Abstract: Embodiments of the present invention provide a hybrid system and method for distributed virtual power plants integrated intelligent net zero. In this method, a cyber physical agent (CPA) is utilized to collect a carbon emission information and an energy management information, and then an artificial intelligence (AI) optimization model of an intelligent central dispatch platform is utilized to obtain a power dispatch manner of the distributed virtual power plants based on the carbon emission information and the energy management information, such that the power dispatch manner of the distributed virtual power plants meets the requirements of enterprise economic benefits and net zero carbon emissions at the same time.

Abstract: A hybrid method for green intelligent manufacturing (GiM) combines the carbon reduction and the energy saving into the intelligent manufacturing based Industry 4.1 cloud platform. GiM assists companies to achieve the goal of net zero transition and help them advance to Industry 4.2 as soon as possible by simultaneously taking carbon footprint and energy issues into account. GiM collects large volumes of essential data (including carbon footprint) via cyber physical agents (CPAs), and sends them to two critical services of carbon management and intelligent energy management system (iEMS) deployed on the cloud platform. The two critical services optimize the energy dispatch schedule by strictly following the requirements of energy saving, carbon reduction, and net zero. Then, the state of zero defects of intelligent manufacturing achieved in Industry 4.1 can be upgraded to net zero of GiM in Industry 4.2.

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 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 processing apparatus with vision-based measurement includes a central control unit and a workpiece transporting unit, a vision-based measurement unit, a processing quality prediction unit, and a processing unit that are respectively connected to the central control unit electrically. The workpiece transporting unit is controlled by the central control unit to transport the workpiece to the vision-based measurement unit to be measured. The data obtained by the vision-based measurement unit from measuring the workpiece is provided to the processing quality prediction unit for conducting quality prediction. The processing quality prediction unit implements a virtual processing quality prediction method to establish a quality prediction model, wherein the workpiece transporting unit is utilized to assist the processes of establishing or modifying the model.

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.

Abstract: A processing apparatus includes a processing quality prediction unit capable of outputting workpiece surface processing signal, a storage unit capable of storing workpiece surface processing signal, and a workpiece surface information management unit capable of interpreting workpiece surface processing signal. The workpiece surface information management unit can convert the surface processing signal into data of workpiece surface quality. The workpiece surface information management unit can be utilized to respectively interpret the surface processing signals provided by the processing quality prediction unit and to convert them into data of the roughness degree of the workpiece surface texture, so as to clearly provide information regarding the quality of the workpiece surface and completely utilize the signals detected during the processing for increasing the value added of the processing apparatus and enhancing the efficiency of use of the processing apparatus.

Abstract: A processing apparatus includes a processing unit for processing the workpiece, a processing quality prediction unit connected with the processing unit electrically, a signal interpretation unit connected with the processing quality prediction unit electrically, and a control unit connected with both the processing unit and the signal interpretation unit electrically, wherein when the processing unit is processing the workpiece, the processing quality prediction unit detects the processing signal and sends the signal to the signal interpretation unit, wherein when the signal interpretation unit interprets the processing signal, if there is abnormality in the processing signal, the signal interpretation unit will immediately notify the control unit to stop the processing unit from operating.

Abstract: A virtual metrology based method for predicting machining quality of a machine tool is provided. In this method, each product accuracy item is correlated with operation paths of the machine tool. During a modeling stage, the machine tool is operated to process workpiece samples, and sample sensing data of the workpiece samples associated with the operation paths are collected during the operation of the machine tool. The sample sensing data of each workpiece sample is de-noised and converted into the sample feature data corresponding to each feature type. The workpiece samples are measured with respect to the product accuracy item and integrated into the feature data for building a predictive model, thereby obtaining quality predicted data for each product accuracy item. During a usage stage, accuracy item values of a workpiece are predicted using the feature data during processing the workpiece in accordance with the predictive models.

Abstract: A virtual metrology based method for predicting machining quality of a machine tool is provided. In this method, each product accuracy item is correlated with operation paths of the machine tool. During a modeling stage, the machine tool is operated to process workpiece samples, and sample sensing data of the workpiece samples associated with the operation paths are collected during the operation of the machine tool. The sample sensing data of each workpiece sample is de-noised and converted into the sample feature data corresponding to each feature type. The workpiece samples are measured with respect to the product accuracy item and integrated into the feature data for building a predictive model, thereby obtaining quality predicted data for each product accuracy item. During a usage stage, accuracy item values of a workpiece are predicted using the feature data during processing the workpiece in accordance with the predictive models.