AHFE Open AccessHuman Aspects of Advanced Manufacturing, Production Management and Process Control
Editors: Waldemar Karwowski, Beata Mrugalska, Stefan Trzcielinski
Topics: Advanced Manufacturing
Publication Date: 2025
ISBN: 978-1-964867-60-1
DOI: 10.54941/ahfe1005995
Articles
Smart Factory and Industry 4.0: A Survey on Advancements, Technologies, Methods and Perspectives of Digital Transformation in Manufacturing
Digitalization is fundamentally transforming the manufacturing industry, leading to the development of intelligent factories, known as Smart Factories. These form the core of Industry 4.0 and combine innovative technologies such as the Industrial Internet of Things (IIoT), Cyber-Physical Systems (CPS), Machine Learning (ML), Artificial Intelligence (AI) and Big Data to maximize efficiency, flexibility and resource conservation. This paper provides a comprehensive overview of the Smart Factory as a central element of the Fourth Industrial Revolution (Industry 4.0). It presents key digitalization methods as well as technological innovations and approaches that have been developed over more than a decade of continuous progress in Industry 4.0 and digitalization. Finally, an insight into current research at University of Applied Sciences Bochum is provided, focusing on the application and practical implementation of intelligent technologies in the manufacturing industry. The emphasis is on solution approaches for the realization of smart production processes that equally address technical, social and economic requirements.After more than a decade of technological progress and developments in the context of Industry 4.0, digitalization is progressing steadily. Characterized by the use of innovative technologies and digital networking, the fourth industrial revolution marks a decisive turning point. It is increasingly becoming a key factor for companies to remain successful and fit for the future in a highly competitive market environment.[1] The systematic planning of a networked factory is made possible through the targeted use of modern methods and tools. This takes into account a variety of framework conditions, integrates all elements of the value chain and at the same time creates the basis for self-controlling and autonomous company processes. The so-called smart factory uses state-of-the-art technologies to not only achieve operational goals efficiently, but also to fulfill social and economic functions by seamlessly connecting the physical and virtual worlds.[1] As part of the digital transformation, complex, interactive and autonomous systems are being created, for example through the use of brownfield methodology. These enable a more efficient and powerful optimization of existing structures as well as business and production processes by specifically upgrading and integrating existing potential.[1,2,3] With the help of cyber-physical systems, physical devices and processes in established production landscapes can be equipped with computing and network capabilities and connected to a data and knowledge structure that is ultimately integrated into the manufacturing process.[1,4,5] The use of algorithms for industrial big data and advanced technologies enables the optimization and adaptation of manufacturing processes. In this dynamic development, self-adaptive, self-learning and autonomous systems can help to successfully overcome the challenges of rapid technological progress and increasing product complexity. The integration of information technologies and operational technologies is crucial to achieving the overarching goal of digitalization in established industries. In this context, there is a particular focus on creating conditions that can fulfill not only operational goals but also social and economic functions within a factory.[6,7] This requirement suggests that the redesign of a digital factory must, on the one hand, ensure the smooth technical and economic flow of the production process and, on the other hand, also create optimal working conditions for the personnel in the factory. [8,9,10]
Haris Karic, Daniel Schilberg, Arockia Selvakumar Arockiadoss
Open Access
Article
Conference Proceedings
Perceived Augmented Reality Affordances in Logistics
Augmented reality (AR) promises to transform logistics operations by enhancing real-world processes and supporting workers in dynamic environments. This study investigates the perceived affordances of AR in logistics and examines how these perceptions differ between AR developers and logistics workers. Based on semi-structured interviews with a balanced sample of five AR developers and five logistics workers, a qualitative content analysis following an adapted affordance framework was conducted. The analysis revealed 11 affordance categories, with enhancing affordances emerging as the most prominent. While developers identified a broader spectrum of potential affordances, logistics workers focused on practical, task-related benefits. These findings extend affordance theory by introducing logistics-specific affordances and highlight the need for AR designs that bridge the gap between technical potential and everyday operational requirements. The insights offer valuable guidance for AR system development within Industry 4.0 contexts and suggest directions for future research.
