Search and rescue operations save thousands of lives yearly, and robotics can further improve survival rates in hazardous, partially observable environments. This article introduces a trustworthy human-autonomy teaming (HAT) navigation framework that fuses human situational awareness with algorithmic planning across both global and local layers. At the global level, a hybrid graph integrates generalized Voronoi diagrams (GVDs), for high-clearance, topology-preserving roadmaps interpretable to operators, with a spatiotemporal graph that dynamically updates edges and node salience as obstacles and mission priorities evolve. Human input is incorporated through a node optimization protocol that prioritizes access nodes, adjusts waypoint sequences, and invokes RRT*-informative path planning when jump-search shortcuts become unreliable, thereby aligning exploration with operator-defined areas of interest and mission constraints. At the local level, a bioinspired neural network (BNN) with an adaptive window strategy provides reactive obstacle avoidance and target tracking, ensuring smooth and safe motion in cluttered, dynamic scenes. Simulation results across indoor and urban-like environments demonstrate that the proposed framework enhances path efficiency, robustness to environmental change, and responsiveness while reducing unnecessary re-planning. By unifying GVD-based global structure, human-guided decision-making, and BNN-based local reactivity, the HAT framework advances autonomous navigation toward greater safety, adaptability, and effectiveness in real-world search and rescue operations.

Aircraft assembly scheduling (AAS) grows increasingly complex in the presence of multiple uncertainties, given that these factors significantly influence assembly process efficiency and the optimal allocation of resources. Existing scheduling methods have exhibited their limitations in taking into consideration of the uncertainties related to assembly resources, the actual number of workers that may change in process, and the previous assembly experience with high productivity. To overcome these limitations, a workstation adjustment mechanism (WAM) is proposed to improve the availabilities of workstations. WAM is fully integrated with actual worker configurations to mitigate the shortages of workers in the task conversions in a schedule. A knowledge transfer-based multiobjective evolutionary algorithm (KT-MOEA) is developed as a systematic optimization framework for addressing multiobjective AAS problems. Moreover, a design of experiments systematically evaluates the impact of controllable variables across multiple instances, which enhances the robustness, interpretability, and generalizability of the proposed framework. This innovative approach systematically integrates transfer learning and the nondominated sorting genetic algorithm II to enhance optimization performance. Quantitative results demonstrate that the proposed KT-MOEA significantly enhances optimization performance, achieving up to 50% improvement in convergence (inverted generational distance), substantial gains in solution diversity (hypervolume), and at least a 7% enhancement in the quality of Pareto-optimal solutions compared with benchmark algorithms.

Zero-shot medical image diagnosis is driven by vision-language pretraining on large-scale image-text pairs. However, aligning chest X-rays with reports remains challenging, particularly when multiple abnormalities with overlapping visual features lead to ambiguous correspondences. To address this challenge, we propose MedCMCL-VLP, a novel vision-language pretraining framework that simulates the hierarchical learning process of medical professionals. The framework consists of two key components: (1) a new enhanced Vision Mamba encoder with a spatial adaptor to capture long-range spatial dependencies and small-scale abnormalities in high-resolution chest X-rays and (2) a knowledge-guided text encoder that integrates medical domain knowledge with large language models (LLMs) to capture fine-grained semantic information from radiology reports. More importantly, MedCMCL-VLP employs a two-phase training paradigm that guides the model from novice to expert levels, effectively improving diagnostic accuracy. We evaluate MedCMCL-VLP on five public chest X-ray datasets. The experimental results show that our framework achieves state-of-the-art performance in both zero-shot and fine-tuned classification tasks.

This paper presents a cognitive map (CM) model fused with a histogram-based reactive local navigator algorithm for real-time robot mapping and navigation. Drawing inspiration from biological navigation, the CM model integrates elements like landmarks, Euclidean distance, exploratory movement, and reward-driven actions. Key features of the model include types of cognitive mapping cells designed to replicate their biological functions in a computationally efficient manner. Built on cognitive map concepts used to explain mammalian navigation, this CM model enables robots to navigate complex environments without needing complete environmental exploration. Additionally, an enhanced ℬ-spline curve scheme ensures a smoother, safer trajectory. After planning a global route, the model utilizes a histogram-based local navigation algorithm to dynamically avoid obstacles while creating real-time maps as the robot follows its path. Simulation and comparison results validate that this integrated approach supports real-time navigation and mapping in unknown environments, offering significant improvements in robotic spatial navigation and real-time map building.

