Reconfigurable manufacturing systems must increasingly deal with rapid design changes, mass customisation requirements, and operational disturbances such as demand volatility, resource shortages, and equipment failures. Traditional decision support methods, grounded in expert-driven heuristics or static weighting schemes, struggle to balance multiple, often conflicting, criteria in real time. To overcome these limitations, this paper introduces SRM-OS, a fully integrated framework that employs semantic ontologies and multicriteria decision making (MCDM) to automate process reconfiguration with minimal human intervention. In SRM-OS, knowledge about products, processes, resources, and decision rules is formalised into interoperable ontologies. At the same time, anMCDMengine generates, evaluates, and ranks alternative configurations against dynamic performance metrics, including quality, availability, cost, and lead time, and deploys the chosen configuration on shop floor equipment. A semantic aggregation layer reconciles heterogeneous data from IIoT sensors, control systems, and external stakeholders, enabling the precise detection of disturbances and the rapid generation of viable solutions. We validate SRM-OS on a laboratory assembly line at PUCPR featuring robotic arms, an automated storage/retrieval system, and a multidirectional conveyor. When a conveyor segment is disabled, SRM-OS identifies and ranks new material flow routes, reducing downtime and improving overall equipment effectiveness. By bridging semantic interoperability and MCDM in a scalable ontology-driven decision support tool, SRM-OS enhances autonomy, resilience, and efficiency in Industry 4.0 environments, laying the groundwork for future extensions that incorporate predictive analytics for proactive self-optimising production.

The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators’ well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators’ cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators’ mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators’ level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

Sector 4.0 technologies, such as cloud computing, big data, the internet of things, and cyber-physical systems, are transforming the manufacturing industry significantly. Companies must concurrently address consumer expectations and production capacities, resulting in an increase in system requirements. This growth in requirements necessitates the ongoing modification of the production characteristics' structures, culminating in the development of Smart Manufacturing Systems that enhance the client value proposition. As market needs change over time, the system's capabilities must adjust to these changes, which may lead to issues such as an increase in production time, cost, and a decline in the quality of the final product. To tackle this problem and help in the decision-making process, assessment techniques may be used to examine and offer several alternatives depending on the current capabilities and needs. In addition, these models may be adopted and linked with ontological techniques since they can effectively represent and communicate information across multiple systems and domains, hence enhancing the accuracy and efficacy of decision-making. In this context, the primary objective of this study is to build a thorough literature mapping of current research on the Smart Manufacturing Systems decision-making process.

The manufacturing industry is undergoing a major transformation based on the emerging industry 4.0 technologies, such as cloud computing, big data, internet of things and cyber-physical systems. These novelty technologies aim at providing central management for the user’s flexible manufacturing requirements and information. Also, the advent of these technologies has transformed the process planning and became crucial for the building of knowledge-based process planning environments. However, current praxis cannot deal with all semantic issues within this new paradigm, as requirements must be clear, consistent, measurable, stand-alone, testable, unambiguous, unique and verifiable. In this context, multicriteria decision analysis models have gained focus of the scientific and industrial communities as a support tool for the decision-making process in the product development and advanced manufacturing as these processes excel in environments with numerous and conflicting alternatives, providing the optimal alternative. Therefore, the main objective of this research is to highlight the current issues and research tendencies regarding ontology-based interoperability systems, multicriteria decision analysis and their integration. To achieve this goal, it will be applied a literature review on the targeted technologies, discussing the current tendencies of the field and the main issues regarding their implementation and integration. Finally, the paper points themes for further research and indicates viable concepts that can compose a solution for the gaps in a systematic manner.

Most information sources in the current technological world are generating data sequentially and rapidly, in the form of data streams. The evolving nature of processes may often cause changes in data distribution, also known as concept drift, which is difficult to detect and causes loss of accuracy in supervised learning algorithms. As a consequence, online machine learning algorithms that are able to update actively according to possible changes in the data distribution are required. Although many strategies have been developed to tackle this problem, most of them are designed for classification problems. Therefore, in the domain of regression problems, there is a need for the development of accurate algorithms with dynamic updating mechanisms that can operate in a computational time compatible with today’s demanding market. In this article, the authors propose a new bagging ensemble approach based on neural network with random weights for online data stream regression. The proposed method improves the data prediction accuracy as well as minimises the required computational time compared to a recent algorithm for online data stream regression from literature. The experiments are carried out using four synthetic datasets to evaluate the algorithm’s response to concept drift, along with four benchmark datasets from different industries. The results indicate improvement in data prediction accuracy, effectiveness in handling concept drift, and much faster updating times compared to the existing available approach. Additionally, the use of design of experiments as an effective tool for hyperparameter tuning is demonstrated.

