ABSTRACT. Proprietary factory simulation software tools are a reliable method for generating performance data and evaluating design challenges around manufacturing workflows and productivity. These tools, however, are generally not an accessible solution for small and medium sized manufacturers (SMEs). Through interviews with experienced simulation developers, this paper attempts to understand major challenges behind the adoption of simulation tools. We additionally discuss potential strategies to mitigate these challenges, such as libraries and automation, and gauge their potential for improving usability.

ABSTRACT. The economic performance of companies in developed countries is currently being tested by the global economic situation due to the Covid pandemic as well as the tense geopolitical context. Industry 4.0 concepts clearly contribute to increasing this overall performance for large companies. But their exploitation in the context of small businesses still remains confidential for SMEs. This article proposes to use sustainability combined with Industry 4.0 concepts such as advanced robotics, artificial intelligence and digital twins, designated by the European Commission as Industry 5.0, to solve this problem and encourage SMEs to use these concepts to improve their overall performance. This article therefore proposes the use of the digital twin as a tool for improving company performance by providing them with an intelligent real-time decision support system. This article is organized as follows: after a literature review on artificial intelligence, digital twins, and performance optimization, the sustainable methodology for digital transformation of companies will be presented. The architecture of the digital twin, as a decision support tool developed in this context, will be detailed. Finally, an example illustration will validate the concepts presented above and show their relevance in improving performance.

ABSTRACT. Digital twins and immersive mixed reality have been improving digital transformation within the industrial context. In computer vision, photogrammetry-based techniques for 3D environment reconstruction make it possible to create virtual models of real scenery based on a set of digital images. This paper addresses the problem of building virtual layouts through the 3D mapping of the real environment and its corresponding digital twin, with a focus on human interaction and training. In the context of human-in-the-loop and Industry 5.0, the research goal is to define a method for preparing humans to be trained using synthetic information, enabling them to perform the real operations required in some productive processes. Through photogrammetry using images, layouts and objects can be recreated, but it is essential to evaluate the accuracy and richness of details to reconstruct a realistic process. Thus, we studied all technological requirements to perform an automated method for shop-floor layout digitalization and digital twin creation, allowing humans to interact and simulate operations as in a real industrial scenario. The research conducts this investigation using a manufacturing automation laboratory and addresses an experimental training task planning. The text presents the novelties in immersive technologies, discusses the feasibility, and evaluates the proposed method.

ABSTRACT. Recent technological advancements have increased the amount of data produced day by day, resulting in an increasing need for real-time data analyses. To fulfill these needs, companies have started to shift their traditional computing systems with other systems in with data integration has a pivotal role for a successful implementation. Therefore, due to its complexity, to guarantee a successful implementation of a data pipeline, an initial effort should be initially placed on the process modelling for the identification of all actors involved. Based on that context, the present paper aims to investigate the main contributions of the state-of-art related to the conceptual modelling of data pipelines. For that, a systematic literature review (SLR) was conducted to identify the existing alternatives and extensions of current notations (e.g.: BPMN, UML, etc.) for the conceptual modelling of data pipelines. As result, not only several notations could be identified, but also some gaps related to the modelling of elements emerged with Big Data.

ABSTRACT. Automation with robots is a very useful of various control systems 7such as computers to control industrial machinery and processes, replacing human operators. Automation plays an increasingly important role in the global economy and in daily experience. Engineers strive to combine automated devices with mathematical and organizational tools to create complex systems for a rapidly expanding range of applications and human activities. In the real-world of automation systems, there are many combinatorial optimization problems (COPs) imposing on more complex structure, nonlinear constraints, multiple objectives and uncertainty. The COPs make the problem intractable to the traditional approaches because of NP-hard ones. Evolutionary Technique (ET) has received considerable attention regarding their potential as novel optimization techniques. There are three major advantages when applying ET to design automation. There are three major advantages when applying ETs to design automation. Adaptability, Robustness, and Flexibility.

In this talk, multiobjective AGV (MoAGV) dispatching in Flexible Manufacturing System (FMS) and multiobjective robot-based assembly line (MoR-ALB) models with recent advances by Evolutionary Technique will be introduced [1~5]. Many applications of automation, such as optimizations and automatic controls, are formulated with complex structures, complex constraints, and multiple objects simultaneously, which makes the problem intractable to traditional approaches. The experimental results demonstrate the effectiveness of the proposed ET algorithms comparing to classical multi-objective genetic algorithms.