In this paper, the concept of Sustainable Manufacturing (SM) is discussed, and the focus is put on the elimination of wastes in a product’s life cycle. Recent development on enabling technologies to advance SM are surveyed to identify promising research areas. We have found virtualVerification and Validation(V&V) should be promoted to enhance the sustainability especially inSmall and Medium sized Enterprises(SMEs). V&V are typically non-value-added and the effects on V&V should be minimized; while due to lack of expertise, most of SMEs rely heavily on prototyping and physical experiments to evaluate their products. We propose virtual V&V to replace physical experiments to the maximum extent. To show the significance of the proposed concept, a case study of virtual V&V system is developed to reduce the needs of physical prototyping and testing in product development. This benefits greatly to (1) cost savings by replacing numerous physical prototyping and testing by virtual V&V, (2) lead-time reduction for new product to enter emerging markets, and (3) global optimization of product design by analyzing and exploring a large number of design options. The improvement at these aspects contributes to system sustainability significantly. The concept of using virtual V&V to reduce non-value-added prototyping and testing is applicable to manufacturing businesses in most of SMEs who design and test their own products.Note to Practitioners—This work is highly motivated by a number of the authors’ industry projects with regional SMEs who put heavy investments in testing to prove products’ quality to clients before the orders for products can be awarded; manufacturing businesses related to testing are non-value-added that should be minimized from the perspective of sustainability. Virtual V&V is proposed as a vital solution to overcome their dilemma, and the proposed solution has its theoretical and practical significance to reduce wastes, shorten lead-times, and optimize products based on parametric study for a wide scope of design alternatives. This helps to increase the lifespan of SMEs ultimately.

Detecting abnormal conditions in an electrical fused magnesia furnace (EFMF) is challenging due to the difficulties in measuring ultra-high temperature, fluctuation of raw materials, uncertain boundary conditions, and numerous processing variables. In practice, images acquired from the wall of furnace are used to reflect working conditions of furnace indirectly. In this article, a supervised matrix regression algorithm is proposed to detect abnormal conditions based on the matrix data. Bilinear analysis is conducted to connect matrix data with labels and thus preserve the correlations in matrix with decreased complexity. By supervising and adjusting the weights of outliers in the modeling process, neutral and chaotic datasets can be monitored in low-dimensional spaces accurately. Moreover, a common information extraction method is proposed to eliminate redundant data. To verify proposed methods, the images recorded from an actual EFMF are used to detect abnormal conditions, and it is found that the proposed methods are able to monitor actual EFMF and issue alarms timely and effectively.

Multimode process monitoring is critical in practical industrial processes, as it directly affects the quality and safety of product. The disadvantages of existing multimode process monitoring methods are as follows. 1) The operation mode isolation is not accurate since the interference from the common information in the original data space. 2) The coordinated operation mode isolation strategy is not considered in the traditional method. 3) The local spatial properties are ignored in multisubspace methods. In this article, a multimode process monitoring algorithm called common and unique subspace decomposition (CUSD) is proposed. Advantages of the proposed method are as follows. 1) The unique mode subspace containing the unique information for each mode and the common mode subspace containing the common information between different modes are decomposed in order to eliminate the influence from the common information. 2) The local and global spatial properties are exhaustively extracted in each subspace by the fusion of the principal components and the manifold information. 3) A coordinated Bayesian inference strategy based on the extracted properties in the decomposed unique mode subspace is proposed for operation mode isolation purpose. Simulations on three different processes have validated that there is an approximately 20% improvement of average operation mode isolation accuracy in the unique mode subspace of CUSD than in the original data space.