Stella Kolarik
Open Access
Article
Conference Proceedings
Complexity reduction using AI-based correlation analysis using the example of operator actions in a thermal waste treatment plant
This Paper examines the use of artificial intelligence (AI) to analyse and optimise operating messages in a waste-to-energy plant. The aim of this thesis is to identify potential for improvement in automation technology. For this purpose, the 13,9 TB of data collected over the years from the message archive of the Wuppertal waste-to-plant are analysed, in particular the operating messages. The Siemens® Simatic PCS7 Process control system, which has been in use since 2016, is used as the data source.The lack of documentation on the database structure of the Siemens® reporting archive is addressed by re-documenting the structure of MSSQL database files (.mdf and .ldf). This provides a detailed insight into the data organisation and structure of the reporting archive for the first time. An MSSQL server is set up on a Linux system to import these files and make them accessible. With MATLAB as a client and using the Statistics and Machine Learning Toolbox, AI algorithms are used to analyse correlations between the operator messages. The aim is to recognise patterns and connections that indicate inefficient processes or unnecessary messages. The results of this analysis will be used to optimise the automation technology. This will reduce the workload for operators at the control centre and at the same time increase the efficiency and safety of power plant operation.The combination of back-documented data structures, AI-based data analysis and application to real operating data makes an important contribution to improving operational management in waste-to-energy plants. It shows how modern technologies and methods can be used to effectively address the challenges of thermal waste treatment and optimise operational processes.The development and structure of the paper is based on the V-model, which is commonly used in software development projects. The V-model was chosen for the structure of this paper because it offers a clear and systematic approach to the development and testing of software development projects. The development process starts at the top left, then goes down, then up again and finally ends at the top right. The descending branch contains constructive activities, while the ascending branch contains work steps for quality assurance. It begins with an “introduction”, which sets out the background, motivation and objectives of the research, followed by the “problem statement”, which highlights the current challenges and the current situation. The “target situation” defines the desired end state and derives requirements from this. These are further specified in the “Requirements” chapter. The “Theoretical foundations” provides the necessary scientific context, while the “Methodology” describes the research and analysis methods used. “Data analysis” and ‘Optimization proposals’ present the investigations carried out and the resulting suggestions for improvement. The “Discussion” reflects on the results and compares them with existing approaches. The “Conclusion” summarizes the findings and provides an outlook on future research opportunities.
Wolfgang Krause, Daniel Schilberg
Open Access
Article
Conference Proceedings
A Framework for Sustainable Logistics 4.0: the Polish perspective
In recent years, the concept of sustainable logistics 4.0 has gained importance and attracted significant attention among scholars and practitioners. Digitalization technologies allow us to guarantee that products are delivered to the appropriate location, at the right time, and in the right quantity, simultaneously minimizing waste. They facilitate the visualization of logistics operations and connect them by simulation and optimization tools, enhancing decision-making processes. Research that examines the role of sustainable logistics 4.0 shows that implementing such technologies can bridge the gap between the physical and digital worlds, and enhance process efficiency and flexibility. Their critical characteristics are autonomy, transparency, coordination, and collaboration amongst the supply chain processes.In this paper, we defined the capability of implementing the activities related to Industry 4.0, digitalization and sustainability in practice. To achieve it, the questionnaire survey was conducted in Poland. It was available for participants on 1KA OneClick Survey (www.1ka.si), an open-source application for online survey services. The questionnaire was based on a five-point Likert-type scale. The survey was conducted in 2023. The analysis covered data from the intended respondents in Poland who were representing the logistics sector. The data were statistically analyzed. Their results showed that optimisation of logistics processes/routes/supply chain to achieve sustainability goals and use of at least one optimisation tool on advanced level and behaviour following environmental ethics were the most often chosen activities in order to direct to sustainable logistics.
