Dynamic Industrial Environment Research Articles

Autonomous Robot Task Execution in Flexible Manufacturing: Integrating PDDL and Behavior Trees in ARIAC 2023.

The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexible manufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamic industrial environments. ARIAC 2023 introduced eight agility challenges involving faulty parts, flipped parts, faulty grippers, robot malfunctions, sensor blackouts, high-priority orders, insufficient parts, and human safety. Given the unpredictability of these scenarios, it is impractical to develop a specific strategy for each possible situation. To address these issues, this paper presents a hierarchical framework for autonomous robotic task generation and execution in dynamic scenarios. The framework is divided into a task level and an execution level. Initially, an immediate task management strategy is adopted at the task level, which reasonably decomposes dynamic tasks and allocates short-term tasks to the floor robot and ceiling robot. Later, at the execution level, each robot is designed with an agent architecture that combines PDDL planning with the quick response of behavior trees. Finally, the effectiveness and practicality of the proposed framework were thoroughly validated in ARIAC 2023.

Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization

In recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods.

A Novel Energy-Efficient Routing Protocol for Industrial WSN Using Hybrid Coot-Ls Algorithm with LSTM-Based Dom Prediction

In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot- Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments.

A resource dependence perspective on the business group effect in developed markets: Evidence from Western European firms

AbstractRecent research shows that business groups matter in developed institutional settings. However, little is known about why business groups influence affiliate performance in such environments. Using a resource dependence lens, we explain how the business group effect may be contingent on a firm's industry environment. Consistent with a resource dependence perspective, our variance decomposition analysis of 3,733 firms in Western Europe (2006–2021) shows that the business group effect is significantly larger in less munificent and more complex industry environments. However, contrary to this perspective, our results also reveal that business groups matter more in less dynamic industry environments. Overall, by examining the importance of the industry environment in explaining how much business groups matter, this paper contributes to a more nuanced understanding of the business group effect in developed markets and provides several directions for future research in this domain.

LoRa-Based Indoor Positioning in Dynamic Industrial Environments Using Deep Gaussian Process Regression and Temporal-Based Enhancements

LoRa-Based Indoor Positioning in Dynamic Industrial Environments Using Deep Gaussian Process Regression and Temporal-Based Enhancements

A security architecture for Metallurgical and Heating Processes (Metallurgical furnace)

This paper presents a novel network security architecture tailored for non-conventional processes, addressing the growing need for robust security solutions in diverse and dynamic industrial environments. Drawing upon insights from existing literature and methodologies, the architecture is designed to accommodate the unique requirements and constraints of non-traditional systems, ensuring resilience and adaptability in the face of evolving threats. Key components of the proposed architecture include multi-layered defense mechanisms, anomaly detection systems, and dynamic access controls, all integrated within a cohesive framework to provide comprehensive protection

Investigating entrepreneurial resilience toward sustainable competitive advantage: Does local culture matter?

Literature indicates it is crucial to understand how SMEs could adapt to changes in their external environment to develop a competitive advantage. However, there is limited discussion on how entrepreneurs’ resilience functions as one of the dynamic capabilities required to maintain their company’s competitive advantage in the long-term. This study aimed to investigate how entrepreneurial resilience affects SMEs ability to achieve sustainable competitive advantage. It also analyzed the moderating role of local cultural values in reinforcing resilience and sustainable competitive advantage. The population consisted of 400 handicraft SME who were thought of as Bali’s sustaining tourism sector. The results demonstrated that financial capital, human capital, and social capital were factors that shaped entrepreneurial resilience, where the ownership of capital and access to capital helped entrepreneurs when encountering changes in the dynamic and challenging industrial environment, where the role of financial capital was one of the sources of resilience that had the most significant influence. Furthermore, entrepreneurial resilience also contributed significantly to the achievement of sustainable competitive advantage. Moreover, the results also indicate that local cultural values strengthen entrepreneurial resilience because entrepreneurs who adjusted to local cultural norms developed in the surrounding community tended to respond positively to their behavior. Positive environmental acceptance could reinforce entrepreneurial resilience, given the psychological support of their behavior.