Aircraft assembly scheduling (AAS) with multiconstraints, multitype workers, and the orders of tasks has become a key research focus in advanced industrial manufacturing. An elite gene retention metaheuristic algorithm (EGHA) is proposed in this article as a transfer knowledge generator, and the elite gene transfer learning heuristic algorithm (TL-EGHA) is also utilized as an optimized framework to tackle these issues for the AAS problem. Fully considered characteristics of the AAS problem, double time windows are proposed to deal with the constraints of worker type and space restrictions, and this guarantees that the proposed algorithm can obtain the solution quickly. The transfer learning strategy imports transfer knowledge associated with features and previous experiences, which prompts the initialization results closer to the task goals and supports dynamic adjustments of the parameters in TL-EGHA to enhance the global searching capability significantly. TL-EGHA has been a verified advancement in scheduling four real-world aircraft assembly lines by a comparative study with some existing scheduling algorithms, including well-known genetic transfer learning.

In this article, the genetic transfer learning (GTL) method is proposed for knowledge transfer of sustainable assembly manufacturing systems. Existing methods for the assembly line balancing problem, such as genetic algorithms (GAs), suffer from three significant limitations, i.e., tedious “trial-and-error” processes, no utilization of existing system solutions, and no consideration of new constraints on system reconfiguration. To address these problems, we propose GTL to migrate the knowledge of the GA setup in system reconfiguration. The contributions of this article are as follows. First, transfer pretreatment is performed to formulate knowledge of existing systems and adapt to the new constraints of future systems. Second, the similarity of existing and future systems is defined quantifiably to determine transfer conditions and avoid weak and negative transfer for maximizing knowledge transfer. Finally, the transfer strategy is made to determine the method and knowledge to be transferred. The case study of a computer assembly line shows that adopting transfer learning has helped to improve assembly line efficiency and sustainability.

Blockchain technology (BCT) has brought a paradigm shift to Business Process Management (BPM). BCT provides a trusted decentralized infrastructure to secure data and process executions using distributed ledgers and smart contract to manage complex business processes. Numerous efforts have been made to exploit BCT in supporting dynamic and trusted collaborations of business processes. This paper aims to understand recent BCT development for its BPM applications and identify the limitations and challenges for further development via a systematic literature review (SLR). It is found that numerous works have reported using BCT as technical solutions to fulfill some traditional BPM functions. This paper is distinguished from existing works, especially several relevant surveys in the sense that (1) the impact of using BCT in BPM is thoroughly explored to identify new constraints and challenges explicitly brought by blockchains; (2) the requirements for Business Process Compliance (BPC) are firstly analyzed in detail. Note that BPC is to assure the adherence of business processes to pre-defined policies, standards, specifications, regulations, and laws when business processes are executed. To fill the gaps of BCT applications in these two aspects, Blockchain-as-a-Service (BCaaS) is adopted in business process architecture, and the trends of BCT developments are identified accordingly.

Workflow is used to support and connect business processes (BP) in organizations. Historically, it is used to define the control of how tasks are coordinated and executed. Its importance has been continuously increasing with the incorporation of rapidly developed Service-oriented Architecture (SoA), Blockchain, and Internet-of-Thing (IoT); new information technologies have expanded the coverage of workflow across various applications. Workflows often interact with services, where SoA is a key driver to smoot service discovery and provide standards for interoperation. In decentralized collaborative environments, a workflow often deals with disparate services dynamically and interacts with services on demand. This not only unlocks the potentials of workflow applications in business process managements (BPM), but also precipitates significant challenges and has brought considerable attentions to the research community. This paper investigates the state-of-the-art of service workflow modelling and enabling technologies. It begins with the identification and examination of workflow and IoT characteristics; it proposes a workflow architecture to classify existing works on service workflows, and it summarizes the methods for workflow modeling, service interoperation to identify the limitations of existing works and clarify future research directions in using service workflows.