ABSTRACT. The cryptocurrency market is increasingly exerting influence on the global economy; however, the prevalence of price volatility serves as a hindrance to the entry of new participants into its commercial dynamics. This article undertakes an assessment of the efficacy of predictive models, specifically the Auto-Regressive Integrated Moving Average model and various machine learning algorithms, including Extreme Learning Machine, Support Vector Regression, Gradient Boosting Machine, Extreme Gradient Boosting, and Random Forest. The focus of existing literature predominantly centers on well-established cryptocurrencies such as Bitcoin and Ether, thereby neglecting a comprehensive examination of alternative assets. Performance evaluation in the test set involved the utilization of key metrics, namely the root mean square error, the coefficient of determination squared, the mean absolute error, the mean absolute percentage error, and the standard deviation. Within forecast horizons ranging from 1 to 4, the Extreme Learning Machine model demonstrated notable accuracy, yielding error percentages of 3.82%, 5.18%, 6.53%, and 7.49%, respectively. In horizon 5, the Gradient Boosting Machine emerged as the optimal model with a percentage error of 8.22%, while a less precise prediction was observed in horizon 10, where the lowest percentage error reached 10%. Conversely, the Support Vector Regression model exhibited suboptimal performance among the machine learning algorithms, registering a percentage error of 12% in the initial forecasting horizon. This underscores the challenges inherent in employing certain models for predicting cryptocurrency values, emphasizing the need for a nuanced understanding of the strengths and limitations of each approach.

ABSTRACT. This paper emphasizes the scientific and research density of the interconnected circular economy, establishing a robust framework for understanding and promoting the interconnection of related concepts and practices over its timeline. Utilizing content analysis and meta-analysis over the past 17 years, it explores the evolution of the circular economy, offering insights into temporality and future research trends. Employing a systematic literature review (RSL) approach based on a PRISMA research protocol, articles from Scopus and Web of Science (WoS) databases were retrieved. The Science Mapping Analysis Software (SciMAT) and Biblioshiny tools were then used to visualize and assess the development and density of scientific contributions. This study illuminates the potential benefits and challenges of the circular economy, identifies key constructs related to its implementation, and evaluates the density and centrality of themes. RSL results highlighted Yong Geng as the author, the Journal of Cleaner Production as the journal, China as the country, and the Delft University of Technology as the institution. Bibliometric analysis demonstrated a significant shift in EC themes, transitioning from "Recycling" to "Circular Economy" as the central theme. It also showed a conceptual expansion, including Chemical Substance, Technology, and Urban Development. The findings of this research confirm conclusions from previous studies and provide a comprehensive and updated view of emerging trends in the Circular Economy. However, it is limited to Scopus and WoS databases, focusing on the interconnected circular economy in general.

ABSTRACT. A hemp-based composite filament (CF) for a fused filament fabrication (FFF) process was developed. The designed filament is based on hemp particles obtained from stack used for cannabidiol (CBD) production and PLA, a bio-based, biodegradable, recyclable plastic. The rheological properties of virgin PLA grains, commercial PLA, and hemp-PLA filaments were analyzed. Then, hemp powder was produced and separated according to particle sizes (75-150 µm and 150-300 µm). The hemp ratio used was 5% and 10% by weight. Next, filaments were extruded from a mixture of virgin PLA grains and hemp powder. Mechanical characteristics of standard specimens were determined. Also, the shape and size of hemp particles were observed. The extrusion temperature was the most prominent parameter. Created 3D-printed parts were more ductile. Moreover, the developed filaments showed lower viscosity that was inversely proportional to the size of the hemp particles and directly proportional to the hemp ratio particles in the composites. The effect of particle size was a more influential factor on viscosity than the hemp ratio in the composite. The ultimate tensile strength (UTS) of the newly designed filaments was lower but the elongation before failure was higher when compared to commercial filaments.

