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.

In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to $80\%$ .

Carbon fiber is currently one of the most popular composite materials in the world, with its wide applications ranging from bikes to space shuttles. However, there is no comprehensive method for the total quality inspection of carbon-fiber products so far due to its feature of continuous production and destructive examination of quality inspection. Through years of development, the Automatic Virtual Metrology (AVM) technology has been applied to various industries to turn the offline sampling inspection with metrology delay into online and real-time total inspection. With AVM, every workpiece of the carbon-fiber products can be examined to ensure product quality. The biggest issue of continuous production is that it can neither accurately define each unit nor label on the products. To solve this problem, a production data traceback (PDT) mechanism is proposed in this paper. The PDT mechanism can provide a virtual label to each unit for referring back to the process parameters of the production. With PDT, the process data of each workpiece of a complete spin can be acquired so as to accomplish the work-in-process (WIP) tracking requirement of applying AVM. Also, the Advanced-Manufacturing-Cloud-of-Things (AMCoT) platform is adopted in this work to fulfill all the smart manufacturing requirements for achieving the goal of zero-defects of carbon-fiber manufacturing.

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 ( $i$ VPPF) to address this gap. $i$ VPPF comprises a central $i$ VPP ( $i$ VPP $_{\mathrm{C}})$ and several regional $i$ VPPs ( $i$ VPP $_{\mathrm{n}})$ , n $=$ E, S, M, and N. These $i$ VPP $_{\mathrm{n}}$ are geographically distributed systems for intelligently managing $i$ VPPs in four regions: east, south, middle, and north, respectively, while the $i$ VPP $_{\mathrm{C}}$ is responsible for dispatching power across $i$ VPP $_{\mathrm{n}}$ . We built $i$ VPP $_{\mathrm{C}}$ and $i$ VPP $_{\mathrm{n}}$ on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for $i$ VPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for $i$ VPP $_{\mathrm{n}}$ . $i$ VPP $_{\mathrm{n}}$ can intelligently control DERs based on the collected data. Also, $i$ VPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate $i$ VPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that $i$ VPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed $i$ VPPF 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 $({\it i} \text{VPPF})$ consisting of a central coordinator $({\it i}\text{VPP}_{\text{C}})$ and distributed regional managers $({\it i}\text{VPP}_{\text{n}}$ for East, South, Middle, and North). Leveraging green power clouds, both ${\it i} \text{VPP}_{\text{C}}$ and ${\it i}\text{VPP}_{\text{n}}$ 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).

Computer numerical control (CNC) tool-wear prediction (TWPred) is an important issue in the industry. Recently, researchers have demonstrated that deep-learning models (DLMs) are effective in TWPred. However, DLMs are ill-suited to small- and medium-scale manufacturers due to high computational costs. Methods exist to reduce the computational costs of DLMs, but most of them depend on overly-complex pruning processes that are not appropriate for the low-end computers used by the above manufacturers. Therefore, we developed a lightweight DLM and an automated framework for TWPred. The framework is based on two concepts: 1) the DLM was pruned by reducing the number of input data fields so the model itself remains unchanged and 2) we designed a framework that enables the automatic establishment of a lightweight DLM. These two concepts make the overall framework applicable to small- and medium-scale manufacturers. Finally, we used real-world dataset PHM 2010 to verify that the lightweight DLM can achieve almost the same reminding useful life (RUL) accuracy as the DLM (DLM: 95.55% and lightweight DLM: 95.51%) using only 0.88% of DLM parameters, which verifies the low cost and high precision of the proposed model.

Cloud manufacturing (CMfg) has emerged as a next-generation manufacturing paradigm that has potential to revolutionize the manufacturing industry. In further promotion of CMfg, how to build CMfg services in an automatic and efficient manner is an essential and challenging subject. Currently, there is no literature addressing such issue. Aimed at facilitating rapid construction of CMfg services, this letter proposes a novel automated construction scheme for developing CMfg services, called Manufacturing Service Automated Construction Scheme (MSACS). First, we develop a three-phase workflow of MSACS to address the issues of how to construct CMfg services automatically using standalone software library package (SSLP). Next, we design a system architecture of MSACS to delineate how to implement MSACS. Then, we depict the designs of MSACS's core components. Finally, we apply MSACS to conduct industrial case studies to build the automatic virtual metrology cloud service and intelligent yield management cloud service for an intelligent manufacturing platform. Testing results demonstrate that MSACS can automatically construct the target CMfg services in a very efficient manner after uploading the required SSLPs. Thus, MSACS can significantly alleviate the burden of engineers in building CMfg services, and in turn can facilitate the promotion of CMfg.