Fall detection is of increasing significance in terms of the health monitoring of the elderly and disabled people, as falls may lead to physical injuries or even mental trauma. The existing fall detection methods have achieved impressive performance, but limitations present in operability, interface with public healthcare systems, and other technical issues such as high-power consumption, cost, and reliability. In this article, we present a wearable fall detection system, which is based on a novel multilevel threshold algorithm. The algorithm combines micro-electro-mechanical-systems (MEMS) with narrow band Internet of Things (NB-IoT). The system also includes a user interface for healthcare professionals, developed based on the cloud technology and server-client architecture. For the verification of the algorithm, we recruited 20 volunteers to perform the activities of daily living and falls. The experimental result showed that the proposed algorithm can achieve an accuracy of 94.88%, a sensitivity of 95.25%, and a specificity of 94.5%, suggesting the effectiveness of our system.

Due to energy efficiency and near real-time finality settlement, permissioned blockchain is mainly used in blockchain-based Internet of Things (BIoT) services. This blockchain requires validators to reach the consensuses. Currently, the validators are chosen based on generic attributes of trusted nodes capriciously. However, service users have no mechanism to provide their requirements in assessing validators. Existing works are biased from users’ perspectives, which risks collusion attacks due to improperly selected validators. This article aims to solve this by considering users’ expectations on services. A user-oriented framework is proposed to evaluate the trust of BIoT services with the consideration of validator attributes. It provides an effective mechanism to express requirements. Specification language is adopted in our algorithm to perform compliance checking. The feasibility has been verified in a simulation. We found that the permissioned blockchain has also benefited from the proposed user-oriented selection of validators in terms of computing efficiency due to additional constraints.

In smart home, voice control becomes a main interface between users and smart devices. To make voice control more secure, speaker verification systems have been researched to apply human voice as biometrics to accurately identify a legitimate user and avoid illegal access. In recent studies, however, it has been shown that speaker verification systems are particularly vulnerable to adversarial attacks. In this work, we attempt to design and implement a defense system that is simple, light-weight, and effective against adversarial attacks for speaker verification. Specifically, we study two opposite operations to preprocess input audios in speaker verification systems against adversarial attacks: denoising that attempts to remove or reduce perturbations and noise-adding that adds small Gaussian noises to an input audio. We show through experiments that both methods can significantly degrade the performance of a state-of-the-art adversarial attack. Specifically, it is shown that denoising and noise-adding can reduce the targeted attack success rate of the attack from 100% to only 56% and 5.2%, respectively. Moreover, noise-adding can slow down the attack 25 times in speed and only has a minor effect on the normal operations of a speaker verification system.

With the fast development of information technologies, traditional value-added chain business models are shifting to service-based applications (SBAs) to cope with ever-changing user behaviors and the modern social system. An SBA allows an organization to utilize external and distributed resources to achieve its business goals, especially when the Internet of Thing (IoT) will soon become mainstream. However, the development of SBAs is at its early stage with a number of unsolved issues, such as the availability of effective methods for services selection and composition, semantic representation of specifications, and security assurances. This article aims to address two main issues in SBAs: 1) the standardization of formulation and 2) the security assurance. A systematic method is proposed to formulate the specifications of services, the prevalent blockchain technology (BCT) is adopted to enable SBAs, and the proposed specification patterns and BCT are integrated as a BCT-based quality-of-service (QoS) framework to support service selections and workflow compositions.

Multi-goal navigation of an autonomous mobile robot seeks to reach multiple goals with a planned shortest trajectory in some real-world applications such as search and rescue robots, agricultural harvesting robots, resource prospecting robots and service robots, etc. An improved max-min ant system (MMAS) approach is proposed in this paper for real-time concurrent multi-goal navigation and map building. A global trajectory is planned by the proposed MMAS approach, in which a foraging-enabled trail is generated to guide the robot to the multiple goals. A histogram-based local navigator is utilized to guide the robot to follow the trail planned by the global path planner, with obstacle avoidance. In this paper, simulation results demonstrate that the real-time mapping and multi-goal navigation of an autonomous robot is successfully carried out under unknown environments.

