Existing cognitive robotic applications follow a practice of building specific applications for specific use cases. However, the knowledge of the world and the semantics are common for a robot for multiple tasks. In this disclosure, to enable usage of knowledge across multiple scenarios, a method and system for ontology guided indoor scene understanding for cognitive robotic tasks is described where in scenes are processed based on techniques filtered based on querying ontology with relevant objects in perceived scene to generate a semantically rich scene graph. Herein, an initially manually created ontology is updated and refined in online fashion using external knowledge-base, human robot interaction and perceived information. This knowledge helps in semantic navigation, aids in speech, and text based human robot interactions.

An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.

A system for tuning of axis control of a multi-axis machine and a method of operating the same are provided. The system includes a knowledge base for acquiring and maintaining factual knowledge associated with the tuning of the axis control. The factual knowledge has a uniform ontology a uniform data representation, and includes known input facts associated with known output facts. The system further includes an inference unit for automatically inferring new output facts associated with given new input facts in accordance with the factual knowledge.

A system for tuning of axis control of a multi-axis machine and a method of operating the same are provided. The system includes a knowledge base for acquiring and maintaining factual knowledge associated with the tuning of the axis control. The factual knowledge has a uniform ontology a uniform data representation, and includes known input facts associated with known output facts. The system further includes an inference unit for automatically inferring new output facts associated with given new input facts in accordance with the factual knowledge.

Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch.

An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.

A manufacturing environment having a machine tool, a measuring device, a storage medium, and having a computer programmed to control: a) chip producing machining of a first workpiece in a machine tool, b) acquiring at least two machine parameters of the machine tool during the chip producing machining of the first workpiece, c) storing the machine parameters in the storage medium, wherein the storing is performed with assignment to the first workpiece, and d) repeating steps a) to c) for a number of n workpieces; and, after one of steps a) to d), or later, triggering a testing method including: (i) selecting at least one of the workpieces, (ii) performing an automated test of the at least one selected workpiece using the measuring device, or (iii) performing a processor-controlled evaluation of the automated test to classify the at least one selected workpiece as a good part, or a reject part.

Existing cognitive robotic applications follow a practice of building specific applications for specific use cases. However, the knowledge of the world and the semantics are common for a robot for multiple tasks. In this disclosure, to enable usage of knowledge across multiple scenarios, a method and system for ontology guided indoor scene understanding for cognitive robotic tasks is described where in scenes are processed based on techniques filtered based on querying ontology with relevant objects in perceived scene to generate a semantically rich scene graph. Herein, an initially manually created ontology is updated and refined in online fashion using external knowledge-base, human robot interaction and perceived information. This knowledge helps in semantic navigation, aids in speech, and text based human robot interactions.