In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of$1.6~\mu \text{m}$and a standard deviation is$0.7~\mu \text{m}$. By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of$13.45~\mu \text{m}$and a standard deviation of$2.5~\mu \text{m}$. In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa Note to Practitioners—As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of previous layer have a strong influence on the printing quality of current layer. The variations of surface roughness will lead to the fluctuation of mechanical properties of the fabricated components. Additionally, to the best of author’s knowledge, current commercial L-PBF machines can not actively control the surface roughness of samples during the process. The proposed IAMA in this work can predict and control the roughness on the top surface of each layer during L-PBF process. Therefore, the constructed architecture has strong potential for integrating into the commercial L-PBF machine for controlling the surface roughness on the top of the parts during the manufacturing process. As a result, the quality of the fabricated components is expected to be in consistent. Accordingly, L-PBF machine equipped with IAMA will have a strong potential in applying for mass production of the components in aerospace and automobile industries.

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.

Quality control is the key for the widespread adoption of metal additive manufacturing (AM). However, online quality estimation is challenging because high-frequency stream data derived from in situ metrology have to be processed in a timely manner to figure out the complicated interactions among material, machine, and part. To tackle such issue, this article proposes an intelligent AM metrology (IAMM) architecture to decouple and evaluate quality variations caused by material properties, machine issues, and process parameters when building an AM part. The IAMM architecture can also estimate the online layer-to-layer quality (e.g., roughness and density of an AM part) by applying the robust parameters derived from the uniform design (UD) method via the enhanced automatic virtual metrology technology in a parallel computing environment, as soon as the microfeatures of melt-pools and the macrofeatures of each layer are extracted. In addition, the associated indices and features in the IAMM architecture can also be used to evaluate the defects caused by machine issues in the given process parameters. The results of case studies validate the applicability of the IAMM architecture and show that the proposed estimation models are prospective for future closed-loop control in an AM process.

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.

To reduce cost, this paper proposes a system architecture to simulate and assess the multivariate of equipment properties. The architecture integrates the Monte Carlo simulation, the neural network model and the sensitivity analysis to construct a virtual metrology system. By assuming the property's probability distribution, the architecture generates the extreme input data to supplement the actual data for enhancing the model accuracy and estimating the property trend. An industrial case applied to validate the proposed system architecture

The required reliability in applications of a distributed computer system is continuous service for 24 hours a day, 7 days a week. However, computer failures due to exhaustion of operating system resources, data corruption, numerical error accumulation, and so on, may interrupt services and cause significant losses. Hence, this work proposes an application cluster service (APCS) scheme. The proposed APCS provides both a failover scheme and a state recovery scheme for failure management. The failover scheme is designed mainly to automatically activate the backup application for replacing the failed application whenever it is sick or down. Meanwhile, the state recovery scheme is intended primarily to provide an inheritable design pattern to support applications with state recovery requirements. An application simply needs to inherit and implement this design pattern, and then can accomplish the task of state backup and recovery. Furthermore, a performance evaluator (PEV) that can detect performance degradation and predict time to failure is developed in this study. By using these detection and prediction capabilities, the APCS can perform the failover process before node breakdown. Thus, applying APCS and PEV can enable a distributed computer system to provide services with near-zero-downtime.

A holonic manufacturing system, designed to realize agile manufacturing, must (be able to) integrate the entire range of manufacturing activities from ordering, through design, modeling, production, to delivery. These activities are performed at many distributed sites. In order to effectively integrate these distributed sites, this work adopts distributed object and mobile object technologies, RosettaNet implementation framework, as well as 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 by adopting the technologies of the distributed object-oriented approach with common object request broker architecture infrastructure, n-tier client/server architecture, a knowledge base, and data warehousing to achieve the properties of holon, error recovery, and security certification. The communication holon (CH) is then generated by inheriting the generic holon. Finally, CHs are employed to establish HICS. The CH exhibits basic holonic attributes, such as intelligence, autonomy, and cooperation. Furthermore, the CH can handle partner interface processes, and data exchange by various data formats following the standards of RosettaNet business messages. The failure recovery mechanism of the CH causes HICS to be more reliable than legacy systems. As such, HICS can meet the future requirements of supply-chain information integration of virtual enterprises.Note to Practitioners-An HICS that handles the information flow of the supply chain is proposed in this work. HICS is composed of many communication holons (CHs). Any company that would like to use HICS to exchange information for enterprise integration may possess a CH. The CH is implemented with the Java web-start technology. Therefore, any company can download a CH from the web server via a web browser. The company is, then, able to communicate with other members throughout the supply chain. Accordingly, CHs are scattered throughout the Internet/intranet and each CH can act as both an information supplier and an information consumer. Further, because the CH adopts the RoesettaNet implementation framework (RNIF), any company that has the capability of RNIF can also communicate with the members in the HICS framework via RosettaNet business messages.

There are hundreds of equipment in a semiconductor factory. In order to increase equipment utilization and improve product quality yield, more reliable services provided by equipment managers are expected. Therefore, it is essential to develop a reliable service management scheme (SMS) for semiconductor factory management systems. In this work, the SMS is developed by applying the Jini and contract technologies. A generic evaluator (GE) is also developed in the SMS to possess error-detection and function-replacement. The GE can also maintain the credit values of all the equipment managers. With the SMS, the process status and parameters of equipment managers in service are stored periodically. If an equipment manager malfunctions, the GE detects and confirms it and sends an event notice to the factory manager. Then, the factory manager finds a spare equipment manager and transfers the backup process status and parameters to the spare equipment manager. Finally, the spare equipment manager can continue monitor the manufacturing process starting from the point where the malfunctioned one left off. Evaluation of reliability improvement and an illustrative example of the SMS are also presented.

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.

Equipment Managers (EMs) play a major role in a Manufacturing Execution System (MES). They serve as the communication bridge between the components of an MES and the equipment. The purpose of this paper is to propose a novel methodology for developing analytical and simulation models for the EM such that the validity and performance of the EM can be evaluated. Domain knowledge and requirements are collected from a real semiconductor packaging factory. By using IDEFO and state diagrams, a static functional model and a dynamic state model of the EM are built. Next, these two models are translated into a Petri net model. This allows qualitative and quantitative analyses of the system. The EM net model is then expanded into the MES net model. Therefore, the performance of an EM in the MES environment can be evaluated. These evaluation results are good references for design and decision making.

Information integration of the supply chain for the manufacturing industry is essential because a competent and promising company needs an efficient information system to communicate with its customers, supplies, and partners within the entire supply chain. In this research, a collaborative and event-driven supply chain information system (SCIS) is developed by using mobile object technology. The Unified Modeling Language (UML) is applied to analyze and design the SCIS Framework which contains two major components: information coordinator component (ICC) and agent component (AC). The ICC acts as the information integration manager. It manages all the ACs and establishes proper event channels for them to exchange information. Each member within the supply chain needs to possess an AC such that it can utilize the SCIS to exchange information with the other members. An application example is constructed and its corresponding system integration and testing procedure is demonstrated. From this demonstration result, it is believed that this SCIS framework provides a collaborative, event-driven, object-oriented, and agent-based infrastructure for the supply chain members to mutually exchange information efficiently.