ABSTRACT. Urban planning in a city faces numerous challenges, particularly in contemporary times, where it must consider multiple domains to develop resilient cities capable of incorporating digital transformation into their growth strategies. Addressing sustainability needs, supported by the Sustainable Development Goals (SDGs), can contribute to achieving Goal 11 by integrating technologies to create increasingly smarter cities. This study aims to enhance our understanding of how the primary pillar, urban planning, relates to the secondary pillars of smart cities and digital transformation. The research reported here investigated preliminarily the components of urban planning, identifying potential gaps for exploration and future research endeavours. One of the key findings is the identification of elements comprising urban planning: Applied Technology, Urban Mobility, Generation/Consumption Energy, and Environment, with the latter subdivided into Air Quality, Climate Urban, Human Factors, Buildings Architecture, and Urbanism. This article may assist scholars and professionals in related fields in identifying relevant authors and their specifically research, potentially facilitating further advancements in urban planning for smart cities and various digital transformation strategies.

ABSTRACT. Industrial scheduling is one of the most burdensome operational activities faced by engineers nowadays. Batch process environments add extra challenges to this problem, such as (i) the difficulties of dealing with non-discrete products that, in many cases, require the consideration of mass balances; (ii) the many different and varying operational features that need to be taken into account – distinct types of plants and processing units, diverse intermediate storage and waiting policies, campaign operating modes, sequence-dependent changeover times, etc. – ; (iii) the need to convert primary requirements for products into individual batches (batching), among others. The first part of this talk will discuss these additional defies in detail and will present the main methodologies that academia has proposed to tackle them.

At present, the academic community is able to face more difficult and larger scheduling problems than those addressed two decades ago. Despite these advances, the lack of academic permeation in industry is clearly noticeable. Unquestionably, there are features that prevent academic developments to turn into software products that are accepted in industry. The second part of this talk will attempt to reveal these features and will make a critical analysis of the academic developments, bringing up some ideas to be exploited in the near future in order to close the current academia-industry gap. Since the ultimate goal would be to turn a scheduling methodology into the core component of a scheduling support application, the analysis will be conducted from the perspective of a software development process.

ABSTRACT. Home Healthcare services have improvement potential in terms of resource utilization, cost efficiency, and overall quality of care. Home healthcare services provide a portfolio of services to address the growing healthcare needs of an aging population and individuals with chronic conditions. Further, Lean principles have successfully transformed various healthcare settings by eliminating waste and optimizing operational processes. In this context, our research aims to apply Lean Healthcare principles to home healthcare in Colombia. The objective of this study is to identify and categorize the mudas (wastes) in the delivery of home healthcare services. To do so, we performed multi-case study with a series of focus groups with healthcare staff and administrative personnel from three different healthcare institutions in Bogotá and Cali, two major cities in Colombia. Our research protocol has been approved by an ethics committee and is being developed in more healthcare institutions around the country. Focus groups were made in two independent groups: First, healthcare staff including nurses, physicians, and therapists participated. Second, administrative personnel responsible for managing home healthcare services were also considered. The eight types of mudas are characterized in the operation of the health care services: waiting times, transportation, inventory, movements, overprocessing activities, defects, non-value-added activities, and underutilization of staff. The most frequent mudas found in the research are associated to the planning of routes for the operation and scheduling the visits, the movements and body postures require to adapt to examine patients in a home setting, and the difficulties to transport medium and large equipment. In the focus groups, participant struggled the most with identifying mudas associated with underutilization of staff. Managing the biohazard waste from the operation seems to be an activity that requires more research to achieve conclusions since divergent opinions are reported. By effectively identifying and addressing the mudas specific to this context, we aim to enhance the efficiency, capacity, and quality of care, ultimately leading to improved patient outcomes and a more sustainable healthcare system.

ABSTRACT. The technological advances brought about by industry 4.0, especially in the context of integration with cyber-physical systems and communication protocols, lead to support for the prediction of failures or errors that may be critical to the production process. In this context, the Digital Twin concept proves to be an important tool in the area of industrial maintenance, improving the efficiency of predictive and prescriptive maintenance. This paper aims to present the barriers incident to the implementation of functional requirements in industrial maintenance. Such requirements play the role of supporting attributes that qualify the maintenance concept. From the perspective of existing frameworks and architectures in the literature, an analysis of the relationship between interoperability, functional maintenance requirements and the concept of Digital Twin is conducted. This analysis is carried out through multicriteria decision support methods, resulting in which functional requirements are most consistent with the digital twin concept and how difficult it is to implement each requirement.

