are directed to additive manufacturing apparatuses, cleaning stations incorporated therein, and methods of cleaning using the cleaning stations.

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.

A debinder provides for debinding printed green parts in an additive manufacturing system. The debinder can include a storage chamber, a process chamber, a distill chamber, a waste chamber, and a condenser. The storage chamber stores a liquid solvent for debinding the green part. The process chamber debinds the green part using a volume of the liquid solvent transferred from the storage chamber. The distill chamber collects a solution drained from the process chamber and produces a solvent vapor from the solution. The condenser condenses the solvent vapor to the liquid solvent and transfer the liquid solvent to the storage chamber. The waste chamber collects a waste component of the solution.

A debinder provides for debinding printed green parts in an additive manufacturing system. The debinder can include a storage chamber, a process chamber, a distill chamber, a waste chamber, and a condenser. The storage chamber stores a liquid solvent for debinding the green part. The process chamber debinds the green part using a volume of the liquid solvent transferred from the storage chamber. The distill chamber collects a solution drained from the process chamber and produces a solvent vapor from the solution. The condenser condenses the solvent vapor to the liquid solvent and transfer the liquid solvent to the storage chamber. The waste chamber collects a waste component of the solution.

A method and an apparatus for collecting powder samples in real-time in powder bed fusion additive manufacturing may involves an ingester system for in-process collection and characterizations of powder samples. The collection may be performed periodically and uses the results of characterizations for adjustments in the powder bed fusion process. The ingester system of the present disclosure is capable of packaging powder samples collected in real-time into storage containers serving a multitude purposes of audit, process adjustments or actions.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.