Although many manual operations have been replaced by automation in the manufacturing domain in various industries, skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realize the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realize the potential. This article describes a taxonomy of variability when considering the automation of manufacturing processes. Three industrial case studies were analyzed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making. ARTICLE HISTORY Received 3 September 2018 Accepted 22 June 2019

Although many manual operations have been replaced by automation in the manufacturing domain in various industries, skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realize the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realize the potential. This article describes a taxonomy of variability when considering the automation of manufacturing processes. Three industrial case studies were analyzed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making.

Currently, changes in the customer’s mindset and increased flexibility in manufacturing shifted competition between companies to focus on the product’s value rather than its cost. In this context, Product-Service Systems (PSSs) are an alternative to traditional value propositions (i.e. pure products, pure services) by combining services with products and providing an experience to customers. In recent decades, PSSs were expanded from solely business aspects to engineering aspects and technical considerations in their scope, such as the paradigms of Technical PSS, Industrial PSS, Digitalized PSS and Cyber-Physical PSS. More recently, due to the application of Industry 4.0 concepts, there is a significant increase of “smart technologies”, i.e. sensors; connectivity; mobile application, in PSSs, naming it a new paradigm on the field, known as Smart PSSs. In this context, this research aims to explore and discuss current issues related to the integration of flexible manufacturing processes and Smart PSSs through a literature review and analysis, regarding information sharing, traceability and system reconfiguration based on customer’s requirements. Found literature points to gaps in the theme related to semantic issues and design issues regarding the dynamic reconfiguration of the manufacturing process and the Smart PSS. The modern approaches to the theme, as found in literature, are still not able to cope with the semantic barriers and high volume of heterogeneous information in a holistic manner. Conclusions point that further investigation of the cited issues is required in a systematic manner, as well as the development of models that can cope with the presented issues.

In Industry 4.0, trends on Servitisation are intertwined with increased use of “smart technologies”, bringing light to Smart Product-Service Systems. Currently, Smart-PSSs need to cope with dynamic customer’s needs, which generates system requirements in a higher frequency, resulting in reconfiguration of such system. This research aims to study current issues regarding the reconfiguration of Smart-PSSs, focusing on flexibilization. An exploration of issues is done by a qualitative literature review, with results structured in a schema. This schema approaches issues which are not yet solved or addressed in literature, showing that systemic solutions are necessary to solve explored issues. A discussion of current limitations and trends in research is presented, bridging studied material and pointing out current Industry 4.0 technologies that might cope with the issues if approached systemically. The paper points to further research regarding a expansions on the literature review and next steps on solving current issues.

Robotic applications are commonly used in industrial automation systems. Such systems are often comprised of a series of equipment, including robotic arms, conveyors, a workspace, and fixtures. While each piece of equipment may be calibrated with the highest precision, their alignment in relation to each other is an important issue in defining the accuracy of the system. Currently, a variety of complex automated and manual methods are used to align a robotic arm to a workspace. These methods often use either expensive equipment or are slow and skill-dependent. This paper presents a novel low-cost method for aligning an industrial robot to its workcell at 6 degrees of freedom (DoF). The solution is new, simple and easy to use and intended for the SMEs dealing with low volume, high complexity automated systems. The proposed method uses three dial indicators mounted to a robot end effector and a fixed measurement cube, positioned on a workcell. The robot is pre-programmed for a procedure around the cube. The changes on the dial indicators are used to calculate the misalignment between the robot and the workcell. Despite simplicity of the design, the solution is supported with complex real-time mathematical calculations and proven to identify and eliminate misalignment up to 3 mm and 5 degrees to an accuracy of 0.003 mm and 0.002 degrees: much higher than the precision required for a conventional industrial robot. In this article, the authors describe a proposed solution, validate the computation both theoretically and through a laboratory test rig and simulation.

Over the past two decades, a major part of the manufacturing and assembly market has been driven by the increasing demand for customised products. This has created the need for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products—or to assemble different parts—with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In high-volume production, fastening operations are commonly automated using jigs, fixtures, and semi-automated tools. However, in low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This motivated the development of a flexible solution, which does not require fixtures and is adaptable to variation in part locations and lighting conditions. As a part of this research, a novel 3D threaded hole detection and a fast bolt detection algorithms are proposed and reported in this article, which offer substantial enhancement to the accuracy, repeatability, and the speed of the processes in comparison with the existing methods. Hence, the proposed method is more suitable for industrial applications. The development of an automated bolt fastening demonstrator is also described in this article to test and validate the proposed identification algorithms on complex components located in 3D space.

