Abstract: A process prepares a polymer-coated hard shell capsule, suitable as container for pharmaceutical or nutraceutical biologically active ingredients. The hard shell capsule can include a body and a cap. In the closed state, the cap overlaps the body either in a pre-locked state or in a final-locked state. The hard shell capsule can be provided in the pre-locked state and spray-coated with a coating solution, suspension, or dispersion that includes a polymer or a mixture of polymers to create a coating layer which covers the outer surface of the hard shell capsule in the pre-locked state.

Abstract: A method of reading a tag located on an object, wherein the tag includes tag values associated with the object, where the method includes obtaining a first sensor output, by reading the tag using a tag reader, the first sensor output being at least one of an erroneous and incomplete representation of the tag values of the tag and includes deriving at least one tag value from the first sensor output using a model, where the model is based on a first set of sensor outputs generated by the tag reader by reading a plurality of tags and tag values associated with the plurality of tags, and where the first set of sensor outputs includes a plurality of sensor outputs, where each sensor output includes at least one of an erroneous and incomplete representation of at least one tag value of the corresponding tag.

Abstract: A method provides predictions of key performance indicators of a product variant of a product family manufactured by a complex manufacturing system in a manufacturing process. The method provides a manufacturing operation model for each manufacturing operation type used to manufacture a product variant of the product family. Via the complex manufacturing system measured contributions to key performance indicators, process context data and process execution data of manufacturing operations, are provided. The model parameters of the provided manufacturing operation models are learned automatically based on collected process context data, collected process execution data, and measured contributions to key performance indicators, to update the manufacturing operation models.

Abstract: A procedure to train an online scheduling system using Reinforcement Learning agents to process any kind of product variant and any kind of machine configuration is disclosed. The novel approach of scheduling jobs or products in a flexible manufacturing system is to train Deep Reinforcement Learning agents with generated training data. One agent may represent a product and may autonomously guide the product through the manufacturing system, including decisions regarding resource allocations (which module should process which operation) and transport decisions. Dependent on the mode to be trained, the identical job-specification for same, job-specifications from the same cluster for similar, and job-specifications from different clusters for different are chosen. This solution may handle any product variant to be produced within the considered system.

Abstract: A method of reading a tag located on an object, wherein the tag includes tag values associated with the object, where the method includes obtaining a first sensor output, by reading the tag using a tag reader, the first sensor output being at least one of an erroneous and incomplete representation of the tag values of the tag and includes deriving at least one tag value from the first sensor output using a model, where the model is based on a first set of sensor outputs generated by the tag reader by reading a plurality of tags and tag values associated with the plurality of tags, and where the first set of sensor outputs includes a plurality of sensor outputs, where each sensor output includes at least one of an erroneous and incomplete representation of at least one tag value of the corresponding tag.

Abstract: An automated method determines a safety zone for a robot. The robot carries out operations along a specified trajectory. For collision-free operation, a safety zone is determined by: dividing the specified trajectory into a plurality of subtrajectories; determining a plurality of fine-grained envelope cuboids around extreme points of each subtrajectory; and determining a number of optimized envelope cuboids from an enlargement of individual fine-grained envelope cuboids in relation to the volume occupied by the enlarged fine-grained envelope cuboids. The optimized envelope cuboids determined in this way form the safety zone for the trajectory. This automated method can be expanded to multiple trajectories of a robot, multiple robots, and replanning a trajectory for an occupied semaphore zone.

Abstract: A method provides predictions of key performance indicators of a product variant of a product family manufactured by a complex manufacturing system in a manufacturing process. The method provides a manufacturing operation model for each manufacturing operation type used to manufacture a product variant of the product family. Via the complex manufacturing system measured contributions to key performance indicators, process context data and process execution data of manufacturing operations, are provided. The model parameters of the provided manufacturing operation models are learned automatically based on collected process context data, collected process execution data, and measured contributions to key performance indicators, to update the manufacturing operation models.

Abstract: The invention relates to a method for adjusting a protective function during operation of a machine (1), in which a transfer region (5) is monitored by a plurality of protective devices (L1, L2), the transfer region (5) being arranged between a risk region (2), in which a dangerous movement is performed by the machine (1), and a surrounding region (3), the transfer region (5) being monitored by first protective devices (L1) in respective first monitoring directions (UI) and, independently thereof, by second protective devices (L2) in respective second monitoring directions (U2), at least one of the first protective devices (L1) and at least one of the second protective devices (L2) being arranged relative to one another in such a way that the first monitoring direction (UI) of the at least one first protective device (L1) and the second monitoring device (U2) of the at least one second protective device (L2) have a point of intersection (S).

Abstract: The invention relates to a method for adjusting a protective function during operation of a machine (1), in which a transfer region (5) is monitored by a plurality of protective devices (L1, L2), the transfer region (5) being arranged between a risk region (2), in which a dangerous movement is performed by the machine (1), and a surrounding region (3), the transfer region (5) being monitored by first protective devices (L1) in respective first monitoring directions (UI) and, independently thereof, by second protective devices (L2) in respective second monitoring directions (U2), at least one of the first protective devices (L1) and at least one of the second protective devices (L2) being arranged relative to one another in such a way that the first monitoring direction (UI) of the at least one first protective device (L1) and the second monitoring device (U2) of the at least one second protective device (L2) have a point of intersection (S).

Abstract: An automated method determines a safety zone for a robot. The robot carries out operations along a specified trajectory. For collision-free operation, a safety zone is determined by: dividing the specified trajectory into a plurality of subtrajectories; determining a plurality of fine-grained envelope cuboids around extreme points of each subtrajectory; and determining a number of optimized envelope cuboids from an enlargement of individual fine-grained envelope cuboids in relation to the volume occupied by the enlarged fine-grained envelope cuboids. The optimized envelope cuboids determined in this way form the safety zone for the trajectory. This automated method can be expanded to multiple trajectories of a robot, multiple robots, and replanning a trajectory for an occupied semaphore zone.