ABSTRACT. We present the problem of job scheduling for the manufacture of textile products, considering common conditions in the real environments of this type of industry. The proposed mathematical model considers sequence-dependent setup times, malleability, eligibility, dynamic input, unrelated parallel machines, variable transfer batch, more than two stages and deadline-based objective function. To solve the problem, we applied a genetic algorithm with initial population that includes solutions by priority rules, assignment by flexibility index, multiple crossing points, variable mutation rate and stop criteria by iterations without improvement. The results show that in cases of moderate size a high degree of attraction to the optimum can be achieved with the proposed parameters and that in cases of larger size it may be necessary to increase, above all, the number of iterations without improvement established to stop.

ABSTRACT. Passive occupational exoskeletons have been increasingly used as one way to decrease the risks for musculoskeletal injuries and potentially increase quality when performing a task. Simulation has been used in some studies to model the ways in which an exoskeleton can aid a user and affect his or her body. This study performed a simulation to determine the way that the additional profit brought by an exoskeleton could be modeled using the changes in quality that could result from using an exoskeleton. A simulation model was created to model an overhead assembly task aided using an exoskeleton. This model was used to study the throughput of parts produced with varying levels of good parts, scrap, and rework. Processing time of a task, level of quality in a task based on wearing an exoskeleton, and profit per part all affected whether the additional profit brought by an exoskeleton justifies the initial cost of purchasing an exoskeleton. Analysis was done to study the data over the time span of a year with cycle times having a random triangular distribution with its center ranging from 5-15 minutes, quality ranging from a rework selection weight of 0.02-0.08 and a scrap selection weight of 0.005-0.02. The model indicated the times at which the cost of an exoskeleton was met or exceeded by the increases in profit from changes in quality, and therefore the cost of the exoskeleton would be justified by its quality improvements.

ABSTRACT. The optimization of healthcare delivery, patient safety, and financial performance is crucial in the healthcare sector, driving the adoption of Lean healthcare methodology. This approach, centered on principles of waste elimination and value addition, has the potential to enhance patient care quality as Lean maturity increases. However, despite successful cases, criticisms have arisen regarding the effectiveness of Lean implementation in hospital environments. It is believed that the existence of underexplored obstacles could explain the slow adoption of the Lean culture in hospital organizations. Thus, it is considered essential to recognize and better understand these factors for a correct and efficient Lean implementation. In this context, this work proposes an evaluative Framework, from the Organizational Interoperability perspective, to enable hospitals to assess their Lean maturity and readiness for successful implementation. The Framework introduces a diagnostic evaluation and decision-making model for guiding Lean implementation based on three multicriteria decision support methods: Analytic Hierarchy Process and Decision Making Trial and Evaluation Laboratory for diagnostic evaluation, and Preference Ranking Organization Method for Enrichment Evaluation for decisional evaluation. Finally, the developed Framework will be applied to an Emergency Care Unit (ECU) undergoing a Lean implementation project. The Framework will enable the assessment of the ECU's capacity to receive such implementation, the identification of obstacles, and their influence on the Lean action plan.

ABSTRACT. The rapid digitalization of industries requires the integration of digital technologies into engineering education to ensure future engineers are formed with interdisciplinary skills. This shift is crucial for maintaining industry competitiveness and overcoming challenges adapting to digitalization. Digital twins (DT) and extended reality (XR) technologies, including augmented reality, virtual reality, and mixed reality, are pivotal components of Industry 4.0. While DT enables the representation of virtual counterparts of physical systems, XR enriches user experiences by overlaying digital information onto reality. However, there is a lack of studies exploring the combined implications of these technologies. This paper aims to fill this gap by systematically reviewing the convergence of DT and XR in the academic domain.

