Traditional virtual power plants (VPPs) combine power from distributed energy resources (DER) to supply energy to users. However, they fall short of net-zero goals because of neglecting carbon footprints during power aggregation. This paper proposes a novel intelligent virtual power plant framework (iVPPF) to address this gap. iVPPF comprises a central iVPP (iVPP$_{\mathrm {C}}$) and several regional iVPPs (iVPP$_{\mathrm {n}}$), n = E, S, M, and N. These iVPPn are geographically distributed systems for intelligently managing iVPPs in four regions: east, south, middle, and north, respectively, while the iVPPC is responsible for dispatching power across iVPPn. We built iVPPC and iVPPn on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for iVPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for iVPPn. iVPPn can intelligently control DERs based on the collected data. Also, iVPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate iVPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that iVPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed iVPPF promises to contribute economic benefits for businesses and the pursuit of net-zero emissions. Note to Practitioners—This paper proposes an intelligent virtual power plant framework$({i} \text { VPPF})$consisting of a central coordinator$({i}\text {VPP}_{\text {C}})$and distributed regional managers (${i}\text {VPP}_{\text {n}}$for East, South, Middle, and North). Leveraging green power clouds, both${i} \text { VPP}_{\text {C}}$and${i}\text {VPP}_{\text {n}}$harness AI for intelligent management and power dispatch across regions. We detail the system architecture and showcase practical applications, including scenarios like dispatching and aggregating for demand response, using the IEEE 13-node test feeder. Additionally, we explore the design of green power clouds and cyber-physical agents (CPAs).

Industry 4.0 is accelerating, with manufacturing enterprises embracing digital transformation and intelligent manufacturing (iM) to enhance competitiveness. Manufacturing enterprises must also improve their environmental performance to meet the goal of net zero by 2050 and to avoid border carbon tax imposed by large economies. Thus, green intelligent manufacturing (GiM), i.e., conducting iM while pursuing to maximize energy conservation and carbon reduction, has become the key development trend and a fundamental challenge for the modern manufacturing industry. To address the challenges of green intelligent manufacturing (GiM), this paper proposes a novel framework called Industry 4.2 for GiM (I4.2-GiM). This framework builds on the Intelligent Factory Automation ($i$FA) platform, which the authors developed to achieve zero-defect manufacturing (i.e., Industry 4.1). I4.2-GiM uses various IoT devices, called Cyber-Physical Agents (CPAs), to collect and integrate large amounts of data. It also includes two interrelated systems, an intelligent carbon emission management system ($i$CMS) and an intelligent energy management system ($i$EMS), simultaneously tackling carbon reduction and energy saving. Existing factory EMSs typically save less than 10% of energy, but I4.2-GiM has been shown to conserve 10.9% of energy and reduce carbon emissions by 12.55% while conducting iM in daily production. I4.2-GiM is a promising new framework that can help manufacturing enterprises approach net zero intelligently.Note to Practitioners—This paper proposes a new green intelligent manufacturing (GiM) framework called I4.2-GiM (Industry 4.2 for GiM). I4.2-GiM builds on the Intelligent Factory Automation ($i$FA) platform, which the authors developed to achieve zero-defect manufacturing (i.e., Industry 4.1). I4.2-GiM also uses various IoT devices called Cyber-Physical Agents (CPAs) to collect data from multiple sources. It also includes two interrelated systems, an intelligent carbon emission management system (iCMS) and an intelligent energy management system (iEMS), to address carbon reduction and energy saving simultaneously. Moreover, this paper provides a systematic implementation procedure and several practical examples to help practitioners adopt the designs and niches of I4.2-GiM and build their desired GiM systems for reaching the goal of net zero.

Most traditional industries or emerging countries may not be capable of directly transiting to Industry 4.0. To fill the gap between as-is Industry 3.0 and to-be Industry 4.0, some disruptive innovations from automation and industrial engineering identify best practice with adopting cost-effective semi-automated systems to manage the potential socio-economic impacts of infrastructure disruptions, while considering total resource management for sustainability. This is the so-called “hybrid strategy (HS),” or “Industry 3.5.” On the other hand, the current Industry 4.0-related technologies should also emphasize quality enhancement to achieve “zero-defect manufacturing (ZDM),” also referred to as “Industry 4.1.” ZDM is a systematic strategy to realize the goal of Zero Defects, which includes two phases. Phase I: accomplish Zero Defects of all thedeliverablesby applying efficient and economical total-quality-inspection techniques; and Phase II: further ensure Zero Defects of all theproductsgradually by improving the yield with big data analytics and continuous improvement. Both the challenges and opportunities from HS to ZDM have significantly expanded the scope of traditional automation science and engineering.