Beata Mrugalska, Brigita Gajsek
Open Access
Article
Conference Proceedings
Evaluating Changeable Production Logistics: A Multicriteria Approach
In the ever-evolving production landscape characterised by various disruptions and uncertainties, the role of production logistics in ensuring operational efficiency is paramount. The increasing challenge is that production, and with it, the supplying production logistics must adapt to dynamic and unpredictable changes due to climate change, pandemics and other global conflicts. In the practice of many manufacturing companies, external consulting firms plan production logistics. They develop various planning alternatives, which are then evaluated by the company's internal teams based on multiple criteria. However, cost factors usually override the evaluation and changeability is not sufficiently considered. The traditional focus solely on economic viability is no longer sufficient. A paradigm shift towards considering resilience, sustainability, and social aspects is imperative. These considerations are the cornerstones of Industry 5.0 which can be achieved through changeability.By emphasising the concept of changeability - the ability to undergo significant structural changes in response to unpredictable internal and external influences - this research contributes to a deeper understanding of the strategic imperatives in modern production logistics environments. It highlights the importance of embracing change and fostering resilience while addressing economic, sustainability and social concerns. This paper addresses the challenge of evaluating and selecting appropriate strategies to enhance changeability in production logistics while considering these multifaceted dimensions.While product changes and their effects have already been widely studied in research on a factory level, production logistic-side change management is still in its infancy. Most Publications such as those by Hernández or Heger deal with the evaluation of changeability on the level of factory planning. Although production logistics is a critical component in the value chain of manufacturing companies, little research deals specifically with the assessment and evaluation criteria of the changeability of production logistics. Closing this gap in the literature is the focus of this publication, and it opens up the possibility of gaining new insights in this area. This publication aims to develop a method for evaluating planning alternatives in production logistics that does not focus exclusively on economic indicators but, above all, considers the changeability of the system. In addition, further evaluation criteria, such as sustainability and social aspects, are included to enable the most comprehensive possible evaluation of the given alternatives.The structure of this research consists of three parts. First, based on a literature review, an overview of definitions and approaches for the multicriteria evaluation of production logistics changeability is presented. Second, a concrete procedure to evaluate changeability is developed, and third, it is applied in production logistics.Thus, the presented measure is one efficient response to the ever-changing business environment of production logistics and offers a multi-criteria evaluation approach for changeable production logistic processes.
Pia Vollmuth, Hanna Hüttner, Johannes Fottner
Open Access
Article
Conference Proceedings
Exploring Operator Requirements for Human-Robot Collaboration in A Composite Lay-Up Process
Many industrial production processes continue to involve laborious manual tasks. Composite layup processes in aircraft interior manufacturing still rely heavily on lengthy and physically demanding manual task performance by skilled human operators. Applying a robot to work collaboratively with the operator in the composite layup process can be a promising solution to not only improve productivity and efficiency but also the operator’s well-being. To ensure human-robot collaboration achieves these benefits, it is important to design the new system taking user requirements into account. This paper describes a study that explores a new robot application design for composite layup from a Human-Centred Design perspective using a participatory design approach. A Hierarchical Task Analysis was first conducted to systematically review the traditional composite layup process that requires two operators’ manual work and identify task challenges to be addressed by the collaboration between one human and one robot. Then, a participatory design group workshop was conducted with experienced layup operators to capture user requirements, indicating expected robot applications to address the current task challenges. These expected applications are further classified into five types: Action, Retrieval, Checking, Selecting, and Information Communication, which reflect desirable cognitive capabilities and technology integration for the robot system. The findings provide insights for designing human-robot systems that align with human capabilities and requirements to facilitate seamless integration into existing layup workflow. Also, the research outcomes could be applied to develop a structured framework for advanced human-robot collaboration development in broader industrial operations.
Jingyi Zhang, Maryam Bathaeijavareshk, Sarah Fletcher
Open Access
Article
Conference Proceedings
Construction Method of Complex Product Repair Model for Process Execution End
The performance of complex products is directly related to their assembly quality, and as a result, the precision requirements for assembling complex products are becoming increasingly stringent. However, in the assembly process, precision deviations are inevitable, often leading to situations where the required accuracy cannot be met. In such cases, rework and adjustment methods are needed to modify certain geometric attributes of the product model to ensure assembly precision. Currently, most rework solutions rely on the personal experience of on-site workers and repeated disassembly and reassembly, lacking scientific guidance. Aiming to improve the accuracy of rework solutions, this paper proposes a rework model construction method tailored for the process execution phase. First, an error propagation function is established based on the Small Displacement Torsor (SDT) and homogeneous transformation matrices. High-precision measurement equipment is then used on-site to collect point cloud data of key features, which is processed to reconstruct the error model of critical features, thereby enabling a more accurate error propagation model. Subsequently, an optimization-based dynamic adjustment mechanism and an interval resampling mechanism are introduced to improve the particle swarm optimization (PSO) algorithm. Using the improved PSO algorithm, more accurate rework plans are generated to guide on-site rework operations. Finally, a case study is conducted using the rework process of a satellite product subassembly structure to validate the proposed method. The results demonstrate that the proposed approach can generate more reasonable and effective rework solutions, not only improving on-site rework efficiency but also increasing the first-time assembly success rate of complex products.
