The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

Parameters of a set of tools are stored on a storage device. The tools are part of a manufacturing assembly usable for removing one or more support structures from a part. The support structures are formed with the part to facilitate additive manufacturing of the part. A near-net shape is modeled which includes the part combined with the support structures. A process plan is developed that includes subtractive manufacturing operations by the manufacturing assembly that remove the support structures. The process plan repeatedly updates the near-net shape as each one of the support structures is incrementally removed.

A systematic approach to constructing process plans for hybrid manufacturing is provided. The process plans include arbitrary combinations of AM and SM processes. Unlike the suboptimal conventional practice, the sequence of AM and SM modalities is not fixed beforehand. Rather, all potentially viable process plans to fabricate a desired target part from arbitrary alternating sequences of pre-defined AM and SM modalities are explored in a systematic fashion. Once the state space of all process plans has been enumerated in terms of a partially ordered set of states, advanced artificial intelligence (AI) planning techniques are utilized to rapidly explore the state space, eliminate invalid process plans, for instance, process plans that make no physical sense, and optimize among the valid process plans using a cost function, for instance, manufacturing time and material or process costs.

The present disclosure relates to utilizing idle processing resources of a robot to reduce future burden on such processing resources. In particular, idle processing resources are utilized to identify future scenarios, and generate reactions to such future scenarios. The generated reactions are stored, and quickly retrieved as needed if corresponding identified future scenarios occur.

A manufacturing support system may be provided. The manufacturing support system may comprise: an obtaining unit (IO) configured to obtain object data of an object to be manufactured; an artificial intelligence, Al, engine (20) configured to receive the object data as an input and to determine a hardware configuration of a manufacturing system for manufacturing the object with reference to information relating to available hardware for the manufacturing system; and an output unit (60) configured to output the determined hardware configuration.

The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

The present disclosure relates to utilizing idle processing resources of a robot to reduce future burden on such processing resources. In particular, idle processing resources are utilized to identify future scenarios, and generate reactions to such future scenarios. The generated reactions are stored, and quickly retrieved as needed if corresponding identified future scenarios occur.

The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

Algorithmic reasoning about a cutting tool assembly's space of feasible configurations can be effectively harnessed to construct a sequence of motions that guarantees a collision-free path for the tool assembly to remove each support structure in the sequence. A greedy algorithm models the motion of the cutting tool assembly through the free-spaces around the intermediate shapes of the part as the free-spaces iteratively reduce in size to the near-net shape to determine feasible points of contact for the cutting tool assembly. Each support beam is evaluated for a contact feature along the boundary of the near-net shape that constitutes a feasible point of contact. If a support beam has at least one feasible configuration at each point, the support beam is deemed ‘accessible’ and a collection of tool assembly configurations that are guaranteed to be non-colliding but which can access all points of contact of each accessible support beam can be generated.