R&D location in dynamic industry environments

Abstract We study firms’ optimal R&D location strategies in a dynamic industry model with competition in product quality. In light of potential future inwards and outwards spillovers firms make their location choices relying on heuristic strategies that are based on the expected present values associated with alternative location patterns. Using a simulation analysis, we show how the strategies of innovators and imitators differ and how they depend on whether firms operate in strongly or weakly innovative industry environments. We also characterize how firms’ location choices should account for the innovativeness of the competitors active in a location.

Studi Pola Aliran Distribusi Pemasaran pada Industri Konveksi Lokal di Desa Ledug, Kecamatan Kembaran, Kabupaten Banyumas

This study carries an analysis related to the pattern of marketing distribution flow of the convection industry in Ledug Village through various levels of marketing channels. Three convection industries, namely Enjoy Apparel, Kayla Jaya Convection, and N Convection, are the main focus in this study. The data collected during the period of 2020 to 2023 becomes material for analysis to understand marketing strategies and adaptations made by the three actors in facing changes in market dynamics. The results of the analysis revealed significant differences in marketing strategy and performance among the three industry players. In zero and single-tier marketing channels, Enjoy Apparel was able to cement impressive distribution growth, reflecting responsive adaptation to fluctuations in domestic and international market demand. On the other hand, Kayla Jaya Convection and N Convection experience more complex performance variations, with repeated periods of increases and decreases. A deeper analysis of level two distribution channels reveals a more detailed shift in marketing strategy. While Enjoy Apparel continues to show steady distribution improvements, Kayla Jaya Convection faces a sharper decline, while N Convection remains at a steady level of performance. In this context, strategic adaptation and the ability to respond to market changes are emerging as decisive factors for maintaining competitiveness in a dynamic industrial environment.

Fog Node Planning With Stochastic Sensor Traffic in Dynamic Industrial Environment

The emergence of Industrial Internet of Things (IIoT) along with Fog Computing (FC) has brought great benefits in the industry field through real-time monitoring, resource optimization configuration and intelligent cloud control. The deployment of fog nodes in industrial plants is the essential precondition for FC implementation. Existing works are mainly based on pre-given sensor traffic in the deployment of fog nodes, which largely ignores the stochastic uncertainty imposed by the dynamic industrial environment. In this paper, without requiring a priori knowledge of sensor traffic pattern, we establish a mathematical model based on uncertain programming to formulate the fog nodes location determination and the sensor association problem. Due to the complexity of the model, we introduce a learning-based algorithm to effectively solve the problem with an acceleration mechanism. Finally, intensive simulations are implemented to verify the performance of the algorithm. The results indicate that our approach outperforms other benchmarks in terms of transmission energy on planning fog nodes in industrial plants with stochastic sensor traffic.

Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks

Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.

The effect of cognitive workload on decision authority assignment in human–robot collaboration

Human–robot collaboration in dynamic industrial environments warrants robot flexibility and shifting between tasks. Adaptive robot behavior unavoidably carries decision-making needs regarding task allocation and scheduling. Such decisions can be made either by the human team members or autonomously, by the robot’s controlling algorithm. Human authority may help preserve situational awareness but increases mental demands due to increased responsibilities. Conversely, granting authority to the robot can offload the operator, at the cost of reduced intervention readiness. This paper aims to investigate the question of decision authority assignment in a human–robot team, in terms of performance, perceived workload and subjective preference. We hypothesized that the answer is influenced by the cognitive workload imposed on the human operator by the work process. An experiment with 21 participants was conducted, in which decision authority and induced workload through a secondary task were varied between trials. Results confirmed that operators can support the robot better when decision authority is allocated according to their workload. However, operator decision authority (a) may cause inferior performance at any secondary tasks performed in parallel with robot supervision and (b) increases perceived workload. Subjective preference was found to be evenly divided between the two levels of decision authority, and unaffected by task difficulty. In brief, if human–robot team performance is a priority, humans should be granted decision authority when their overall workload allows it. In high-workload conditions, system decision-making algorithms should be developed. Nonetheless, process designers should be mindful of the interpersonal differences between operators who are destined to collaborate with robots.