Jianqiang Liu, Xiaojun Liu
Open Access
Article
Conference Proceedings
Enhancing E-commerce Efficiency: AI Solutions for Last-Mile Delivery in Johannesburg.
In the South African context, Johannesburg has become the hub of economic activities and the case of e-commerce expansion comes with a problem of last mile delivery. The challenges caused by the void in artificial intelligence (AI) literature are well known, but such application in Johannesburg’s logistical environment where there is a high level of traffic, poor infrastructure and even laws is lacking.This study seeks to bridge that gap by employing a quantitative approach, drawing on statistical data from a variety of sources, including academic journals, industry reports, and case studies of AI in last-mile delivery. Key findings demonstrate that AI technologies including dynamic routing, predictive analytics, and autonomous delivery vehicles significantly enhance delivery performance by reducing times and costs and improving reliability and customer satisfaction metrics. Particularly, the adoption of AI-driven route optimization and scheduling has led to measurable improvements in delivery efficiency and reductions in operational costs. These results have substantial implications for e-commerce logistics, suggesting that targeted AI implementations could mitigate many of the current delivery inefficiencies in Johannesburg, thereby enhancing overall business competitiveness and sustainability in the e-commerce sector.. As a result, the application of AI algorithms in path finding is expected to greatly reduce costs, giving rise to better and more efficient last-mile delivery services in Johannesburg and other regions.
Matanda Alan Tshinkobo, John Ikome, Ibrahim Idowu
Open Access
Article
Conference Proceedings
End-user engagement in developing Virtual Reality Training systems for Industry 5.0
Virtual reality (VR) training has emerged as an effective means of training workers in safety procedures and in learning new technologies and roles. In the field of industrial human-robot collaboration (HRC), VR can be a promising means of training and upskilling industrial operators. Operators familiarize themselves with robots through a virtual simulation before in-person interaction with the robots. As with any technology, the development of VR training systems should include user-centred research to ensure user friendliness of the training interface, and effective use and acceptance by the end-user. The following study presents a methodology for engaging end-users in the initial stages of development of VR training systems. The end-user engagement took place as an online workshop with 5 industrial operators from the automotive industry. They first discussed their current training practises and expectations from future training for HRC. Following this, they were shown first-person videos of a user interacting with the VR training system and their opinions and feedback regarding the system and the process of VR training were captured. The operators’ feedback about the VR training system, and integration of this feedback are discussed in detail.
Vaishnavi Sashidharan, Sarah Fletcher, Diego Sagasti Mota, Eduardo Ibañez, Nikos Dimitropoulos, Ilias Tompoulidis
Open Access
Article
Conference Proceedings
Aspects of Nano Scale SAP of 18-8 Stainless Steel with Permanent Rare Earth Magnetic Tool for Humans
Serious health issues can develop in people from eating contaminated food, especially if the food production pipelines are not adequately maintained. Preventing biofilm formation on the interior surfaces of pipelines in these industries is crucial for ensuring food safety. SAP is an advanced, non-traditional technique used for achieving nano scale finishing on very hard non-ferrous materials like stainless steel and ceramics. For precise finishes, costly abrasives such as Alumina, Silicon carbide, and Diamond powder are utilised. Although SAP is not a novel concept, it requires initial setup restoration, high operational costs, and have few thermal issues with abrasive materials. This study explores the parameters of nano scale finishing using sintered abrasives like Green Silicon Carbide and Electrolytic Iron powder with permanent rare earth magnets arranged in a zig-zag pattern on 18-8 Stainless Steel. The findings indicate that the gap between the 18-8 stainless steel work-piece and SAP magnetic tool has the greatest impact on PISF, followed by circumferential speed, achieving highest of 94% PISF with a 1.17% material extraction rate.