Internet of Things (IoT) refers to networks with billions of physical devices for collecting, sharing, and utilizing data in the virtual world. Most of IoT applications centralize security assurance in creating, authenticating, transferring, or delating system components. However, the centralization exposes its limitations to meet security needs of a rapidly growing number of things world-widely. How to scale up the applications with assured security becomes a critical challenge. Blockchain technology (BCT) is a promising solution to provide security and protect privacy in a large scale; especially, smart contracts offer opportunities to improve the reliability of IoT applications. Smart contracts establish trusts for both of data and executed processes. Recently, many literature surveys and positioning articles have been published on the integration of BCT with IoT, but they are limited to superficial discussions of technical potentials, and very few of them have a thorough exploration of the challenges in developing BCT for IoT at technical levels. This paper uses the system design approach to scrutinize the state of the art of study on BCT-based applications and clarify critical research areas of enabling BCT for security assurance: 1) the relations of BCT and IoT are modeled and discussed; 2) the needs of eliminating threats in IoT-based applications are defined as functional requirements (FRs), existing works on enabling technologies of BCT are defined as the physical solutions (PSs); and 3) the mappings between FRs and PSs are established to identify the limitations and the critical areas for the applications of BCT in large-scale distributed environment.

Internet of Things (IoT) is able to integrate the computation and physical processes as services in the social world. The number of services at the edge of IoT is rising rapidly due to the prevalent uses of smart devices and cyber-physical systems (CPSs). To explore the promising applications of IoT services, one of the challenges is to enable the interoperability of the services in a decentralized environment. The blockchain technology (BCT) has been proven as a promising solution to establish the trust of data and call for executions; theoretically, it can be used to support the interoperability of services. BCT verifies data or a process and stores it as a transaction in a distributed ledger. Similar to the topology to IoT, applying BCT at the edges of the network exhibits the distributed characteristic. However, currently, BCT is still facing the challenges for interoperability due to a number of factors such as consensus protocols, block sizes, and interval of blocks. Prominent protocols such as proof-of-work (PoW) may cause excessive delays in finality settlement. One promising protocol Practical Byzantine Fault Tolerant offers a fast finality settlement and uses hyperledger to support the scalability; however, the trust might also be a concern if the validators are chosen improperly. This paper discusses the interoperability of IoT services and the challenges and proposes an architecture solution by integrating BCT, service-oriented architecture (SoA), and enablers of key performance indicators (KPIs) and service selections. The proposed architecture aims to solve both interoperability and trust issues for IoT services. The feasibility of the proposed method is validated by the examples of smart contract implementations.

Owing to the exponential growth of Internet of Things (IoTs), ensure that the Quality of Service (QoS) over IoT becomes challenges at the network edge or on cloud. The traditional mechanisms for QoS measurements rely on the centralized trusted third parties who use specialized agents to collect data and measure the performances of services. However, these mechanisms are ineffective to deal with highly dynamic and distributed nature of IoT-based services. Moreover, the dynamism of QoS needs to collect, update, and access reliable quality relevant data frequently, while lacking trust becomes a major hurdle for data utilization. It is our argument that the QoS measurement of IoT-based services would be decentralized and the trusts be built from collectively trusted subnetworks. In this paper, we propose to integrate the blockchain technologies (BCT) with a multi-agent approach to warrantee the trustiness of real-time data for the measurement of QoS in the IoT environment. The proposed approach is verified by some demonstrative examples in addressing QoS specification patterns commonly found in service-based applications (SBAs), where qualitative analyses are conducted for the evaluation of the patterns.

In addition to functionalities, business process management (BPM) involves several key indicators such as openness, security, flexibility, and scalability. Optimizing system performance is becoming a great challenge for an ever-increasing large-scale distributed application system in the digital economy on the Internet of Things (IoT) era. In a centralized BPM, many indicators, such as security and openness, or cost and flexibility, are conflicting with each other. For example, inviting new partners across enterprises, domains, and regions to form a service workflow exposes new risks and needs additional security mechanisms for scrutiny; enhancing the flexibility of business workflow compositions increases the cost of security assurance. Blockchain technology (BCT) has thrown the light on the development of vital solutions to various BPM problems. BCT has to be integrated with other BPM system components that often involve IoT devices to implement specified functionalities related to the application. Currently, the potentials of using BCT have been explored although still at an early stage. In this paper, the states of the art are presented to identify emerging research topics, challenges, and promising applications in integrating BCT into the development of BPM.

