To meet personalized user demands, customized and personalized production (CPP) has become an effective manufacturing paradigm. However, wired network connections inhibit flexible production line reconfiguration and current DRL methods cannot converge and obtain eligible scheduling results for CPP due to the high-dimensional solution space and the negligence of significant machine reconfiguration time. To address this challenge, we first propose a wireless manufacturing system framework to support ultra-flexible reconfiguration and resource scheduling. Next, we build a reconfiguration oriented scheduling model to reflect the significant impact of reconfiguration time. Then, we design a knowledge guided deep reinforcement learning algorithm to effectively solve the CPP scheduling problem facing the dimension explosion problem. The knowledge guidance incorporates reconfiguration time and machine workload to significantly reduce the feasible action space, enabling the rapid convergence of KGDRL. The experiment results show that our approach provides a robust and scalable solution and obtains shorter total makespan of whole production during scheduling.

Production logistics (PL) is essential for linking manufacturing activities through the timely transportation of work-in-progress items. However, increasing product customization, a broader range of materials, and more complex spatial-temporal constraints among resources have made PL orchestration significantly more challenging. To address these issues, we propose the cyber-physical computer (CPC), an intelligent edge terminal designed to coordinate operators, vehicles, robots, and materials involved in PL tasks. The CPC continuously collects Internet of Things (IoT) signals and extracts semantic-level relationships among PL resources, constructing a resource semantic graph that forms the basis for cyber-physical twinning. Upon receiving a new resource request, the CPC identifies relevant nodes to generate a corresponding spatial-temporal subgraph and performs subgraph reasoning by integrating contextual information with its local knowledge memory. Informed by task-specific insights distilled from prior expert decisions, the CPC recommends a prioritized allocation plan to the manager and enables interactive refinement using a large language model. To validate the proposed approach, we conduct comparative experiments across four simulated environments representing typical PL scenarios with varying degrees of dynamicity, along with a real-world case study at an air conditioning equipment manufacturer. Results demonstrate that the CPC, empowered by subgraph reasoning, outperforms existing methods in punctuality rate and delivery distance.

Rapid advancements in digitalization and artificial intelligence (AI) have catalyzed the adoption of digital twin technologies in the construction sector, enabling real-time synchronization between virtual models and physical systems. Simultaneously, on-site robotic automation has shown promise for reducing physical workloads, enhancing productivity, and contributing to sustainability goals that are key values of Industry 5.0. However, current digital twin implementations rarely incorporate multirobot construction systems, often relying on single-robot approaches or purely offline simulations. This gap hinders the realization of truly integrated construction environments that combine sensing, data analytics, wireless communications, and multirobot coordination. In response, this article proposes ConstrucTwin, a digital twin-driven multirobot construction framework designed to support complex construction tasks in real-world settings. By combining a 5G communication estimation-involved architecture and a cross-level planning strategy, ConstrucTwin streamlines interactions between physical robots and their digital counterparts. Essential tasks such as motion and task-level planning, as well as remote human-in-the-loop (HIL) oversight, are orchestrated within a single unified architecture. Through case studies involving rebar cage and brick wall construction, we demonstrate how an integrated approach to vision-based servoing and multirobot coordination enhances execution speed, precision, and scalability. The results underscore the system’s potential to advance human-centric, resilient, and sustainable construction, thereby aligning with the broader vision of Industry 5.0.

Chatter will affect machining accuracy, production efficiency, tool wear and workers' health. In order to avoid chatter early, a grinding chatter online monitoring model based on multisensor fusion information and hybrid deep neural network is proposed. First, the grinding experiment of acoustic emission (AE), force and displacement multichannel signal acquisition are carried out. Then, the grinding process is divided into five stages: air cut; stable; slight chatter; severe chatter; and severe chatter with beat effect, the correlation between sensor signals and classic evaluation indicators is analyzed. Next, a hybrid deep neural network model is established, and the feature classification ability, testing accuracy, sensitivity and generalization ability of the model are studied. Finally, the proposed model is applied to microstructured grinding wheel to further verify the generalization ability and chatter prediction ability of the model. The results indicate that our approach can predict the occurrence of flutter 0.15–0.45 s in advance.

Foundation models have demonstrated remarkable capabilities in various tasks such as natural language processing, content generation, and complex reasoning and have the potential to spark new technology and application revolutions in the industrial domain. However, Industrial Foundation Models (IFMs) remain almost unexplored, and the industrial sector has domain-specific issues and challenges to address when harnessing the capabilities of foundation models. Therefore, we introduce the concept and construction paradigm of IFMs and propose a 5-dimensional general framework of the IFMs. Moreover, we present the key research issues and technologies of IFMs and discuss some advanced and potential industrial applications. We hope this paper can serve as a useful resource for researchers seeking to innovate within the domain of IFMs.