Rahul Sharma
Open Access
Article
Conference Proceedings
MetaBPL: Fault Detection in Business Logic Systems
Critical workflows in manufacturing, infrastructure, and logistics rely heavily on business process logic systems, where even minor faults or vulnerabilities can lead to significant operational disruptions or security breaches. For large companies, a typical product recall may cost more than ten million USD, and every hour unplanned downtime of a manufacturing line might incur a million dollars in losses. While typical processes like statistical quality assurance and auditing can help mitigate future occurrences of faults, they time consuming and often lead to downtime as corrective actions are put in place. To address these real-world challenges, we deploy a Large Language Model (LLM) agent capable of detecting vulnerabilities and faults within business systems. Our dual-pipeline framework first extracts rules and specifications, which are efficiently stored for use in the subsequent fault detection pipeline. By integrating advanced natural language processing (NLP) techniques and Retrieval-Augmented Generation (RAG) methodologies, our approach automates the extraction and analysis of process specifications from diverse document formats, including BPMN diagrams, structured PDFs, and JSON files. Once extracted and organized, these rules and their instantiations can be analyzed by the system’s fault detection pipeline or exported to formal languages for evaluation. The first pipeline automates specification generation through advanced text extraction methods. Utilizing optical character recognition (OCR), structured data extraction, named entity recognition (NER) and semantic processing, we identify and map key phrases to form fields using domain-specific lexicons. We employ coreference resolution to establish accurate mappings across documents, allowing us to automatically generate consistent specifications. We convert the extracted data into a vector database (ChromaDB) to facilitate similarity-based retrieval. Building upon this foundation, we implement the second pipeline using a RAG architecture designed for fault detection. When a user submits a query, the system searches the vector database to retrieve the relevant sub-context, which is combined with a business logic-specific prompt derived from the user's query. Dynamically generated fault detection queries enable the LLM to identify discrepancies in workflows and produce detailed fault analyses and corrective recommendations. We evaluate our approach using three metrics: (1) the correctness of the LLM-generated answers (completeness versus hallucination), (2) question-answer accuracy given the context provided to the LLM, and (3) the quality of the RAG process. Our corpus for evaluation is based on artifacts related to typical business processes such as excerpts from regulations (DOT), standards (ISO-9000), and organizational documentation such as Quality Maintenance System and internal business form (travelers, work instructions, bills of material etc.). Our dual-pipeline framework enhances the precision and scalability of fault detection, offering advancements in the automated optimization of business process logic systems. Also, it empowers end users to verify the correctness of processes without extensive technical expertise and mitigate risks associated with faulty business logic. By improving the efficiency of critical workflows, our solution contributes to the overall security and robustness of essential industrial operations.
Gregorios Katsios, Diego Manzanas Lopez, Benjamin Ryjikov, Samuel A Merten, Daniel A Balasubramanian
Open Access
Article
Conference Proceedings
From Composite Filaments to Low-loss Circuits: A Novel Hybrid Manufacturing Approach to 3D-Printed Electronics
Electrically conductive filaments have shifted the paradigm of 2D circuitry towards 3D-printed electronics by enabling rapid prototyping of complex, embedded circuits. Despite being limited to low-current applications due to ohmic losses from their composite structure, the involvement of a user is kept at a minimum. Selective immersion-based electroplating addresses this limitation but is constrained to specific design spaces and requires human intervention. This study explores the concept and novelty of a proposed hybrid manufacturing system based on 3D printing followed by electroplating, addressing the limitations of traditional immersion-based methods. Moreover, both processes are performed on the same machine - a modified Prusa i3 MK3S+, paving the way towards sequential and human-independent hybrid manufacturing. Thusly 3D printed and electroplated samples were validated in terms of coating quality and their subsequent electrical resistance. The conductivity of commercially available copper-infused polymer filament was locally increased by two orders of magnitude through the proposed approach. Additionally, microscopy was used to characterize homogeneity. As a functional demonstrator, an electromagnetic acoustic transducer of 7.3 Ω resistance was successfully manufactured and tested, highlighting the practical applicability of the proposed method. Selective cup-based electroplating of 3D-printed electronics therefore announces its potential as a human-independent process within low-loss circuitry, while employing the full design-space flexibility of additive manufacturing.
Artis Fils, Hanna Maxie Brüggemann, Andrei Alexandru Popa
Open Access
Article
Conference Proceedings
Operation and Maintenance System Design with AI Intervention：Bibliometric Analysis Based on Citespace
This paper employs bibliometric analysis to explore the design of AI-intervened operation and maintenance systems. By examining 326 relevant publications from the database, the study identifies key developments, research hotspots, and emerging trends within the field. The findings highlight a growing body of literature and increasing international collaboration, with research spanning diverse areas and showing notable shifts in focus. Through Citespace analysis, the paper uncovers design opportunities and underscores the importance of future research on human-AI interaction to enhance operational efficiency.