Service-Oriented Computing (SOC) is changing the way modern information systems that are designed, operated, and evolved. SOC makes possible to aggregate distributed resources at the phases of decision-making support and system operations. When myriads of resources with similar functionalities are available, effective methodologies are demanded to select services and compose them as service workflows for the specified goals. The computation for workflow composition is very complex since it depends on the numbers of services and their dynamic characteristics. Therefore, composing optimized workflows in a timely manner poses a great challenge. We are highly motivated to reduce the complexity of service selection and composition. The formalized semantics in SWSpec is extended so that unqualified or inferior services can be eliminated directly from the scope of the design solution space. In this paper, a brief review of the proposed SWSpec language is given and the focus is on the sematic formalization. A new compositional proof-system is developed with a set of inference rules and the proven system properties. The proposed semantic formalization has its great significance in reducing the complexity of composing workflows and developing efficient algorithms for compliance checking.

Service-oriented computing (SOC) lays the foundation for modern enterprise systems where services are incorporated as building blocks in application workflows. However, SOC is still at development stage with many unsolved issues; particularly, composing services for a workflow pose a significant challenge when some factors including distribution and dynamic characteristics of services are in consideration. The number of vendors providing similar services is increasing, and this requires effective methodologies for service workflow compositions. Service workflow specification (SWSpec) language was proposed to specify requirements of service attributes and verify them in a workflow during service selection processes automatically. One requirement for SWSpec is that all services must be represented for service selections adequately, while existing SWSpec exhibits its limitation in addressing significant specification patterns found in service-based applications (SBAs). This paper proposes a strengthened SWSpec, called real-time monitoring SWSpec that incorporates new modalities to represent missing specification patterns of events occurring in SBAs.

The brain damage could lead to the loss of the central nervous system, so a stroke patient may lose the function of dominating his/her body. The rehabilitation aims to maximize the potential to restore a patient who has an impairment. Traditional rehabilitation is to train a patient's muscles and joints under the guidance of doctors to improve the strength of muscles and restore the motor function of joints. However, stroke patients are usually depressed, lonely, and irritable, and they might easily generate negative emotions during a rehabilitation process. With a sole goal of helping patients restore their body functions from the physiology perspective, the traditional rehabilitation took little consideration on the impact of rehabilitation, which is reflected and measured from the perspective of emotions. Therefore, we suggest adding affective regulation to the stroke rehabilitation; in such a way, the patients' exercise could be completed with high intrinsic motivation, and the performance of the rehabilitation process can be enhanced. Two main contributions in the presented works are: 1) the expanded emotional model to represent the status of stroke patients where the impact of psychological factors can be taken into consideration and 2) the quantifiable measurement of rehabilitation performance as well as the corresponding design of experiments to verify the positive impact of psychological adjustment on human subjects. Note that due to the limited conditions, the experimental verification was performed on healthy college students. Since our work focused on modeling and quantification of psychological factors, it is reasonable to expend our work to other human subjects including stoke patients.

In the assembly modeling of complex products, it is not unusual facing an explosive growth of assembly relations, or called combination explosion. Through integrating object-oriented methods with the knowledge template based modeling, in this paper, a new methodology is proposed to deal with the complexity of the assemblies of large-scale products. As such, the templates with various assembly relations can be defined for complex products; constitutive parts or subassemblies in a product structure are defined as object classes; their features can be encapsulated, and defined or modified freely via the parameters of object classes to meet the requirements of a specific instance. The new method supports reusing knowledge and simplifying assembly modeling of complex products. In this paper, recent literatures on object-oriented methodologies and knowledge template methods for assembly modeling are reviewed, the rationales of object-oriented templates are discussed, and a method is proposed for creating assembly templates for complex products. In the case study included in this paper, the developed method has been applied to digital modeling of aero-engines, thus, validating its performance.