Manufacturing systems envisioned for factories of the future will promote human-centricity for close collaboration in a shared working environment towards better overall productivity within the context of Industry 5.0. Robust and accurate recognition and prediction of human intentions are crucial to reliable and safe collaborative operations between humans and robots. For this purpose, this paper proposed a safety-aware human-centric collaborative assembly approach driven by function blocks, human action recognition for intention detection, and collision avoidance for safe robot control. Within the context, a deep learning-based recognition system is developed for high-accuracy human intention recognition and prediction, and an assembly feature-based approach driven by function blocks is presented for assembly execution and control. Thus, assembly features and human behaviours during assembly are formulated to support safe assembly actions. Skeleton-based human behaviours are defined as control inputs to an adaptive safety-aware scheme. The scheme includes collaborative and parallel mode-based pre-warning and obstacle avoidance approaches for a human-centric collaborative assembly system. The former is to monitor and regulate robot control modes when working in parallel with humans, and the latter uses a position-based approach to control robot actions by adaptively adjusting obstacle avoidance trajectories in a dynamic collaborative environment. The findings of this paper reveal the effectiveness of the developed system, as experimentally validated through an engine-assembly case study.

Human-robot collaborative disassembly (HRCD) has gained much interest in the disassembly tasks of end-of-life products, integrating both robot’s high efficiency in repetitive works and human’s flexibility with higher cognition. Explicit human-object perceptions are significant but remain little reported in the literature for adaptive robot decision-makings, especially in the close proximity co-work with partial occlusions. Aiming to bridge this gap, this study proposes a vision-based 3D dense hand-object pose estimation approach for HRCD. First, a mask-guided attentive module is proposed to better attend to hand and object areas, respectively. Meanwhile, explicit consideration of the occluded area in the input image is introduced to mitigate the performance degradation caused by visual occlusion, which is inevitable during HRCD hand-object interactions. In addition, a 3D hand-object pose dataset is collected for a lithium-ion battery disassembly scenario in the lab environment with comparative experiments carried out, to demonstrate the effectiveness of the proposed method. Note to Practitioners—This work aims to overcome the challenge of joint hand-object pose estimation in a human-robot collaborative disassembly scenario, of which can also be applied to many other close-range human-robot/machine collaboration cases with practical values. The ability to accurately perceive the pose of the human hand and workpiece under partial occlusion is crucial for the collaborative robot to successfully carry out co-manipulation with human operators. This paper proposes an approach that can jointly estimate the 3D pose of the hand and object in an integrated model. An explicit prediction of the occlusion area is then introduced as a regularization term during model training. This can make the model more robust to partial occlusion between the hand and object. The comparative experiments suggest that the proposed approach outperforms many existing hand-object estimation ones. Nevertheless, the dependency on manually labeled training data can limit its application. In the future, we will consider semi-supervised or unsupervised training to address this issue and achieve faster adaptation to different industrial scenarios.

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.

Digital twin (DT) models are computational models that can effectively represent different assets and processes in the manufacturing environment. Moreover, the DT models can support intelligent automation by integrating with the digital foundation and the data analytics provided by the cyber-physical system (CPS) in an industrial environment. To properly model a physical process, a DT model should be updated online to closely and timely model the underlying process and reduce modeling uncertainty in the CPS. However, most DT models are created offline and implemented online, which cannot be easily updated by using online data from heterogeneous product designs or manufacturing processes. This limitation arises from existing online learning methods, which are typically designed for identical structures, while real manufacturing CPS involves personalized designs and diverse processes. More importantly, there are limited samples for the same product design or manufacturing process due to manufacturing personalization, which slows down the online updating of DT models. In this article, the authors investigated online DT model updating based on data collected from different product designs and/or processes. The authors proposed a mutual active learning framework to identify informative samples from different designs or processes for online DT model updating. Specifically, by properly balancing the gradient-based features of the DT models and the similarity among these heterogeneous designs or processes, the proposed method can effectively query the most informative samples among heterogeneous processes to update the corresponding DT model in a timely manner. The advantages of the proposed method are illustrated by an engineering-driven statistical DT model for an additive manufacturing process (i.e., fused deposition modeling).