Many core technologies of Industry 4.0 have gained substantial advancement in recent years. Digital Twin (DT) has become the key technology and tool for manufacturing industries to realize intelligent cyber-physical integration and digital transformation by leveraging these technologies. Although there have been many DT-related works, there is no standard definition, unified framework, and implementation approach of DT until now. Widely developing DTs for the manufacturing industry is still challenging. Thus, this paper proposes a novel implementation framework of digital twins for intelligent manufacturing, denoted as IF-DTiM, which possesses several distinct merits to distinguish itself from previous works. First, IF-DTiM fully utilizes new-generation container technology so that DT-related applications and services can be packaged in a self-contained way, rapidly deployed, and robustly operated with the capabilities of failover, autoscaling, and load balancing. Second, it leverages existing intelligent cloud manufacturing services to realize the intelligence for DT externally in a scalable and plug-and-play manner instead of using traditional approaches to embed intelligence in DT. Third, IF-DTiM contains Product DT for products, Equipment DT (i.e., EQ DT) for equipment, and Process DT for production lines, which can generically fulfill the demands and scenarios to achieve intelligent manufacturing for various manufacturing industries. Testing results show that IF-DTiM can achieve remarkable performance in rapid deployment and real-time data exchanges of DT-related applications. Finally, we develop an example DTiM system for CNC machining based on IF-DTiM to demonstrate its efficacy and applicability in facilitating the manufacturing industry to build their DT systems.Note to Practitioners—Developing Digital Twin (DT) systems to realize intelligent manufacturing is challenging. The proposed IF-DTiM (Implementation Framework of Digital Twins for Intelligent Manufacturing) provides a novel container-technology and cloud-manufacturing-service-based systematic methodology for building DTiM. In this paper, we present the system architecture and several operational scenarios (e.g., how to create and use DTs) of IF-DTiM, together with the design of its core functional mechanisms (e.g., rapid deployment scheme for DT, real-time data exchange for DT, DT interface pattern, and general workflow architecture for DT). Also, an example DTiM system for CNC machining based on IF-DTiM is presented to facilitate the practitioners to adopt the designs and niches in IF-DTiM to build their desired DTiM systems.

EDM (Electrical Discharge Machining) is a process to remove metal from conductive materials using electrical sparks. To monitor the EDM process using virtual metrology (VM), we need to obtain the electrode’s voltage and current signals of a machine tool. Due to the nature of EDM, the sensors installed on the machine tool acquire the signals at a high sampling rate and generate a vast amount of data in a short time, thereby raising the big-data processing issue. Our previous work proposed an efficient approach called BEDPS to process the EDM big data in a Hadoop distributed cluster. This paper presents a novel big data processing approach to feature extraction for EDM by using container technology (i.e., Docker and Kubernetes). We re-implement some Spark algorithms of BEDPS in Python (originally in Scala) and then run the refined BEDPS in containers in a Kubernetes cluster. Testing results show that the refined BEDPS developed in this study can reduce the execution time by almost half, compared to the original Scala version (9.6577 minutes vs. 19.2735 minutes). The adoption of Python in Spark is also shown to have similar performance with Scala, although there are some cases where Python performance falls short, for example, parallel processing using Python parallel processing library. The results also show that the Kubernetes cluster is promising to be an alternative way, other than the Hadoop, for processing big data. At the same time, it can bring some advantages to the big data processing applications, such as easy deployment, robustly running, load balance, self-healing, failover, and horizontal auto-scaling for containerized applications.