ABSTRACT. The Chilean wine industry is economically and culturally vital, providing direct employment to over 100,000 people and accounting for 16.5% of agricultural operations. As the leading wine exporter in Latin America and the fourth worldwide for over two decades, it plays a significant role. Nevertheless, challenges like climate change, water scarcity, and rising production costs impact its competitiveness. In this context, the final wine filtration stage is critical for product quality but demands extensive resource use, including labor, chemicals, water, and filtering media, increasing costs and causing operational issues. The Laurenty filtration index, widely used in Chile, aims to estimate the solid content's impact on wine's final filtration. Despite its utility, discrepancies between expected and actual system performance often occur, leading to reduced filling rates, unplanned cleaning stops, and sudden filter changes, significantly affecting production costs and packaging process continuity. These operational challenges require urgent attention and effective solutions to maintain the industry's competitiveness. This research contributes to the development of robust and reliable methods for operational decision-making in the final stage of wine filtration and bottling. To this end, the adoption of comprehensive indicators to estimate the level of solids in wine and their impact on the final bottling process is being investigated, alongside evaluating predictive models to estimate the expected value of FI associated with physicochemical variables. Through the use of questionnaires, experimental methods, and machine learning tools, it is determined that 87% of surveyed companies use the Laurenty Filtration Index (FI) to estimate solids content in wine, but do not employ it to calculate costs or performance due to its low interpretability. The analysis reveals high variability in FI measurement, limiting its application for decision-making. The Random Forest model demonstrates a 97.7% accuracy in predicting the FI level, offering an effective tool for production management

ABSTRACT. The dynamism and competitiveness in the various industrial segments have encouraged companies to develop ways of making their operations and production processes increasingly efficient, seeking alternatives to reduce the costs of their processes, increase the quality of their products, and become increasingly flexible to satisfy the needs of their customers. The purpose of Production Planning, Scheduling, and Control is to guarantee the quality of products, delivery as stipulated, lower costs, and efficiency of production processes the Lean philosophy (lean manufacturing) comes to the rescue to help companies be more competitive, because with the principle of identifying where the customer sees value in the product or service and mapping the entire value chain to look for waste to be reduced or, preferably, eliminated. The article aims to present a review of the concepts of Production Planning, Scheduling, and Control (PPCP) and Lean manufacturing, as well as proposing a structured procedure for integrating the Lean philosophy into production planning, scheduling, and control. The methodology developed is based on Value Stream Mapping (VSM) with a case study applied to a company in the food industry. Among the most relevant results were cost reductions by optimizing bottlenecks, minimizing intermediate stocks, and optimizing the production process. The contribution of this work is a detailed description of VSM in the planning, scheduling, and control of production in a food company.

ABSTRACT. This study addresses customer perceptions of reverse packaging logistics in a Brazilian company in the production and marketing sector of animal nutrition supplements and the challenges faced by the company's management. Carried out in Paraná (Brazil), the research involved a questionnaire for 65 customers and an interview with the company's production manager. The main challenges identified include the cost of correct disposal, the lack of nearby collection points, and the lack of standardization for disposal. Although awareness about sustainable practices grows, many customers still dispose of some waste incorrectly. Most respondents expressed interest in receiving more guidance on reverse logistics, indicating a willingness to adopt more sustainable practices. However, the company currently does not have direct actions to encourage the correct disposal of packaging by end customers, limiting itself to complying with government regulations. This fact neutralizes its position about developing internal sustainable practices, which could bring benefits such as cost reduction and improving logistics processes.

ABSTRACT. Maintenance is a crucial aspect to any type of industry. The careful planning and execution of a maintenance schedule is paramount to a correct and predictable behavior of equipment, the smooth operation of the production line, and that resources and manpower are not wastefully spent in the process. However, the process of maintenance scheduling has never been a trivial one, due the interdependence on several factors that are usually not accounted for, such as production scheduling, resource availability, among others. The significance of those issues is even more evident on the industrial scenario, where a multitude of equipment of similar functionality is present in large numbers, and carefully parameterizing the maintenance of every single machine is often not a feasible option. Amidst In this context, transfer learning techniques present a potential solution for circumventing this impracticality, as they can allow for a quick and easy adaptation of models in-between different types of equipment by taking advantage of the similarity they may exhibit, be they structural or operational. This research aims to explore the overall scenario of transfer learning applications to maintenance-related problems. It focuses on giving an overview of the current state-of-the-art, pointing out and summarizing the most prominent approaches when tackling maintenance-related problems with similar equipment, and identifying potential literature gaps for further research to be conducted. The process is conducted with the parameterization of a literature review, considering relevant keywords for the theme, such as “maintenance” and “transfer learning”. Journal papers from the last 10 years are considered, while conference papers from the last 3 are included, as to account for possible material that may not yet been published on a journal. The search is performed, and the results from three bases (Scopus, Web of Science and Compendex) is aggregated. The usual funneling process is performed, first by title, then abstract, then full text in order to eliminate papers that are not aligned with the discussed topic. The resulting papers are then analyzed, discussed, and their main points grouped and presented, such as difficulties faced, and techniques utilized. The initial search has been performed and returned a total of 977 relevant papers to the topic, and some key points could be observed from the preliminary analysis. Results show a considerable reliance on reinforcement learning (RL) techniques in conjunction with the transfer learning ones, in most cases using a multi-agent RL approach to take advantage of the multi-equipment scenario of the problem. Papers could also be classified based on the granularity as of what they considered to be similar equipment, with results spanning from side-to-side assembly lines to entire fleets of vehicles, to even equipment spanning different domains of operations. In conclusion, the transfer learning umbrella presents a wide variety of techniques that can perfectly match the asset similarity problem found in the industrial scenario. The applied techniques also seemed to vary based on the granularity of the equipment similarity, which instigates further research on the topic.

