Abstract: An additive manufacturing (AM) method is provided. The method includes performing a laser powder bed fusion (L-PBF) process on the powder layer. Then, a first surface roughness value of the powder layer after the L-PBF process is obtained to generate a first surface profile. An absorptivity and a set of re-melting process parameters data are used to perform a heat transfer simulation. A second surface profile of the powder layer after laser re-melting is obtained by using the first surface profile and a low-pass filter. Then, the set of re-melting process parameters data is adjusted iteratively to perform the heat transfer simulation until a second surface roughness value predicted from the second surface profile is smaller than or equal to a surface roughness threshold, thereby obtaining optimal values of re-melting process parameters for performing a re-melting process to reduce a surface roughness of a powder layer after the L-PBF process.

Abstract: An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed during a fabrication of each of the workpiece products; performing photography on each of the melt pools on the powder bed during the fabrication of each of the workpiece products; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.

Abstract: An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed; performing photography on each of the melt pools on the powder bed; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; first converting each of the workspace images to a gray level co-occurrence matrix (GLCM); building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.

Abstract: An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed; performing photography on each of the melt pools on the powder bed; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; first converting each of the workspace images to a gray level co-occurrence matrix (GLCM); building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.

Abstract: An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed during a fabrication of each of the workpiece products; performing photography on each of the melt pools on the powder bed during the fabrication of each of the workpiece products; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.

Abstract: An additive manufacturing (AM) system, an AM method, and an AM feature extraction method are provided. The AM system includes an AM tool, a product metrology system, an in-situ metrology system, a virtual metrology (VM) system, a compensator, a track planner, a controller, a simulator and an augmented reality (AR) device. The simulator is used to find feasible parameter ranges, while the AR device is used to support operations and maintenance of the AM tool. The product metrology system, the in-situ metrology system and the VM system are integrated to estimate the variation of material on a powder bed of the AM tool. The compensator is used for compensating the process variation by adjusting process parameters. The product metrology system is used to measure the quality of products. The in-situ metrology system is used to collect features of melt pools on the powder bed.

Abstract: An additive manufacturing (AM) system, an AM method, and an AM feature extraction method are provided. The AM system includes an AM tool, a product metrology system, an in-situ metrology system, a virtual metrology (VM) system, a compensator, a track planner, a controller, a simulator and an augmented reality (AR) device. The simulator is used to find feasible parameter ranges, while the AR device is used to support operations and maintenance of the AM tool. The product metrology system, the in-situ metrology system and the VM system are integrated to estimate the variation of material on a powder bed of the AM tool. The compensator is used for compensating the process variation by adjusting process parameters. The product metrology system is used to measure the quality of products. The in-situ metrology system is used to collect features of melt pools on the powder bed.

Abstract: An additive manufacturing (AM) method is provided. The method includes performing a laser powder bed fusion (L-PBF) process on the powder layer. Then, a first surface roughness value of the powder layer after the L-PBF process is obtained to generate a first surface profile. An absorptivity and a set of re-melting process parameters data are used to perform a heat transfer simulation. A second surface profile of the powder layer after laser re-melting is obtained by using the first surface profile and a low-pass filter. Then, the set of re-melting process parameters data is adjusted iteratively to perform the heat transfer simulation until a second surface roughness value predicted from the second surface profile is smaller than or equal to a surface roughness threshold, thereby obtaining optimal values of re-melting process parameters for performing a re-melting process to reduce a surface roughness of a powder layer after the L-PBF process.

Abstract: An additive manufacturing (AM) system, an AM method, and an AM feature extraction method are provided. The AM system includes an AM tool, a product metrology system, an in-situ metrology system, a virtual metrology (VM) system, a compensator, a track planner, a controller, a simulator and an augmented reality (AR) device. The simulator is used to find feasible parameter ranges, while the AR device is used to support operations and maintenance of the AM tool. The product metrology system, the in-situ metrology system and the VM system are integrated to estimate the variation of material on a powder bed of the AM tool. The compensator is used for compensating the process variation by adjusting process parameters. The product metrology system is used to measure the quality of products. The in-situ metrology system is used to collect features of melt pools on the powder bed.

Abstract: 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.