In this paper, we propose a novel Hough circle-assisting deep-network inspection scheme (HCADIS), aiming at identifying defects in dense coffee beans. The proposed HCADIS plays a critical role in a camera-based defect removal system to collect defective bean positions for picking all defects off. The idea of the HCADIS is to mix intermediate data from a deep network and a feature engineering method call Hough circle transform for utilizing advantages of both methods in inspecting beans. The Hough circle transform is adopted because it performs quite stable and bean shapes are highly close to circles in nature. A set of core mechanisms are designed for collaboration between the deep network and the Hough circle transform for precisely and accurately inspecting defective beans. Finally, we implement a prototype of the HCADIS and conduct experiments for testing the proposed scheme. The test results reveal that the HCADIS indeed successfully inspect defects among dense beans with superior performance in various metrics. This work provides industrial participants useful experiences for creating deep-learning solutions to bean products in coffee industries.

Because virtual metrology (VM) can achieve real-time and on-line total inspection, it is a promising way for measuring machining precision of machine tools. However, the machining processes possess the characteristics of severe vibrations. Thus, how to effectively handle signals with low signal/noise ratios and extract key features from them is a challenging issue for successfully applying VM to the machine tools. In this paper, a novel VM scheme for predicting machining precision of machine tools is proposed based on several previously developed methods for data quality evaluation, model reliance evaluation, and machining precision prediction. Besides, for data preprocess, we propose a Wavelet-based de-noising method to improve the S/N ratio of sensor data. In addition, we base on the stepwise technique to develop an automatic feature selection method that can extract key features related to machining operations in time, frequency, and time-frequency domains, and can reduce the dimension of essential features. Testing results of a 3-axis CNC machine center machining standard workpieces show that the VMS can achieve the performance that the maximum average error of machining-precision conjecture is less than 2 um and the conjecture of 20 machining-precision items can be completed within 3.8 sec.

Virtual Metrology (VM) is a method to conjecture manufacturing quality of a process tool based on data sensed from the process tool and without physical metrology operations. Historical data is used to produce the initial VM models, and then these models are applied to operate in a process drift/shift environment. The accuracy of VM highly depends on the modeling samples adopted during initial-creating and on-line-refreshing periods. Since design-of-experiments (DOE) may not be performed due to large resources required, how could we guarantee stability of the models and predictions when they move into these unknown environments? Conventionally, static-moving-window (SMW) schemes with a fixed window size are adopted during the on-line-refreshing period. The purpose of this paper is to propose a dynamic-moving-window (DMW) scheme for VM model refreshing. The DMW scheme adds a new sample into the model and applies a clustering technology to do similarity clustering. Next, the number of elements in each cluster is checked. If the largest number of elements is greater than the predefined threshold, then the oldest sample in the cluster with the largest population is deleted. Test results show that the DMW scheme has better on-line conjecture accuracy than that of the SMW scheme.

This paper develops a ZigBee indoor positioning scheme based on the location fingerprinting approach. The proposed scheme includes four workflows: (1) creating the location fingerprint table, (2) training the locating model using neural network (NN), (3) preprocessing data through the Signal-Index-Pair method, and (4) estimating the coordinate of the mobile target instantly. Testing results show that within the error distance of 5 meters, the NN locating model with the Signal-Index-Pair data preprocess method can increase the positioning precision by 17% compared with the original NN, in terms of the cumulative error probability (CEP). It also achieves 5% CEP higher than the k (k=5) nearest neighbor method and the weighted k (k=5) nearest neighbor method. Potential applications include patient tracking in hospitals, object tracking for factory monitoring, self-navigation of autonomous robots, and visitors monitoring in military buildings, and so on.

In current semiconductor and TFT-LCD factories, periodic sampling is commonly adopted to monitor the stability of manufacturing processes and the quality of products (or workpieces). As for those non-sampled workpieces, their quality is usually monitored by such as a fault-detection-and-classification (FDC) server. However, this method may fail to detect defected products. For example, a workpiece with all the individual manufacturing process parameters being in-spec may still result in out-of-spec product quality. Under this circumstance, unless this certain defected workpiece is selected for sampling by chance, it cannot be detected by simply monitoring the manufacturing process parameters collected from the production equipment. To solve the above mentioned problem, this research proposes a product quality fault detection scheme (FDS), which utilizes the classification and regression tree to implement a model for identifying the relationship between process parameters and out-of-spec products. Through this model, each set of normal manufacturing process parameters can be real-time and on-line examined to detect failure or defected products.

