A diagnostic apparatus (10) diagnoses the existence of an abnormality in a tool for processing of a processing target by each of multiple working machines. The diagnostic apparatus (10) includes an acquirer (110) and a diagnoser (160). The acquirer (110) acquires program identification information identifying a program executed in each of the working machines, tool information indicating a type of the tool applied to processing, and transition information indicating transition of load in the working machine from the start to the end of the processing executed by execution of the program, at execution of the processing in the working machine. The diagnoser (160) diagnoses whether an index value of the load obtained from the transition information is out of a predetermined range corresponding to a combination of the program identification information, the tool information, and machine information identifying the working machine that transmits the transition information.

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.

Tool wear monitoring is critical for quality and precision of manufacturing of parts in the machining industry. Existing tool wear monitoring and prediction methods are sensor based, costly and pose challenge in ease of implementation. Embodiments herein provide method and system for monitoring tool wear to estimate Remaining Useful Life (RUL) of a tool in machining is disclosed. The method provides a tool wear model, which combines tool wear physics with data fitting, capture practical considerations of a machining system, which makes the tool wear prediction and estimated RUL more stable, reliable and robust. Further, provides cost effective and practical solution. The disclosed physics based tool wear model for RUL estimation captures privilege of physics of tool wear and easily accessible data from CNC machine to monitor and predict tool wear and RUL of the tool in real-time.

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.

A diagnostic apparatus (10) diagnoses the existence of an abnormality in a tool for processing of a processing target by each of multiple working machines. The diagnostic apparatus (10) includes an acquirer (110) and a diagnoser (160). The acquirer (110) acquires program identification information identifying a program executed in each of the working machines, tool information indicating a type of the tool applied to processing, and transition information indicating transition of load in the working machine from the start to the end of the processing executed by execution of the program, at execution of the processing in the working machine. The diagnoser (160) diagnoses whether an index value of the load obtained from the transition information is out of a predetermined range corresponding to a combination of the program identification information, the tool information, and machine information identifying the working machine that transmits the transition information.

Apparatuses, systems, and methods are disclosed for a multidimensional print, cut, and craft device. A device includes a housing, a gantry coupled to the housing, a tool housing movably coupled to the gantry, a tool holder coupled to the tool housing, and a material platform. The tool housing is movable in three dimensions in response to the gantry being actuated to craft a material placed on the material platform using a tool placed in the tool holder.

The present invention discloses a method and device for evaluating a performance state of a numerical control cutting tool bit for flexible materials. The method includes: detecting three-phase current data of a brushless direct current motor during operation in real time by a preset current probe when the brushless direct current motor rotates to drive the numerical control cutting tool bit to vibrate; extracting characteristic values from the three-phase current data; clustering the characteristic values, and generating m real-time clusters, wherein m is an integer greater than 0; and acquiring j reference clusters, and determining a performance state level of the numerical control cutting tool bit based on the j reference clusters and the m real-time clusters, wherein j is an integer greater than 0.

The invention relates to a method for producing or machining a toothing (2) on a workpiece (3), in which method the workpiece, which is rotationally driven about its axis of rotation (C), is brought into rolling machining engagement with tool toothing (5) rotating about an axis of rotation (C2) which is, in particular, at a non-null crossed-axes angle to the axis of rotation of the workpiece, wherein the machining operation is automatically monitored, using sensors to record same automatically, already at the machine operation stage for a recurring irregularity originating from tool wear (52), in particular higher wear of at least one tool tooth (51) compared to other tool teeth.

A determination apparatus includes circuitry to receive operation information corresponding to an action being performed by a machine to be diagnosed and a detection signal of a physical quantity that changes according to the action of the machine; take out, from the detection signal, an operation detection signal indicating that the machine is operating, based on the operation information; extract feature information of the operation detection signal; select, from the feature information, particular feature information to be compared with a plurality of reference feature information; and determine a machining section of the machine in the feature information, based on the plurality of reference feature information and the particular feature information.

Tool wear monitoring is critical for quality and precision of manufacturing of parts in the machining industry. Existing tool wear monitoring and prediction methods are sensor based, costly and pose challenge in ease of implementation. Embodiments herein provide method and system for monitoring tool wear to estimate Remaining Useful Life (RUL) of a tool in machining is disclosed. The method provides a tool wear model, which combines tool wear physics with data fitting, capture practical considerations of a machining system, which makes the tool wear prediction and estimated RUL more stable, reliable and robust. Further, provides cost effective and practical solution. The disclosed physics based tool wear model for RUL estimation captures privilege of physics of tool wear and easily accessible data from CNC machine to monitor and predict tool wear and RUL of the tool in real-time.