Abstract: An additive manufacturing (AM) system, an AM method, and an AM feature extraction method are provided. The AM system includes an AM tool, a product metrology system, an in-situ metrology system, a virtual metrology (VM) system, a compensator, a track planner, a controller, a simulator and an augmented reality (AR) device. The simulator is used to find feasible parameter ranges, while the AR device is used to support operations and maintenance of the AM tool. The product metrology system, the in-situ metrology system and the VM system are integrated to estimate the variation of material on a powder bed of the AM tool. The compensator is used for compensating the process variation by adjusting process parameters. The product metrology system is used to measure the quality of products. The in-situ metrology system is used to collect features of melt pools on the powder bed.

Abstract: A method for predicting precision of an electrical discharge machine is provided. In the method, plural sets of process data are obtained while the electrical discharge machine processes workpiece samples. Process features are established based on the process data. Each of the workpiece samples with respect to each of at least one measurement item is measured by using a metrology tool, thereby obtaining measurement values of the workpiece samples with respect to each measurement item. A correlation analysis operation is performed to obtain correlation coefficients. At least one key feature is selected from the process features as representative according to the correlation coefficients. The measurement values of the workpiece samples with respect to each measurement item, and the sets of process data corresponding to the key features are used to build a predictive model for predicting the precision of the electrical discharge machine.

Abstract: A product quality prediction method for mass customization is provided. When a production system has a status change, data of sets of process parameters and actual measurement values of workpiece samples processed before the status change occurs, and data of sets of process parameters and actual measurement values of few workpiece samples processed after the status change occurs are used for build or retrain a prediction model, thereby predicting a metrology value of a next workpiece.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method is provided. First, channel data is obtained, and whether the channel data complies with a predetermined condition of an event is determined. If the channel data complies with the predetermined condition, the channel data is recorded into a database according to a sampling frequency and a recording duration. The channel data corresponding to the event is obtained from the database, and is displayed according to a user operation. A replay frequency and stepping time interval corresponding to the user operation are obtained. The recording duration is adjusted according to the replay frequency, and the sampling frequency is adjusted according to the stepping time interval.

Abstract: A product quality prediction method for mass customization is provided. When a production system has a status change, data of sets of process parameters and actual measurement values of workpiece samples processed before the status change occurs, and data of sets of process parameters and actual measurement values of few workpiece samples processed after the status change occurs are used for build or retrain a prediction model, thereby predicting a metrology value of a next workpiece.

Abstract: A method for predicting precision of an electrical discharge machine is provided. In the method, plural sets of process data are obtained while the electrical discharge machine processes workpiece samples. Process features are established based on the process data. Each of the workpiece samples with respect to each of at least one measurement item is measured by using a metrology tool, thereby obtaining measurement values of the workpiece samples with respect to each measurement item. A correlation analysis operation is performed to obtain correlation coefficients. At least one key feature is selected from the process features as representative according to the correlation coefficients. The measurement values of the workpiece samples with respect to each measurement item, and the sets of process data corresponding to the key features are used to build a predictive model for predicting the precision of the electrical discharge machine.

Abstract: A processing apparatus includes a central control unit, and a processing quality prediction unit, a processing unit, and a tool compensation unit which are respectively connected with the central control unit electrically. The processing quality prediction unit implements a virtual processing quality prediction method to predict the processing quality of the workpiece, output an accurate data of quality to the central control unit, and generate tool path for the processing unit to process the workpiece. The central control unit judges the data from the processing quality prediction unit and outputs the data to the tool compensation unit to calculate tool compensation data. The tool compensation unit provides the tool compensation data to the processing quality prediction unit to form a new processing path. Then the processing unit implements the compensated processing path to process the workpiece.

Abstract: A digital marking processing apparatus includes a central control unit, and a processing quality prediction unit, a processing unit and a marking unit which are respectively connected with the central control unit electrically. The processing quality prediction unit can implement a virtual processing quality prediction method to predict the processing quality of the workpiece, output an accurate data of quality to the central control unit and generate tool path for the processing unit to process the workpiece. The central control unit is able to compile the data of quality from the processing quality in prediction unit into file information, so that the marking unit can then utilize the file information to correspondingly mark barcode or other digital pattern on the workpiece, which facilitate workpiece management and information disclosure.