ABSTRACT. The Lean principles are widely recognized as the foundation for efficient and optimized operations in the field of production engineering. Originating from the Toyota Production System (TPS) and globally adopted, these principles have proven effective in waste reduction, productivity improvement, and fostering continuous improvement. Additionally, the advent of Industry 4.0 has brought opportunities to address various business challenges through the integration of cyber-physical systems and real-time data exchange. This new era of production emphasizes data-driven decision-making and agile responsiveness to customer demands, potentially driven by the use of social media platforms, offering significant opportunities for collaboration and communication in businesses. By leveraging the interaction between Lean principles and social media platforms, companies have the potential to enhance communication, collaboration, knowledge sharing, organizational learning, and customer relationships in business contexts. This article explores this interaction through a systematic literature review, using text mining tools to highlight the implications of social media platforms in Lean environments and establishing connections with the impact of social media in other work contexts.

ABSTRACT. Cloud manufacturing (CMfg) is a recent manufacturing paradigm that aims to provide a networked environment to manufacturing providers and customers. Within this network, manufacturing providers can share resources and collaborate to satisfy customer orders. One of the main challenges in the automation of such sharing is selecting multiple resources that can collectively undertake complex tasks in an optimal way. This problem, often referred to as “service composition and optimal selection (SCOS),” has been studied extensively. However, the main body of the work has focused on developing and implementing cutting-edge algorithms to further optimize the Quality of Service (QoS) of the final composite service. In this paper, we target some of the shortcomings of these studies by first, collecting and utilizing a real-world manufacturing dataset and second, implementing a bidirectional encoder representation from transformers (BERT)-based model on the aforementioned dataset to form the candidate sets. A well-established metaheuristic (genetic algorithm) is also implemented to form the optimal composite solution.

ABSTRACT. This article explores the impact of concept drift in smart manufacturing through a systematic mapping approach, focusing on digital twin systems. In the dynamic environment of manufacturing, the gradual evolution of data patterns presents a challenge to artificial intelligence (AI) mechanisms and so the consistent integration of digital twins. Concept drift refers to the phenomenon where the statistical properties of the target variable in a predictive modeling task change over time, necessitating adjustments in the model to maintain accuracy and reliability. With the consolidation of industry 4.0 technologies, factories shopfloor provide thousands of data each day creating a great opportunity for AI applications, but also defining some challenges for data-drift handling in data-based digital twins. In this context, drift detection tools empower manufacturers to rapidly address potential defects and maintain the desired level of product quality, production line performance and accuracy of predictive maintenance AI models. The integration of technologies mentioned above not only improves real-time quality assurance but also establishes a proactive initiative for continuous improvement. By consolidating current research, this mapping study contributes to the understanding of drift challenges in smart manufacturing and identifies avenues for future research to enhance the robustness of AI systems embedded in digital twin solutions.