Collaborative clustering parallel reinforcement learning for edge-cloud digital twins manufacturing system

To realize high-accuracy physical-cyber digital twin (DT) mapping in a manufacturing system, a huge amount of data need to be collected and analyzed in real-time. Traditional DTs systems are deployed in cloud or edge servers independently, whilst it is hard to apply in real production systems due to the high interaction or execution delay. This results in a low consistency in the temporal dimension of the physical-cyber model. In this work, we propose a novel efficient edge-cloud DT manufacturing system, which is inspired by resource scheduling technology. Specifically, an edge-cloud collaborative DTs system deployment architecture is first constructed. Then, deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications. We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning (CCPQL) algorithm and prediction-based CCPQL to solve the problems. The proposed approach reduces the total delay with a higher convergence rate. Numerical simulation results are provided to validate the approach, which would have great potential in dynamic and complex industrial internet environments.

ML-Assisted Beam Selection via Digital Twins for Time-Sensitive Industrial IoT

In this article, we propose a machine learning (ML)-assisted beam selection framework that leverages the availability of digital twins to reduce beam training overheads and thus facilitate the efficient operation of time-sensitive IoT applications in dynamic industrial environments. Our approach employs a digital twin of the environment to create an accurate map-based channel model and train a beam predictor that narrows the beam search space to a set of candidate configurations. To verify the proposed concept, we perform shooting-and-bouncing ray modeling for a reconstructed 3D model of an industrial vehicle calibrated using the real-world millimeter-wave propagation data collected during a measurement campaign. We confirm that lightweight ML models are capable of predicting the optimal beam configuration while enjoying a considerably smaller size compared to the map-based channel model.

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized and a parallel deep reinforcement learning approach is presented. We consider an assembly line control problem in which a set of tasks (e.g., vehicle assembly tasks) needs to be planned and controlled during their execution, with the aim of optimizing given key performance criteria. Specifically, the aim will be that of planning the task in order to minimize the total time taken to execute all the tasks (also called cycle time). Tasks run on workstations in the assembly line. To run, tasks need specific resources. Therefore, the tackled problem is that of optimally mapping tasks and resources to workstations, and deciding the optimal execution times of the tasks. In doing so, several constraints need to be respected (e.g., precedence constraints among the tasks, constraints on needed resources to run tasks, deadlines, etc.). The proposed approach uses deep reinforcement learning to learn a tasks/resources mapping policy that is effective in minimizing the resulting cycle time. The proposed method allows us to explicitly take into account all the constraints, and, once training is complete, can be used in real time to dynamically control the execution of tasks. Another motivation for the proposed work is in the ability of the used method to also work in complex scenarios, and in the presence of uncertainties. As a matter of fact, the use of deep neural networks allows for learning the model of the assembly line problem, in contrast with, e.g., optimization-based techniques, which require explicitly writing all the equations of the model of the problem. In order to speed up the training phase, we adopt a learning scheme in which more agents are trained in parallel. Simulations show that the proposed method can provide effective real-time decision support to industrial operators for scheduling and rescheduling activities, achieving the goal of minimizing the total tasks’ execution time.

Do Slack Resources Promote or Constrain Firm Performance in Munificent, Dynamic, and Complex Industrial Environments?

Do Slack Resources Promote or Constrain Firm Performance in Munificent, Dynamic, and Complex Industrial Environments?

