Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines. Reinforcement models dynamically adjust equipment settings for producing products to account for equipment performance degradation over time and changes in operation performance.

The system performs a process for creating robotic control code for manufacturing products. A Designer UI displays virtual parts and receives inputs for processing and assembling the virtual parts that are required to create a virtual product. The designer identifies options for processing and assembling the virtual parts which are displayed on the user interface. The robot abilities and the options are selected to optimize a metric of the product manufacturing. The inventive toolset produces the robot control codes for performing a sequence of robotic abilities with the selected options to product the product. The robot control codes are used by a simulator which manipulates virtual robots and virtual parts to create a virtual product to check the robot control code. The verified robot control code is used to control real robots to create the product.

The system performs a process for creating robotic control code for manufacturing products. A Designer UI displays virtual parts and receives inputs for processing and assembling the virtual parts that are required to create a virtual product. The designer identifies options for processing and assembling the virtual parts which are displayed on the user interface. The robot abilities and the options are selected to optimize a metric of the product manufacturing. The inventive toolset produces the robot control codes for performing a sequence of robotic abilities with the selected options to product the product. The robot control codes are used by a simulator which manipulates virtual robots and virtual parts to create a virtual product to check the robot control code. The verified robot control code is used to control real robots to create the product.

Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines. Reinforcement models dynamically adjust equipment settings for producing products to account for equipment performance degradation over time and changes in operation performance.

The system performs a process for creating robotic control code for manufacturing products. A Designer UI displays virtual parts and receives inputs for processing and assembling the virtual parts that are required to create a virtual product. The designer identifies options for processing and assembling the virtual parts which are displayed on the user interface. The robot abilities and the options are selected to optimize a metric of the product manufacturing. The inventive toolset produces the robot control codes for performing a sequence of robotic abilities with the selected options to product the product. The robot control codes are used by a simulator which manipulates virtual robots and virtual parts to create a virtual product to check the robot control code. The verified robot control code is used to control real robots to create the product.

Embodiments herein generally relate to methods, systems and devices for automated assembly of building structures. In at least one embodiment, there is provided a method for automated assembly of building structures, the method comprises analyzing assembly data associated with a building structure; based on the analyzing, determining an assembly sequence for assembling building parts to construct the building structure, wherein the assembly sequence comprises a plurality of assembly tasks; generating robot-specific control instructions, for each of one or more assembly robots in a robotic assembly cell, to execute the assembly sequence; and transmitting the robot-specific control instructions to the one or more assembly robots in the robotic assembly cell.

The present disclosure relates to a work-in-process management control method and a work-in-process management control system using the work-in-process management control method. The work-in-process management control method includes: receiving, from first process devices that execute a first process, an out-of-process request for work-in-process; determining whether a second process corresponding to the out-of-process request is a process subjected to control; in the case where the second process is not a process subjected to control, shifting out the work-in-process in response to the out-of-process request; in the case where the second process is a process subjected to control, determining whether a current quantity of the work-in-process in use for the second process exceeds a control threshold in the second process; and in the case where the current quantity of the work-in-process in use for the second process exceeds a control threshold in the second process, stopping responding to the out-of-process request.

Embodiments herein generally relate to methods, systems and devices for automated assembly of building structures. In at least one embodiment, there is provided a robotic assembly cell for assembling building structures. The robotic assembly cell comprises a perception sensor system; one or more assembly robots; and at least one processor operable to: receive instructions corresponding to an assembly sequence for assembling a building structure; determine a plurality of building parts required for assembling the building structure based on the assembly sequence; identify each building part within a facility, based on sensor data from the perception sensor system; configure the assembly robot to retrieve the target pieces; and configure the assembly robot to assemble the target pieces according to the assembly sequence.

The present disclosure relates to a work-in-process management control method and a work-in-process management control system using the work-in-process management control method. The work-in-process management control method includes: receiving, from first process devices that execute a first process, an out-of-process request for work-in-process; determining whether a second process corresponding to the out-of-process request is a process subjected to control; in the case where the second process is not a process subjected to control, shifting out the work-in-process in response to the out-of-process request; in the case where the second process is a process subjected to control, determining whether a current quantity of the work-in-process in use for the second process exceeds a control threshold in the second process; and in the case where the current quantity of the work-in-process in use for the second process exceeds a control threshold in the second process, stopping responding to the out-of-process request.

Embodiments herein generally relate to methods, systems and devices for automated assembly of building structures. In at least one embodiment, there is provided a robotic assembly cell for assembling building structures. The robotic assembly cell comprises a perception sensor system; one or more assembly robots; and at least one processor operable to: receive instructions corresponding to an assembly sequence for assembling a building structure; determine a plurality of building parts required for assembling the building structure based on the assembly sequence; identify each building part within a facility, based on sensor data from the perception sensor system; configure the assembly robot to retrieve the target pieces; and configure the assembly robot to assemble the target pieces according to the assembly sequence.