We consider a broadband over-the-air computation empowered model aggregation approach for wireless federated learning (FL) systems and propose to leverage an intelligent reflecting surface (IRS) to combat wireless fading and noise. We first investigate the conventional node-selection based framework, where a few edge nodes are dropped in model aggregation to control the aggregation error. We analyze the performance of this node-selection based framework and derive an upper bound on its performance loss, which is shown to be related to the selected edge nodes. Then, we seek to minimize the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones by optimizing the selected edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. By resorting to the matrix lifting technique and difference-of-convex programming, we successfully transform the formulated optimization problem into a convex one and solve it using off-the-shelf solvers. To improve learning performance, we further propose a weight-selection based FL framework. In such a framework, we assign each edge node a proper weight coefficient in model aggregation instead of discarding any of them to reduce the aggregation error, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required.We also analyze the performance of this weight-selection based framework and derive an upper bound on its performance loss, followed by minimizing the MSE via optimizing the weight coefficients of the edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. Furthermore, we use the MNIST dataset for simulations to evaluate the performance of both node-selection and weight-selection based FL frameworks.

The deep learning based fault diagnosis methods show excellent performance. However, cost and delay factors make it difficult for their widespread industrial application. Microcontroller units (MCUs) in industrial equipment have the advantages of real-time response and high reliability and usually have some redundant computational resource. However, even lightweight deep learning models cannot be deployed in MCUs due to severely limited computational resources. This paper proposes an end-edge collaborative fault diagnosis framework, by combining real-time decision-making at the end with dynamic adaptive diagnosis at the edge to improve inference performance. The model’s minimum input size is deduced through theoretical analysis of the bearing working mechanism, and to make the model suitable for MCUs, we leverage the differential characteristics of the bearing vibration data and proposed a TinyML model based on stacked autoencoders. The pre-autoencoder extracts differential features, while the post-autoencoder performs fault diagnosis based on pooled differential features. Finally, the stacked-autoencoder model and collaborative framework were evaluated using the CWRU bearing dataset, achieving 384x compression in parameter size and 100% accuracy for binary fault classification, requiring only 6.44kB RAM. With the dynamic adaptive collaboration mechanism, the proposed fault diagnosis framework can reduce the edge load by approximately 94%.

With the increasingly developed industry and more comprehensive product offerings, customized and personalized products (CPPs) gradually become a main business model of many enterprises. However, the characteristics of CPPs, such as large differences in product modules and short product delivery cycles, put forward very high demands for the intelligence, flexibility and real-time performance of cloud manufacturing (CMfg). To satisfy the above typical demands, a cloud-edge-device collaborative framework of CMfg is proposed to support distributed data processing and fast decision-making. In the context of Cloud-edge-device collaboration, the vertically and horizontally distributed deployment and update mechanisms of deep learning models (DLMs) are brought forward and analyzed in detail to provide rapid response and high-performance decision-making services for CPPs. In addition, related key technologies are presented to provide references for the technical research direction.

We consider a broadband over-the-air computation empowered model aggregation scheme for federated learning (FL) in Industrial Internet of Things systems. Due to fading and communication noise, the received global gradient parameters inevitably become inaccurate, leading to a notable decrease of the learning performance. Instead of discarding any edge nodes to reduce the aggregation error, we propose to assign each of them a proper weight coefficient in the model aggregation procedures, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required in this paper. We derive an upper bound on the performance loss of the proposed FL scheme, which is shown to be related to the weight coefficients of edge nodes and the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones. Then, we derive a closed-form expression for MSE and use it as the objective function to formulate an optimization problem with respect to the edge nodes’ transmit equalization coefficients, their weight coefficients, and the receive scalars of the cloud server. We transform the formulated optimization problem into a convex one and solve it optimally using CVX. Last, we leverage the popular MNIST dataset and conduct experiments to evaluate the prediction accuracy of the proposed FL scheme. Simulation results demonstrate its superior performances.

Using soft actuators as legs, soft quadruped robots have shown great potential in traversing unstructured and complex terrains and environments. However, unlike rigid robots whose gaits can be generated using foot pattern design and kinematic model of the rigid legs, the gait generation of soft quadruped robots remains challenging due to the high DoFs of the soft actuators and the uncertain deformations during their contact with the ground. This study is based on a quadruped robot using four Compressible Tendon-driven Soft Actuators (CTSAs) as the legs, with the actuator's compression motion being utilized to improve the walking performance of the robot. For the gait design, an inverse kinematics model considering the compression of the CTSA is developed and validated in simulation. Based on this model, walking gaits realizing different motion speeds and directions are generated. Closed loop direction and speed controllers are developed for increasing the robustness and precision of the robot walking. Simulation and experimental results show that omnidirectional locomotion and complex walking tasks can be realized by tuning the gait parameters and the motions are resistant to external disturbances.