This work proposes an advanced key-variable selecting method, the neural-network-based stepwise selection (NN-based SS) method, which can enhance the conjecture accuracy of the NN-based virtual metrology (VM) algorithms. Multi-regression-based (MR-based) SS method is widely applied in dealing with key-variable selecting problems despite that it may not guarantee finding the best model based on its selected variables. However, the variables selected by MR-based SS may be adopted as the initial set of variables for the proposed NN-based SS to reduce the SS process time. The backward elimination and forward selection procedures of the proposed NN-based SS are both performed by the designated NN algorithm used for VM conjecturing. Therefore, the key variables selected by NN-based SS will be more suitable for the said NN-based VM algorithm as far as conjecture accuracy is concerned. The etching process of semiconductor manufacturing is used as the illustrative example to test and verify the VM conjecture accuracy. One-hidden-layered back-propagation neural networks (BPNN-I) are adopted for establishing the NN models used in the NN-based SS method and the VMconjecture models. Test results show that the NN model created by the selected variables of NN-based SS can achieve better conjecture accuracy than that of MR-based SS. Simple recurrent neural networks (SRNN) are also tested and proved to be able to achieve similar results as those of BPNN-I.

For maintaining high stability and production yield of production equipment in a semiconductor fab, on-line quality monitoring of wafers is required. In current practice, physical metrology is performed only on monitoring wafers that are periodically added in production equipment for processing with production wafers. Hence, equipment performance drift happening in-between the scheduled monitoring cannot be detected promptly. This may cause defects of production wafers and the production cost. In this paper, a novel virtual metrology scheme (VMS) that is based on a radial basis function neural network (RBFN) is proposed for overcoming this problem. The VMS is capable of predicting quality of production wafers using real-time sensor data from production equipment. Consequently, equipment performance abnormality or drift can be detected timely. Finally, the effectiveness of the proposed VMS is validated by tests on chemical vapor deposition (CVD) processes in practical semiconductor manufacturing. It is therefore proved that RBFN can be effectively used to construct prediction models for CVD processes

A generic embedded device (GED) that can be installed to various kinds of information equipment, such as manufacturing equipment, portal servers, automatic guided vehicles, etc., is successfully developed In this work. GED is equipped with an embedded real-time operating system and several software modules to retrieve, collect, and manage equipment data. In particular, the communication management module of GED can transmit and receive data to/from remote clients via both wired and wireless networks. Moreover, GED has an object-oriented application interface that flexibly enables GED to add-in or update any customized application. Three typical communication mechanisms, namely the standard processes of exception notification, periodic inspection, and data inquiry are built in GED. As such, GED is able to handle a variety of customized applications, such as monitoring, detection, diagnostics, and prognostics of various kinds of information equipment. We believe that GED possesses the potentiality of information acquisition and transmission so that it can assist all kinds of information equipment to reach the goal of equipment-to-system (E2S) communication and facilitate the maintenance tasks.

In this paper, a remote monitoring and control architecture is developed. The architecture consists of a Web-services-based monitoring and control gateway (WSMCG), distributed Ethernet-ready I/O modules, safety detection modules, Web cameras, and networks. It can not only provide general functions of remote monitoring and control but also possess the mechanism of actively detecting appliance's abnormal power consumption and ensuring appliance safety. This research incorporates the newest network-related technologies with the concept of ensuring appliance safety for developing remote monitoring and control systems. The research results are with novelty and high practicability. It is believed that the developed technologies and concepts can be applied in constructing new-generation remote monitoring and control systems.

In recent years, the emerging Web-services technology has provided a new and excellent solution to the data integration among heterogeneous systems. In this paper, a Web-services-based e-diagnostics framework (WSDF) is proposed. It can achieve the automation of diagnostic processes and diagnostics-information integration for semiconductor equipment. First, the system framework and the system component model are designed. Then, the object-oriented analysis and design of system components are accomplished. In particular, for the purpose of code reuse, several common functions, such as SOAP communication, UDDI registration, security mechanism, data exchange mechanism, and local database access, are built into a generic component, called Web-service agent. By inheriting the Web-service agent, other system components can be constructed and have these common functions. In addition, a generic equipment object model, a unified authentication-service mechanism, and a safe network connection are also designed in the framework. WSDF is intended to support the e-diagnostics functions defined by International SEMATECH. It is believed that WSDF can be applied to construct e-diagnostics systems for semiconductor manufacturing industry.