Manufacturing companies need greater capabilities to respond quicker to market dynamics and varying demands. Paradigms such as mass customization, global manufacturing operations and competition provide a platform to meet these needs. Therefore a continuous restructuring and re-engineering of the processes is seen in the manufacturing industries. This is extremely important in cases when automated machines are used in production. Automotive industry is an example of having intensive use of automated processes. During the reengineering of the processes it must be focused to control the factors which add cost during the processes. Energy is one of the important parameter which acts continuously over the process and increases the product price. Therefore, this paper proposes to validate the processes for energy optimization during the design stages well before, to physically build a machine. This could be done by using virtual environment and discrete event simulation integration. The pilot study of an ongoing research has been carried out to identify the level of energy consumption in a case study along with the identification of information to be used in the virtual tool prior to build the models. The adopted approach would propose to identify the processes to keep them off if consuming energy even in idle states. It could be identify through simulation that which one is the energy intensive process when they are idle, and then try it for the option, to keep it off when not working. Utilizing less energy in production helps society to have low cost products as well as to maintain the sustainable resources over a long period of time.

Industrial robots arms are widely used in manufacturing industry because of their support for automation. However, in metrology, robots have had limited application due to their insufficient accuracy. Even using error compensation and calibration methods, robots are not effective for micrometre (μ m) level metrology. Non-contact measurement devices can potentially enable the use of robots for highly accurate metrology. However, the use of such devices on robots has not been investigated. The research work reported in this paper explores the use of different non-contact measurement devices on an industrial robot. The aim is to experimentally investigate the effects of robot movements on the accuracy and precision of measurements. The focus has been on assessing the ability to accurately measure various geometric and surface parameters of holes despite the inherent inaccuracies of industrial robot. This involves the measurement of diameter, roundness and surface roughness. The study also includes scanning of holes for measuring internal features such as start and end point of a taper. Two different non-contact measurement devices based on different technologies are investigated. Furthermore, effects of eccentricity, vibrations and thermal variations are also assessed. The research contributes towards the use of robots for highly accurate and precise robotic metrology.

Manual forming of sheet metal parts through traditional panel beating is a highly skilled profession used in many industries, particularly for sample manufacturing or repair and maintenance. However, this skill is becoming gradually isolated mainly due to the high cost and lack of expertise. Nonetheless, a cost-effective and flexible approach to forming sheet metal parts could significantly assist various industries by providing a method for fast prototyping sheet metal parts. The development of a new fixtureless sheet metal forming approach is discussed in this article. The proposed approach, named Mechatroforming®, consists of integrated mechanisms to manipulate sheet metal parts by a robotic arm under a controlled hammering tool. The method includes mechatronics-based monitoring and control systems for (near) real-time prediction and control of incremental deformations of parts. This article includes description of the proposed approach, the theoretical and modelling backgrounds used to predict the forming, skills learned from manual operations, and proposed automation system being built.

Virtual engineering (VE) environment helps to verify process and resource design through visualisation. By using VE, the impacts of re-configurability and new-process additions in the machine stops can be viewed down to the component level. On the other hand, discrete event simulation (DES) typically forecasts the system behaviour over a period of time to predict future performance. During pre-build stages of machines, DES analysis comes with uncertainties, as most of the parameters in the model are based on the assumptions. Therefore, it was aimed to use the validated and verified data, for example ‘process time’ of a machine component available from the VE-emulated systems, in the DES model. Thus, a systematic algorithm was proposed to integrate the VE tool data, with the DES. This article presents the development of a package known as ‘virtual-driven discrete event simulation’ (VDSim), used to establish an integration between the VE and DES domains. The success of this integration depends upon the quality of information and the compatibility of data flow between these independent domains. VDSim integration will help productivity planners and schedulers to get the best possible options for resource selection at stages even when the resource is not physically present.