In cloud manufacturing systems, fault diagnosis is essential for ensuring stable manufacturing processes. The most crucial performance indicators of fault diagnosis models are generalization and accuracy. An urgent problem is the lack and imbalance of fault data. To address this issue, in this article, most of existing approaches demand the label of faults asa prioriknowledge and require extensive target fault data. These approaches may also ignore the heterogeneity of various equipment. We propose a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, named label-split multiple-inputs convolutional neural network, in cloud manufacturing. First, a multiattribute cooperative representation-based fault label sampling space enlarging approach is proposed to extend the variety of diagnosable faults. Besides, a multi-input multi-output data augmentation method with label-coupling weighted sampling is developed. In addition, a cloud-edge collaborative adaptation approach for fault diagnosis for scene-specific equipment in cloud manufacturing system is proposed. Experiments demonstrate the effectiveness and accuracy of our method.

In this paper, we consider a millimeter wave multiple-input single-output tracking system, where the time-varying angle of departure (AoD) is assumed to change following a discrete state Markov process. Depending on whether the associated AoD transition function is available or not, we propose two different training beam sequence design approaches. Specifically, in the case when the AoD transition function is available, we leverage the maximum a posteriori criterion to estimate the updated AoD in each beam tracking period. Since it is infeasible to derive an explicit expression for the resultant estimation error rate, we turn to its upper bound, which possesses a closed-form expression and is therefore used as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by the analog phase shifters, we resort to a particle swarm algorithm to solve the formulated optimization problem. In the case when the AoD transition function is unavailable, we turn to the maximum likelihood criterion for AoD estimation. To cope with the unknown AoD transition function, we reformulate the beam tracking problem as a partially observable Markov decision process problem and develop an actor-critic reinforcement learning framework to obtain an efficient training beam sequence design. Numerical results demonstrate superiorities of the proposed training beam sequence design approaches for both two cases.

Industrial trends and new generation information and communication technologies have become driving forces for advancement in the process control and manufacturing industry. This paper thoroughly investigates the future industrial trends from the perspectives of market, engineering system, product, innovation, etc., then incorporates the concept of software defined networking and proposes a new cloud based manufacturing model, Software Defined Cloud Manufacturing (SDCM). The key characteristics, reference architecture and emerging enabling technologies of SDCM are presented to support the SDCM's advantages in terms of real-time response, reconfiguration and operations of the manufacturing system. Resource virtualization and function programmability lie at the core of SDCM to empower the manufacturing sector. The paper is concluded with remarks and future work.

Cloud manufacturing is a novel service-oriented networked manufacturing paradigm that aims to provide on-demand manufacturing cloud services to consumers. Scheduling is a critical means for achieving that aim. Currently, research on scheduling in cloud manufacturing is still in its infancy, and current frequently adopted meta-heuristic algorithm-based approaches have some shortcomings, e.g. they require complex design processes and lack adaptability to dynamic environments. Deep reinforcement learning (DRL) that combines advantages of reinforcement learning and deep learning provides an efficient, adaptive and intelligent approach for solving scheduling problems in cloud manufacturing. However, to the best of our knowledge, there has been no application of DRL to scheduling in cloud manufacturing. This work conducts a preliminary exploration over this issue. First, a DRL-based framework for scheduling in cloud manufacturing is proposed. Then a DRL model for online single-task scheduling in cloud manufacturing is presented to demonstrate the effectiveness of the framework. DRL as a promising technique will find wide applications in cloud manufacturing, and this work can provide some reference for future research on this.

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

Accurate kinematic models and measurements are needed in many robotic applications. However uncertainties related to joint angle measurements and manipulator geometry are unavoidable, especially when grasping and using different tools or when we do not have access to an accurate robot model, e.g. when we construct a robotic system by hand. The generalized inverse kinematics methods are not applicable when a manipulator stay inside its singular region. We derive the upper bounds on the joint measurement errors and geometric uncertainties, in order to guarantee that the open-chain serial manipulators stay outside the singular region. These bounds in other words enable en effective execution of generalized inverse kinematics methods for a robotic system which is prone to geometric uncertainties. In addition to the analytic derivation, We validate the proposed bounds through a trajectory tracing task performed by a PR2 robot simulator.