Ying Zhang, Jun Zhang
Open Access
Article
Conference Proceedings
Introducing 3D printing of TPU in the leather industry: a discontinuous innovation of local manufacturing practices in Italy
This study investigates how design processes can drive the transformation of production models in local manufacturing districts by integrating co-design and co-creation in these multi-stakeholder contexts. Particularly, this research, as part of the PNRR EAR – Enacting Artistic Research project, aims at investigating the bridge between the expertise of master artisans and the potential of additive manufacturing. Indeed, by combining 3D printing technology with traditional artisanal practices, the research reinterprets Made in Italy as a dynamic balance between heritage and innovation. It examines the Italian leather tanning industry in the Santa Croce sull’Arno Leather District, which consists of over 1,100 companies generating a turnover of 4.3 billion euros. In this context, the research analyzes the use of 3D printing to produce thermoplastic polyurethane (TPU) components for leather embossing, which is traditionally performed with high-frequency (HF) techniques. This approach enhances production flexibility and customization, particularly benefiting small and medium-sized enterprises (SMEs) in artisanal sectors. Indeed, the Italian manufacturing model—positioned between craftsmanship and industry—relies on intermediate technologies that ensure both efficiency and adaptability. The research underscores the design’s role in social change and disruptive innovation. It demonstrates how emerging technologies like 3D printing can provide accessible, cost-effective solutions when design acts as a strategic tool, fostering innovation while respecting cultural, material, and territorial heritage. Through a critical examination of technological transitions, the study concludes that: (a) design serves as a key driver for rethinking production models and strengthening local expertise within a global framework; and (b) shifting from traditional to additive manufacturing in leather embossing is not just a technical upgrade but a fundamental transformation—cultural, systemic, and multidimensional. This shift presents new growth opportunities for the sector while promoting a more sustainable, inclusive, and adaptable approach to design, capable of reconfiguring resources even in peripheral areas.
Gabriele Goretti, Lorenzo Masini, Sonia Massari, Caterina Dastoli
Open Access
Article
Conference Proceedings
Empowering process engineers with natural user interfaces for incident analysis and documentation – A case study in battery cell manufacturing
Driving process innovation and ensuring the product quality to reduce scrap rates are among the key objectives for a process engineer in battery cell manufacturing. Since unresolved product quality incidents often lead to a higher scrap rate, thorough analysis and documentation are required. However, the root cause analysis of product quality issues is often complicated by the lack of systematic documentation and its associated data. This deficit points to an urgent need for a user-friendly interface that facilitates efficient data collection and analysis. In this case study we propose the development of an application providing a user interface tailored for the challenging working conditions in battery cell manufacturing, such as the use of multi-layered gloves, face masks, noisy environments, and the limitation on space for input devices in cell assembly and formation. Focus groups and contextual interviews were conducted to gather insights from users to help design the initial version of the user interface. Due to an usability inspection, areas for improvement as well as features that already had a positive impact were identified. Further iterative testing and evaluation methods such as RITE (Rapid Iterative Testing and Evaluation) were applied to conduct user studies, which allowed for continuous feedback to ensure that the application delivered on user requirements and usability standards. In this context, we investigated the usability of natural user interfaces (NUIs) on mobile devices and integrated technologies such as speech-to-text input and image attachment to optimise the analysis and documentation of incidents. The results demonstrate a significant improvement in operational efficiency. With the proposed solution, the duration of the documentation and analysis process is can be reduced by 20% to 50%. Moreover, it enhances the overall user experience by addressing the industry-specific challenges of battery cell manufacturing. In addition, the solution minimises the barriers for documenting incidents. It serves as a starting point for further detailed user studies with a commercial battery manufacturer. This improvement not only confirms the effectiveness of NUIs in enhancing data collection and analysis in battery manufacturing environments but also highlights the potential for broader applications in similar industrial settings.
Matthias Kammermeyer, Patricia Gödri, Tobias Dapolonia, Florian Maier, Johannes Wanner
Open Access
Article
Conference Proceedings