Avoiding Covid-19 Using a 3D Digital Mock Up and Augmented Reality with Cobot in Digital Factory

The Fourth Industrial Revolution or Industry 4.0 is extremely relevant and important in manufacturing for several reasons. Failing to adopt the technology of the Fourth Industrial Revolution can cause organizations to fall behind, as their operations are not digitized enough in benchmarking competitors. A major industry as bicycle manufacturing needs to be transformed into a digital factory in order to keep up with the evolving technology. The objectives of this research are in designing a 3D digital mock up and Augmented Reality (AR) for bicycle frame production in the form of digital factory for increasing production capability by decreasing the break-even point whilst avoiding Covid-19 contraction using Tecnomatix Plant Simulation and Unity 3D programs. This research constructed three alternative digital factory layouts that are designed by using a 3D simulation program. The risk of Covid-19 contraction points and the three layouts of cost per unit are analyzed. The results show that the break-even point of the first to the third layouts with Automated Guided Vehicle (AGV) are 26,580, 26,322 and 25,354 units respectively. The result of the risk about Covid-19 contraction points from the first to the third layout with AGV are 21,272, 2,872 and 0 points respectively. This means the most appropriate layout for bicycle frame production is the third layout due to the best break-even point that can significantly avoid Covid-19 contraction. The benefit of this research is to integrate a 3D digital mock up, AR and Cobot including AGV to assist the resilience of operations with a dynamic industrial environment.

Topological Navigation and Localization in Tunnels—Application to Autonomous Load-Haul-Dump Vehicles Operating in Underground Mines

Mobile robots are no longer used exclusively in research laboratories and indoor controlled environments, but are now also used in dynamic industrial environments, including outdoor sites. Mining is one industry where robots and autonomous vehicles are increasingly used to increase the safety of the workers, as well as to augment the productivity, efficiency, and predictability of the processes. Since autonomous vehicles navigate inside tunnels in underground mines, this kind of navigation has different precision requirements than navigating in an open environment. When driving inside tunnels, it is not relevant to have accurate self-localization, but it is necessary for autonomous vehicles to be able to move safely through the tunnel and to make appropriate decisions at its intersections and access points in the tunnel. To address these needs, a topological navigation system for mining vehicles operating in tunnels is proposed and validated in this paper. This system was specially designed to be used by Load-Haul-Dump (LHD) vehicles, also known as scoop trams, operating in underground mines. In addition, a localization system, specifically designed to be used with the topological navigation system and its associated topological map, is also proposed. The proposed topological navigation and localization systems were validated using a commercial LHD during several months at a copper sub-level stoping mine located in the Coquimbo Region in the northern part of Chile. An important aspect to be addressed when working with heavy-duty machinery, such as LHDs, is the way in which automation systems are developed and tested. For this reason, the development and testing methodology, which includes the use of simulators, scale-models of LHDs, validation, and testing using a commercial LHD in test-fields, and its final validation in a mine, are described.

Exploring the need for a new paradigm in engineering management and the decision-making process in technology-based organisations

Abstract Engineering management and engineering projects are subject to greater levels of uncertainty and complexity as part of the current dynamic and competitive industrial environment. Engineering managers need to navigate the arising challenges and consequently gain access to effective decision-making processes. Engineering education has a clear role to play here. However, formal education in quantitative methods is only part of the solution — engineers and engineering managers should also have access to a broader set of skills and knowledge to be effective in the industrial landscape. Therefore, we now need a new paradigm for engineering management and the decision-making process. This article draws on supporting material from the literature and the insights gained from a series of industrial cases using the participatory action research method and a process of inductive reasoning to allow synthesis of generalised propositions that are linked to the industrial cases and antecedent factors from the literature. The findings lead to a set of areas that require further development to support engineering managers to be more effective when dealing with increasing levels of uncertainty and complexity. This includes a number of areas, which are as follow: the need for engineering managers to have enhanced professional skills and knowledge; the importance of experience-based judgement; effective knowledge management; supportive leadership and overall organisational culture; and a holistic approach to decision-making. The research study has practical relevance to engineering management practitioners working in industrial companies to support self-evaluation and professional development. The findings are also pertinent to academic researchers seeking to evaluate decision-making models as part of extending the current understanding of the field of engineering management in technology-based organisations.

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations from the actual value of device state, a trusted-based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning (DRL), to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.