Increasing the cost of electricity and the global obligations for efficient use of energy have added additional pressure to industrial companies in an already challenging market. Manufacturing companies are adopting methods to have a greater agility to respond quicker to the market dynamics and varying demands by changing production configurations. In recent years, deploying virtual engineering design approaches and extensive simulation methods have facilitated an early insight into how a system may perform, in advance of the physical build. However, the impact of system reconfiguration on the cost of energy consumption is typically unknown. Much research has been carried out on the development of new energy-efficient drives (e.g. motors and actuators) and also the possibility of turning off the drives when idle to save energy. However, in this article, the authors propose a new method of energy saving for engineering production lines by fine tuning the low level device motions to optimise energy consumption. An integration method between virtual engineering design and simulation modelling is proposed, and as a result a simulation method for energy optimisation is developed. In this article the method of interpreting virtual design data for use in simulation modelling of a production assembly line in an automotive industry is discussed. Furthermore, a developed algorithm for optimising energy usage based on adjusting dynamic properties of the system components (e.g. accelerations, torque and mass) is discussed and the result of implementing the concept in an experimental application in the powertrain industry is reported.

Manufacturing companies need greater capabilities to respond quicker to market dynamics and varying demands. New paradigms such as mass customization, global manufacturing operations and competition provide this platform to meet these needs. Therefore manufacturing enterprises have a continuous effort to restructure and re engineer their business in a response to meet the 21st century challenges. To face these challenges management decisions about the production operations need to facilitate the products life cycle dynamics and variances, including the product cycle times, resource allocation, supply pace and production cost in term of resources and energy utilization. Every related aspect of the production operations needs a careful concentration. However one of the important focused areas for almost all of the industries is the energy usage and its control. Therefore this paper proposes a conceptual approach to minimize the energy consumption during production with an integrated monitoring system. The paper also supports the strategy in the early validation of the processes for the flexible, reconfigurable production environment, which is necessary to analyze energy consumption. Utilizing less energy in production helps society to have low cost products as well as to maintain the sustainable resources over a long period of time.

Pressure on the powertrain sector of the automotive industry is mounting as market demand for higher variety and lower-cost automation systems increases. To maintain the market competitiveness, design-to-market time for new products should be significantly shorter and considerable cost saving needs to be made during the design and manufacture of production facilities. Virtual construction, test and validation of systems prior to build are now identified as crucial because engineering changes owing to untested designs cannot be afforded any longer, and approved designs need to be reused more efficiently. In this article, the authors report research collaboration between Loughborough University and Ford Motor Company, to improve the current business and engineering model used in the powertrain industry. The current problems are highlighted and corresponding industrial engineering requirements are specified. The existing end-user and supply-chain interaction models are captured and new business and engineering interaction models are proposed to address the requirements. A set of engineering services required for the new interaction models is described and an evaluation approach to identify the impact of the new model on the current enterprises is explained. In addition, an overview is given on the research findings on the predicted impacts on the current businesses based on a set of evaluation criteria.

Autonomous and intelligent control devices within the context of factory automation are seen as an essential ingredient in making time and cost savings in factory automation environments. In moving to mass customisation scenarios where production lines are subjected to frequent changes and mixed types of products, the agility and reconfigurability of automation systems are prime requirements to support changes in manufacturing lifecycles. In addition, intelligent functionalities including process monitoring, diagnostics and process reconfiguration are also desirable factors to facilitate an effective production unit with competitive costs and ease of use and maintenance. In this context, the adoption of Web Services on the distributed embedded control devices to enhance reconfigurability and integrability with supported manufacturing and business applications is proposed. This paper demonstrates the use of Web Services (WS) both in building device control functionality of control components and in business application integration. This WS approach offers the ability to integrate pervasive enterprise applications (e.g. process monitoring and planning systems) as well as the ability to reconfigure and manage lower level devices from higher manufacturing and business control levels through unifying WS interface and neutral Simple Object Access Protocol (SOAP) message communication between control systems and business applications.

The prediction and capturing of defects in low-volume assembly of electronics is a technical challenge that is a prerequisite for design for manufacturing (DfM) and business process improvement (BPI) to increase first-time yields and reduce production costs. Failures at the component-level (component defects) and system-level (such as defects in design and manufacturing) have not been incorporated in combined prediction models. BPI efforts should have predictive capability while supporting flexible production and changes in business models. This research was aimed at the integration of enterprise modelling (EM) and failure models (FM) to support business decision making by predicting system-level defects. An enhanced business modelling approach which provides a set of accessible failure models at a given business process level is presented in this article. This model-driven approach allows the evaluation of product and process performance and hence feedback to design and manufacturing activities hence improving first-time yield and product quality. A case in low-volume, high-complexity electronics assembly industry shows how the approach leverages standard modelling techniques and facilitates the understanding of the causes of poor manufacturing performance using a set of surface mount technology (SMT) process failure models. A prototype application tool was developed and tested in a collaborator site to evaluate the integration of business process models with the execution entities, such as software tools, business database, and simulation engines. The proposed concept was tested for the defect data collection and prediction in the described case study.