ABSTRACT. Modern manufacturing has been undergoing significant transformation into the digital era. Advances such as additive manufacturing, biomanufacturing, digital twin, and digital manufacturing bring benefits to manufacturers, but also pose the challenge of transforming planning data using several software tools to accom-plish real-time feedback and adjustment for optimal production control and moni-toring. Software tools such as CAD, and CAM. PLM, Simulations, ERP, and MOM usually store data in their data models without regard for the semantic meaning of such data. To address interoperability issues researchers have devel-oped ontologies to capture the semantics of data, for example, Industrial Ontolo-gy Foundry (IOF) is leading efforts to develop reference ontologies for semantic data integration. This paper reports the results of applying those reference ontolo-gies for data interoperability between CAPP and ERP systems. We have extend-ed reference ontologies to include terms from a domain of plastics manufacturing, particularly process and production planning applications. Domain ontologies are integrated with some domain data of process plans plastic parts from a CAPP systems, and Odoo, an ERP system. We have demonstrated semantic equivalency of those data, though they carry different data model labels. The knowledge graph of ontology and data has been visualized using knowledge graph tool GraphDB. The demonstration serves as a pathway and method for software systems in-teroperability in industry.

ABSTRACT. At the beginning of industrial automation, the options for connectivity were limited compared to the systems available in 2024. The automation paradigm was characterized by the prevalence of isolated and proprietary systems, leading to a deficiency in interoperability and a lack of open and standardized communication frameworks. The Industry 4.0 (I4.0) in Small and Medium Enterprises (SMEs) study reveals a shortage of research on how manufacturing can transition from conventional models to interconnected and virtual due to less availability of financial resources compared to the larger ones. However, there is a heightened attention to SMEs, which can be attributed to the fact that these enterprises constitute the vast majority of businesses in most countries. The current work aims to guide a medium sawmill to digitization designing and planning for a modern interconnected ecosystem. The new architecture is composed of concepts of the international standard ISA-95 model, middleware software architecture, and communication protocols for components enabling interoperability and flexibility. The overall system brings a new perspective to the business operation, focusing on improving its manufacturing and logistics transparency.

ABSTRACT. This research explores the integration of blockchain technology with digital twins in semiconductor manufacturing and its impact on supply chain optimization. Drawing insights from leading work such as "Blockchain Revolution", and "The Business Blockchain", this paper delves into interrelated models, processes, and protocols, highlighting the potential for transforming the semiconductor manufacturing and supply. An implementation case study is examined.

ABSTRACT. The advances in virtual and mixed-reality technologies have provided new opportunities to promote interactive experiences and real-time monitoring in advanced manufacturing. The objective of this study is to develop a data visualization and information fusion platform using VR/XR to assist operators and field engineers in monitoring the equipment in real time. This study employs XR to develop a mixed twin in an advanced manufacturing setting while using computer vision techniques to provide an immersive experience for operators. This immersive experience will allow users to visualize the processes in an information-rich environment and help dynamic and intelligent decision-making. This platform pro-vides a dynamic and interactive 3D model of the physical environment, complete with functional digital replicas of key industrial equipment. A case study of virtu-al assembly is presented to demonstrate the potential of the proposed framework. The findings of this research highlight the potential of mixed reality to transform user experiences in various applications in the manufacturing industry and work-force development, as well as offering advances in remote equipment monitoring.

ABSTRACT. Assistive Technology (AT) is an adaptive resource designed to promote the inclusion and autonomy of individuals with various types of limitations by incorporating complex and computerized devices. This assists in enhancing the social and interactive participation of people with disabilities and the elderly, whether their limitations are physical, auditory, visual, and/or neurodivergent. Alongside this, we have technological advancements associated with AT, such as the Internet of Things (IoT), robotic assistants, smart homes, among others. Therefore, the research objective is to highlight the major technological advancements and their impacts on AT device users. The literature was searched in Scopus and Web of Science (WoS), and articles focusing on the applicability of technological advancements in AT were selected, including artificial intelligence (AI), Alternative and Augmentative Communication (AAC) devices, robotic exoskeletons, the Internet of Things (IoT), wearable devices, augmented reality, 3D virtual environments, and Brain-Computer Interfaces (BCIs). A bibliometric analysis was conducted as the methodological procedure to explore, delimit, and assess the relevance of articles in this research area. Through this academic production, it is evident that sharing knowledge and collaboration between research centers, technological industries, and global universities are crucial for enhancing AT and developing more adaptable and accessible devices for users, based on the gaps found in the literature. The bibliometric analysis applied to the tools revealed that the topics of Deep Learning and robotic assistants represent future research opportunities with a central focus and limited existing research. However, the term 'occupational therapy' showed a tendency towards centrality within AT research.

ABSTRACT. Closing session