One of the tasks in assembly planning is to create the exploded views for the visualization purpose and the verification of assembly plans. In this paper, we introduce how to generate exploded views automatically from an assembly model. To increase the level of automation, a new relational matrix called extended interference matrix (EIM) is used to represent the assembly relations among constitutive parts. Information in an EIM is used to create the exploded views. An innovative method is developed to generate the exploded views based on the assembly sequences and EIMs. The corresponding Application Programming Interface (API) to the Unigraphics software has been developed as an integration of the algorithm with the CAD software tool. The assembly model of a gear reducer is used as a case study.

Design and operation of a manufacturing enterprise involve numerous types of decision-making at various levels and domains. A complex system has a large number of design variables and decision-making requires real-time data collected from machines, processes, and business environments. Enterprise systems (ESs) are used to support data acquisition, communication, and all decision-making activities. Therefore, information technology (IT) infrastructure for data acquisition and sharing affects the performance of an ES greatly. Our objective is to investigate the impact of emerging Internet of Things (IoT) on ESs in modern manufacturing. To achieve this objective, the evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigms; and the relation of IT infrastructure and ESs is explored to identify the technological gaps in adopting IoT as an IT infrastructure of ESs. The future research directions in this area are discussed.

After the technologies of integrated circuits, personal computers, and the Internet, Internet of Things (IoT) is the latest information technology (IT) that is radically changing business paradigms. However, IoT's influence in the manufacturing sector has yet been fully explored. On the other hand, existing computer-aided software tools are experiencing a bottleneck in dealing with complexity, dynamics, and uncertainties in their applications of modern enterprises. It is argued that the adoption of IoT and cloud computing in enterprise systems (ESs) would overcome the bottleneck. In this paper, the challenges in generating assembly plans of complex products are discussed. IoT and cloud computing are proposed to help a conventional assembly modeling system evolve into an advanced system, which is capable to deal with complexity and changes automatically. To achieve this goal, an assembly modeling system is automated, and the proposed system includes the following innovations: 1) the modularized architecture to make the system robust, reliable, flexible, and expandable; 2) the integrated object-oriented templates to facilitate interfaces and reuses of system components; and 3) the automated algorithms to retrieve relational assembly matrices for assembly planning. Assembly modeling for aircraft engines is used as examples to illustrate the system effectiveness.

Today's manufacturing environments are very dynamic and turbulent. Traditional enterprise information systems (EISs) have mostly been implemented upon hierarchical architectures, which are inflexible to adapt changes and uncertainties promptly. Next-generation EISs must be agile and adaptable to accommodate changes without significant time delays. It is essential for an EIS to obtain real-time data from the distributed and dynamic manufacturing environment for decision making. Wireless sensor networks (WSNs) and radio-frequency identification (RFID) systems provide an excellent infrastructure for data acquisition, distribution, and processing. In this paper, some key challenges related to the integration of WSN and RFID technologies are discussed. A five-layer system architecture has been proposed to achieve synergistic performance. For the integration of WSN and RFID, one of the critical issues is the low efficiency of communication due to redundant data as redundant data increases energy consumption and causes time delay. To address it, an improved data cleaning algorithm has been proposed; its feasibility and effectiveness have been verified via simulation and a comparison with a published algorithm. To illustrate the capacity of the developed architecture and new data cleaning algorithm, their application in relief supplies storage management has been discussed.

Numerous software architecture proposals are available to industrial information engineers in developing their enterprise information systems. While those proposals and corresponding methodologies are helpful to engineers in determining appropriate architecture, the systematic methods for the evaluation of software architecture are scarce. To select appropriate software architecture from various alternatives appropriately, a scenario-based method has been proposed to assess how software architecture affects the fulfillment of business requirements. The empirical evaluation on the selection of a supply chain software tool has shown that the developed method offers remarkable insights of software development and can be incorporated into the industrial informatics practice of an organization with a moderate cost.

