A system and method for mapping the cutting of a plurality of features in a three dimensional workpiece with a plurality of cutting tools controlled by a plurality of cutting tool machines according to process information is disclosed. The method comprises receiving the process information describing cutting parameters the plurality of features from the plurality of cutting tool machines, parsing the process information to extract the cutting parameters, transforming each feature location in the workpiece from a three dimensional space to a two dimensional space, initiating a call to retrieve cutting tool tracking information from each cutting machine for each of the plurality of cutting tools while cutting the plurality of features in the three dimensional workpiece and providing cutting status associated with each feature at the coordinate transformed location of each feature for presentation in the two dimensional space.

Provided herein are a numerical control mechanism, a tool replacement equipment and a tool replacement method, which are used for disassembling a first tool and preparing a second tool according to a tool replacement task and replacing the first tool with a second tool, so as to realize automatic tool replacement, reduce manpower and machine waiting time for the tool replacement, and improve the tool replacement efficiency.

A machining dimension prediction apparatus includes a trend acquirer that acquires, for each workpiece, trend information indicating a trend of a state of a machining tool during a machining period of machining performed by the machining tool, a feature calculator that calculates, based on the trend information, a feature using the trend of the state in each of sections included in the machining period, a measurement value acquirer that acquires a measurement value of a dimension of each workpiece after being machined, a section specifier that specifies, as a specific section of the sections, a section including a calculated feature having a greatest degree of relevance to the measurement value, and a predictor that predicts, when a new-target workpiece is machined, a dimension of the new-target workpiece after being machined based on the feature calculated using the trend of the state in the specific section.

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

A universal manufacturing vise jaw plate for use on a machine tool with a vise fixture, and configured for real-time operational data collection and analysis. The vise plate comprises a combination of acoustic and vibration sensors with a plate for cutting tool monitoring applications.

A processing system having a function for appropriately maintaining processing accuracy, without depending on a degree of abrasion of a tool. An unused tool used in a machine tool is captured by using a camera mounted on a robot, so as to obtain a reference image of the tool. Next, an image of the tool is captured after a predetermined number of processing operations carried out. An average amount of tool abrasion per one processing operation is calculated based on the two images, so as to estimate a current amount of tool abrasion. Based on the estimation result and a previously input limit amount of abrasion, the number of remaining possible processing operations of the tool is calculated and output.

An air tool monitoring apparatus includes a housing having a hollow chamber formed therein, and also having an inlet and an outlet formed therein, each of the inlet and an outlet in communication with the chamber. The apparatus also includes first and second sensors for sensing condition indicative of tool usage and wear, a battery disposed in the housing, a generator for recharging the battery, and a microprocessor operatively connected to the housing and including a timer, a memory storage module, and a unique identifier. The apparatus may include a baffle for guiding air past the generator. The apparatus further includes a switch for starting and stopping the timer, and a communication device for sending data from the microprocessor to a data collection device. Methods of using the apparatus, along with systems for monitoring and reporting on usage of multiple air tools equipped with the apparatus, are also described.

In order to monitor replacement time for a consumable part of a machine tool, the present invention defines a time period after the power of the machine tool is turned off until the next turn-on thereof as a pause time for the consumable part, and provides a notification that the replacement time for the consumable part has come when the pause time is not less than a predetermined threshold.

A tool wear monitoring and predicting method is provided, and uses a hybrid dynamic neural network (HDNN) to build a tool wear prediction model. The tool wear prediction model adopts actual machining (cutting) conductions, sensing data detected at the current tool run of operation and the predicted tool wear value at the previous tool run of operation to predict a predicted tool wear value at the current tool run. A cyber physical agent (CPA) is adopted for simultaneously monitoring and predicting tool wear values of plural machines of the same machine type.

A system and a method for machine tool maintenance and repair is provided for allowing an expert at a remote site to collaborate with an on-site personnel to maintain or repair a physical machine in a manner of combining the physical machine with a virtual reality (VR) model or an augmented reality (AR) model. Two maintenance modes are provided, which are an augmented virtual reality model guided by a standard operation procedure (referred as a SOP-AVR mode), and an augmented virtual reality model guided by an expert operation procedure (referred as an EG-AVR mode). A cyber physical agent (CPA) is adopted for simultaneously monitoring and repairing plural machines of the same machine type.