A testbed device includes high performance actuators, a video microscopy system and a plurality of high resolution, throughput sensors adapted or configured for collecting data that may be used in predictive modelling of machine processes.

Provided is a cutting speed planning system including a graphic preprocessing engine, a first speed planning engine, an included angle calculation engine, a second speed planning engine and a speed determination engine. The graphic preprocessing engine substitutes a simplified cutting route for a plurality of short straight paths of a graphic path. The first speed planning engine calculates a reasonable maximum cutting speed of each cutting route. The included angle calculation engine calculates the included angle between two adjacent ones of the cutting routes. The second speed planning engine adjusts the terminal cutting speed and the initial cutting speed of the cutting routes. The speed determination engine performs speed planning on the cutting routes according to digital control system period time. A cutting speed planning method and a non-transitory storage medium are further provided.

A diagnostic apparatus includes circuitry that acquires a detection result of a time-varying physical quantity generated by a machine that performs a plurality of processes; generates a determination result of the processing based on the detection result; and outputs, to the machine, batch determination information in units of a plurality of same type processes performed by the machine. The batch determination information indicates whether at least one of the plurality of same type processes is determined as abnormal. Based on the batch determination information, the machine performs an action.

Methods, systems, and apparatuses for adapting a predictive model for a manufacturing process. One method includes receiving, with an electronic processor, a plurality of data points for a plurality of manufactured parts and the predictive model. The predictive model outputs a label for a manufactured part provided by the manufacturing process indicating whether the manufactured part is accepted or rejected. The method also includes estimating, with the electronic processor, a drift for each of the plurality of data points and generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points. In addition, the method includes outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model.

A modeling method of a cutting force model is provided, including: using a cutting tool to cut a unidirectional fiber reinforced polymer along a circular path; using a measurement member to measure the cutting force on the cutting tool corresponding to the angle between the feeding direction of the cutting tool and the fiber direction of the unidirectional fiber reinforced polymer; and obtaining the functions of the cutting force coefficients in a formula according to the measurement result of the measurement member. With the modeling method, a mechanistic force model can be rapidly established to predict approximate cutting forces of the cutting tool in use.

This disclosure relates generally to recommending tool configurations in machining. The machining tool configuration selection involves the selection of several tool specification parameters concerning the material, geometry and composition of the machining tool. The state-of-the-art methods uses a rule and knowledge-based system to select tool configuration, however these methods do not recommend tool configurations which satisfy customer requirement. Embodiments of the present disclosure uses a hierarchical model which is trained to predict acceptable tool specification parameters for a given requirement by learning the patterns from past tool selection data. Further a probabilistic approach is used to predict the top set of recommendations of tool configurations with a probability score for each prediction. The disclosed method is used for recommending tool configurations in a cylindrical grinding wheel process.

A testbed device includes high performance actuators, a video microscopy system and a plurality of high resolution, throughput sensors adapted or configured for collecting data that may be used in predictive modelling of machine processes.

Provided is a tool life estimating device that enables estimation of a life of a tool used in a machine tool according to changes in machining conditions. The tool life estimating device includes a state observation unit that acquires machining information indicative of a status of the machining in a state where the life of the tool remains sufficiently, wherein the machining information is acquired from log data recorded while the machine tool is operated, and creates input data based on the machining information that has been acquired; a learning unit that constructs a learning model in which clusters of the machining information are created by unsupervised learning using the input data that has been created by the state observation unit; and a learning model storage unit that stores the learning model.

Prediction of a machining time at higher accuracy considering a machine delay generated in a machine is allowed by a numerical controller of the invention. The numerical controller includes a reference machining time prediction unit for predicting a reference machining time corresponding to a machining time not considering a delay time of servo control and machine motion based on the machining program, a program analysis unit for extracting a combination of at least one program command included in the machining program, a data storage unit for storing information related to an actual delay time of servo control and machine motion measured for each combination of program command types, a correction time calculation unit for calculating a correction time for correction of the reference machining time based on the combination of the program commands extracted by the program analysis unit and the information stored in the data storage unit, and a machining time prediction unit for calculating a predicted machining time obtained by correcting the reference machining time using the correction time.

Provided is a tool life estimating device that enables estimation of a life of a tool used in a machine tool according to changes in machining conditions. The tool life estimating device includes a state observation unit that acquires machining information indicative of a status of the machining in a state where the life of the tool remains sufficiently, wherein the machining information is acquired from log data recorded while the machine tool is operated, and creates input data based on the machining information that has been acquired; a learning unit that constructs a learning model in which clusters of the machining information are created by unsupervised learning using the input data that has been created by the state observation unit; and a learning model storage unit that stores the learning model.