Constraint based programming provides a versatile framework for combining several different constraints into a single robot control scheme. We take advantage of the redundancy of a robot manipulator to improve the execution of a reactive tracking task, in terms of a task-dependent measure which is a weighted sum of velocity transmissions along the current directions of motion. With inspiration from recent work, we provide analytical gradients and computable weights of the task-dependent measure, which enable us to include it in a reactive constraint based programming framework, without relying on inexact numerical approximations and manually tuning weights. The proposed approach is illustrated in a set of simulations, comparing the performance with a standard constraint based programming method.

Feature-based technologies are widely researched for manufacturing automation. However, in current feature models, features once defined remain constant throughout the whole manufacturing lifecycle. This static feature model is inflexible to support adaptive machining when facing frequent changes to manufacturing resources. This paper presents a new machining feature concept that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. Basic geometry information for feature construction of complex parts with various intersecting features is represented as a set of meta machining features (MMF). Optimum feature definition is generated adaptively by choosing optimum merging strategies of MMFs according to the capabilities of the selected machine tool, cutter, and cutting parameters. A composite function block for dynamic machining feature modelling is designed with Basic Machining Feature Function Block, Meta Machining Feature Extraction Function Block and Feature Interpreter Function Block. Once changes of the selected machining resources occur, they are informed as input events and machining features are then updated automatically and adaptively based on the event-driven model of function blocks. An example is provided to demonstrate the feasibility and benefits of the developed methodology.

This paper presents a new approach for real-time collaborations in adaptive manufacturing, including web-based remote monitoring and control of an industrial robot, and active collision avoidance for human-robot collaborations. It is enabled by using virtual 3D models driven by real sensor data and depth images of human operators. The objectives of this research are to significantly reduce network traffic needed for real-time monitoring over the Internet and to increase the human safety in a human-robot coexisting environment. The results of a case study show that the approach consumes less than 1% of network bandwidth of traditional camera-based methods, and is feasible and practical as a web-based solution.

In this paper, a new energy model is developed based on the kinematic and dynamic behaviors of a chosen machine tool. One significant benefit of the developed energy model is their inherited relationship to the design variables involved in the manufacturing processes. Without radical changes of the machine tool's structure, the proposed model can be readily applied to optimize process parameters to reduce energy consumption. A new parallel kinematic machine Exechon is used as a case study to demonstrate the modeling procedure. The derived energy model is then used for simulation of drilling operations on aircraft components to verify its feasibility. Simulation results indicate that the developed energy model has led to an optimized machine setup which only consumes less than one-third of the energy of an average machine setup over the workspace. This approach can be extended and applied to other machines to establish their energy models for green and sustainable manufacturing.

This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study.

This paper presents an integrated approach for Web-based collaborative manufacturing, including distributed process planning, dynamic scheduling, real-time monitoring, and remote control. It is enabled by a Web-based integrated sensor-driven e-ShopFloor (Wise-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. Utilizing the latest Java technologies (Java 3D and Java Servlet) for system implementation, this approach allows users to plan and control distant shop floor operations based on runtime information from the shop floor. The objective of this research is to develop methodology and algorithms for Web-based collaborative planning and control, supported by real-time monitoring for dynamic scheduling. Details on the principle of the Wise-ShopFloor framework, system architecture, and a proof-of-concept prototype are reported in this paper. An example of distributed process planning for remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward Web-based collaborative manufacturing.

Interoperable manufacturing systems help manufacturing companies stay competitive in the environment of frequent and unpredictable market changes. An important part of a manufacturing system is computer numerically controlled (CNC) machine tools. Over the years, G-codes have been extensively used by CNC machine tools and are now considered as a bottleneck for making these machines adaptable and interoperable. Two new technologies emerged in recent years: Standard for the Exchange of Product data for Numerical Control (STEP-NC) and function blocks. The STEP-NC data model represents a common standard for NC programming, making the goal of a generic NC code generation facility a reality. Function blocks are an emerging IEC standard for distributed industrial processes and control systems. They can be used for CNC controls to encapsulate machining data, such as machining features and their needed algorithms. This paper introduces the above two new standards and the technologies that are developed based on the standards. The main body is devoted to analyze the standards from the functionality viewpoint. These functionalities include, bidirectional information flow in computer-aided design/computer-aided manufacturing, data sharing over the Internet, the use of feature-based machining concept, modularity and reusability, intelligent and autonomous CNC, and portability among resources. Some implementations are also presented to showcase how the standards are used to develop technologies for interoperable machining. Note to Practitioners-Modern computer numerically controlled (CNC) machine tools are limited in functions because their controllers rely on G-codes for communications. G-code is considered a "dumb" language as it only documents instructional and procedural data, leaving most of the design information behind. G-code programs are also hardware dependent, denying modern CNC machine tools desired interoperability and portability. In recent years, two new standards emerged, STEP-NC and function blocks. They may hold the key to empowering CNC machine tools with richer information which, in turn, gives CNC machine tools the ability to "think" intelligently and to be interoperable. This paper introduces these two standards, the technologies that have been developed based on the standards and some prototype systems using the standards and technologies. The intention is not to highlight any achieved research outcome. Instead, the focus is on informing the research and practical world about these new standards, analyzing them from the viewpoint of supporting interoperable CNC machine tools, and offering some futuristic views about these standards and technologies. While these standards are still in their infancy, research activities and prototype systems are already coming thick and fast. There seems to be a "healthy" mixture of participants working in the field. They range from the manufacturers of all systems related to the data interface (i.e., CAM systems, controls, and machine tools), to the users and academic institutions.

Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified.

The research objective is to develop methodologies and framework for collaborative process planning and scheduling, supported by a real-time monitoring system in distributed environments. A function block enabled collaborative process planning approach is proposed to handle various dynamic changes during process plan generation and execution. This paper focuses on collaborative process planning, particularly on the development of a function block designer As function blocks can sense environmental changes, it is expected that a so generated process plan can adapt itself to the changes with dynamically optimized solutions for plan execution and process monitoring.

In today's globalized business world, outsourcing, joint ventures, mobile and cross-border collaborations have led to work environments distributed across multiple organizational and geographical boundaries. The new requirements of portability, configurability and interoperability of distributed device networks put forward new challenges and security risks to the system's design and implementation. There are critical demands on highly secured collaborative control environments and security enhancing mechanisms for distributed device control, configuration, monitoring, and interoperation. This paper addresses the collaborative control issues of distributed device networks under open and dynamic environments. The security challenges of authenticity, integrity, confidentiality, and execution safety are considered as primary design constraints. By adopting policy-based network security technologies and XML processing technologies, two new modules of Secure Device Control Gateway and Security Agent are introduced into regular distributed device control networks to provide security and safety enhancing mechanisms. The core architectures, applied mechanisms, and implementation considerations are presented in detail in this paper.

Global competition is driving manufacturing companies to change the way they do business. New kinds of shop floor control systems need to be implemented for these companies to respond quickly to changing shop floor environments and customer demands. This paper presents a new concept called iShopFloor-an intelligent shop floor based on the Internet, web, and agent technologies. It focuses on the implementation of distributed intelligence in the manufacturing shop floor. The proposed approach provides the framework for components of a complex control system to work together as a whole rather than as a disjoint set. It encompasses both information architecture and integration methodologies. The paper introduces the basic concept of iShopFloor, a generic system architecture, and system components. It also describes the implementation of eXtensible Markup Language message services in iShopFloor and the application of intelligent agents to distributed manufacturing scheduling. A prototype environment is presented, and some implementation issues are discussed.

Targeting the remote monitoring and control of shop floors, this paper proposes a new framework called Wise-ShopFloor Web-based integrated sensor-driven e-Shop Floor that can be applied to distributed manufacturing environments. It utilizes the latest Java technologies (Java 3D and Java Servlet) as enabling technologies for system implementation. This web-based framework allows users to monitor and control a distant shop floor device using Java 3D models instead of cameras. The behavior of a 3D model is driven by sensor signals of its physical counterpart. The goal of this research work is to eliminate network traffic, while still providing end users with intuitive environments.

The paper presents a framework for building Web-based collaborative workspaces using the latest Java technologies-Java 3D, JavaServer Page (JSP), and Java Servlet. This Web-based approach allows designers, engineers and production managers to share a common workspace that can be used for design review, production monitoring, remote control, and troubleshooting, based on a set of interactive Java 3D models that represent the physical world with common interests. Following a brief overview of the related research work, the paper discusses the Java 3D concept from its scene graph structure to behavior control, and explains our approach to building Web-based collaborative workspaces using Java 3D. The proposed framework uses the popular client-server architecture and view-control-model design pattern with a secured session control. Control logic and the interfaces, which interact with the real world, are handled by an application server through servlets. The benefits enabled by the framework include reduced network traffic, increased flexibility of remote monitoring, interactive control, Web-based synchronous collaboration and quick response. It also shows significant potential for various Web-based real-time and distributed applications.