Despite the rapid advance of information technology (IT), a fully automated Enterprise Information System (EIS) for designing and planning of complex products and assemblies is still not available, and human interventions are required in one way or another. Therefore, one of the challenges in developing an EIS for complex multidisciplinary design is how to design human-machine interfaces and integrate experts' knowledge in decision-making. In this paper, a rule-based methodology is proposed to take into consideration experts' experience; the rules are applied based on human designers' visual perception. The strength of IT and human experts can be synthesized to find a suboptimized solution efficiently. To illustrate the performance of the developed methodology, an EIS for aero-engine pipe routing is used as a case study. Pipe routing is a typical no-deterministic polynomial-time hard (NP-hard) problem; in addition, many factors, such as fluid dynamics, sequences of pipes, installation, maintenance, machinability, stability, and vibration, have to be taken into consideration simultaneously. Experts' guidance is essential to find a vital solution in the confined 3-D space. In the implementation, virtual objects have been determined to define feasible spaces for pipes based on the analysis of fluid structure interaction. A new algorithm is developed to find the possible graphs of pipes routes and improve the solutions with the help of human visual perception. The effectiveness of the proposed methodology has been verified.

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.

To automate assembly planning for complex products such as aircraft components, an assembly planning and simulation system called AutoAssem has been developed. In this paper, its system architecture is presented; the main components and the key technologies in each component are discussed. The core functions of the system that have been focused include Digital Assembly Modeling, Assembly Sequence Planning (ASP), Path Planning, Visualization, and Simulation. In contrast to existing assembly planning systems, one of the novelties of the system is it allows the assembly plans be automatically generated from a CAD assembly model with minimal manual interventions. Within the system, new methodologies have been developed to: (i) create Assembly Relationship Matrices; (ii) plan assembly sequences; (iii) generate assembly paths; and (iv) visualize and simulate assembly plans. To illustrate the application of the system, the assembly of a worm gear reducer is used as an example throughout this paper for demonstration purpose. AutoAssem has been successfully applied to virtual assembly design for various complex products so far.

Coating automation of a complex product family with changes and uncertainties is investigated. A CAD- and scanner-based robotic coating system is proposed. Its software system consists of a data-processing system and automatic programming and simulation system. The data processing system is capable of generating accurate surface model from points-cloud and/or as-designed CAD model. The automatic programming and simulation system is capable of creating robot programs based on the surface models. In this paper, the system framework and the methodology for generating robot programs are focused

This research treats a design for reconfigurable robotic systems with large variations in configurations. To evaluate system adaptability, we define the configuration space to be the set of all feasible configuration variations of the robotic system. We define the volume of the configuration space expressed in terms of physical structures to be a measure of system reconfigurability and the volume of the configuration space expressed in terms of Denevit-Hartenberg notation to be a measure of system adaptability. We develop an evaluation method to determine the architecture of reconfigurable robotic systems with high adaptability. A case study is presented to demonstrate that a reconfigurable robotic system with high reconfigurability may not have high adaptability. Finally, we describe how to achieve task-oriented configuration design of reconfigurable robotic systems.

In comparison with dedicated robot systems, the main goal of modular robot systems is to achieve system adaptability by providing various modular configuration variations to meet possible task requirements. System architecture determines the system configuration variations, as the architecture specifies primary building blocks and the types of ways they are connected. In order to make systems more adaptive, configuration variations are expected to be as many as possible subject to the constraints of machining manufacturing applications. This paper reported briefly our recent research progresses on modular robot system architecture, including: (1) a proposed strategy to achieve high system adaptability; (2) a systematic method to develop modular system architecture; (3) the general representation of modular robot configurations.

A quasi-ellipsoidal gear drive is suitable for the precise flexible joint of robots, but the meshing problems of this transmission are rarely being considered in the related papers. Based on the two-parameter conjugate surface theory, this article studies the critical curves, the induction normal curvature and the short-rang torsion. The method of analysis is efficient for not only the quasi-ellipsoidal gear drive but also the other type of spatial gear drive.