In this paper we propose a novel equipment integration framework that is constructed on top of Ethernet and possesses the capability of real-time, synchronous control networks. The equipment integration framework can be used in the equipment-control layer of factory automation networks. Also, it can easily integrate with information-management layer networks. Simulation and experimental results with a laboratory-scale factory show that the proposed equipment integration framework can not only achieve a good performance but also remedy some drawbacks of traditional automaton networks. It is believed that our work can be a significant contribution for factory automation.

A holonic manufacturing system (HMS), which is designed to realize an agile manufacturing enterprise, must be able to integrate the entire range of manufacturing activities from market demands, design, modeling, production, through delivery. These entire activities are implemented by several distributed sites. In order to effectively integrate these distributed sites, we adopt distributed object, mobile object, and object web technologies as well as the holon and holarchy concepts derived from studying social organizations and living organisms to develop a holonic information coordination system (HICS). The generic holon is first developed to achieve the properties of holon, error recovery and security certification. Communication holons are then generated by inheriting generic holon. Finally, communication holons are used to establish HICS. Thus, communication holons have the basic holonic attributes, such as intelligence, autonomy, and cooperation. Further, communication holons can handle information sharing, coordination among enterprises and data exchange by different data format. It is believed that HICS can meet the future requirements of supply chain information integration for virtual enterprises.

In this paper, a novel quantitative measure of redundancy, called refined redundancy index (RRI), which is measured in the joint-rate level, is proposed. Based on the concept that a larger solution space of the inverse kinematics problem represents larger redundancy, the RRI is defined as the normalized magnitude of the solution spaces. The value of the RRI varies from 0 to 1, and larger RRI corresponds to larger redundancy. Unlike joint-angle-level approaches, our method does not have the problem that different regions of joint angles correspond to the same primary task. The proposed RRI is computationally efficient and easy to apply for real-time applications. Simulation results show that with RRI the manipulator can reduce the execution time of a given task significantly, compared to the conventional approach. The results also illustrate that motion failures can be avoided with RRI. It is believed that RRI can be applied to a variety of applications of redundant robots in the future.

In this paper, a graphical dynamic simulator is developed that can simulate actively-coordinated wheeled vehicle systems on uneven faceted terrain. Based on the considerations of model fidelity and computational efficiency, a simple geometric modes for wheel-terrain contact is proposed. In addition, a computationally-efficient algorithm for contact detection is developed. We also devise a contact force model based on soil mechanics. Simulation results of a case, where the wheeled actively articulated vehicle traverses a concave edge between facets, are used to demonstrate the good performance of our contact model.

In this paper, an efficient and systematic formulation of the force distribution equations for general tree-structured robotic mechanisms is presented. The applicable platforms include not only systems with star topologies, such as walking machines that have multiple legs with a single body but also general tree-structured mechanisms, such as variably configured wheeled vehicles having multiple modules. The force balance equations that govern the relationship between the contact forces and the resultant inertial forces/moments of the vehicle will be derived through a recursive and computationally efficient algorithm. Also, the joint torque constraints that specify the joint actuator limits, and contact friction constraints that may be used to avoid slippage and maintain contact, are efficiently incorporated in the formulation. Based on this formulation, several standard optimization techniques, such as linear programming or quadratic programming, can be applied to obtain the solution. An algorithm summarizing the results developed, and suitable for computer implementation, is included. The algorithm has been applied to an n-module actively articulated wheeled vehicle, and the computational cost evaluated. The efficiency of the algorithm is demonstrated with results showing real-time execution on a Pentium PC.

An efficient formulation of the force distribution equations for actively-coordinated vehicles is presented. The applicable platforms include not only systems with star topologies, such as walking machines that have multiple legs with a single body, but also general tree-structured mechanisms, such as variably-configured wheeled vehicles having multiple modules. Based on this formulation, several standard optimization techniques, such as linear programming or quadratic programming, can be applied to obtain the solution. The efficiency of the formulation is demonstrated with results showing real-time execution on a Pentium PC.