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In the past decades, the German paper industry has been increasing the sustainability of paper production by continuously using more recovered paper. Today, recovered paper is an essential secondary raw material to produce new paper. Nevertheless, around 20 % of the paper produced in Germany is still not returned to the recovered paper stream and instead is mostly thermally used. To further increase the use of fibers from recovered paper as secondary raw material and thus, to reduce primary energy consumption and greenhouse gas emissions, new approaches to recycle recovered paper from mixed waste streams are needed. In the research project "Energy savings in paper production by opening up the value chains of recovered paper from lightweight packaging, residual waste and commercial waste" (EnEWA), a treatment process is developed to recover and recycle recovered paper from the mentioned waste streams. The advanced mechanical process to be newly developed consists of dry-mechanical sorting, the manufacturing of economically usable secondary raw materials (defibration), their wet-mechanical dissolution, separation and hygienization and finally their reuse in paper production. This enables the exploitation of new waste streams in material recycling and thus contributes to reduced energy consumption as well as CO2 emissions within the circular economy. Initial material characterization analyses show that on the one hand the quality of the paper discarded in mixed waste streams is sufficient to be used as secondary raw material and that on the other hand up to 50 % of the paper discarded could have been disposed of within the separate paper collection. The results are incorporated in the development of a sorting process for pre-concentrates, which serves as input material for the treatment process to recover paper fibers. In further steps, the results generated will be integrated, for instance, into the future development of the minimum standard for packaging recycling. [ABSTRACT FROM AUTHOR]

Smart specification and verification of product geometry for manufacturing rely heavily on robust mathematical and computational support. This paper introduces statistical methods that are tailored to address the challenges facing the next generation of ISO GPS standards that govern geometrical product specification and verification. A key focus of this work is the handling of outliers, which are inevitable in industrial metrology. The paper begins by defining outliers based on established statistical literature, standards, and practices. It then explores the application of these concepts to the context of geometrical measurements in ISO GPS standards, while also considering the unique characteristics of structured multidimensional data under the Euclidean distance metric prevalent in this domain. By employing optimization theories and techniques in a unified formulation, the paper extends existing mean and rank-order statistical tools to present preliminary findings for structured data in multidimensions. Furthermore, it outlines potential future research directions concerning mathematical theories and computational techniques aimed at effectively applying median and rank-order statistics, and their incorporation into the future ISO GPS standards. [ABSTRACT FROM AUTHOR]

This paper's goal is to give an overview to tolerance analysis. The cost and quality of a product can be significantly impacted by decisions about tolerating. Increased focus on tolerance design is required if high-precision assemblies are to be produced at cheaper costs. After a component's tolerance has been determined, tolerance analysis looks to propagate the manufacturing imprecisions (geometrical defects) and to validate the value of the functional requirements. The mathematical modelling is absolutely necessary for this validation. Models for tolerance analysis are discussed in a lot of literature. There are summaries of the current state of the art, the most recent advancements, and the upcoming trends in tolerancing research. From a mathematical perspective, this paper provides a taxonomy of mathematical concepts for tolerance analysis. [ABSTRACT FROM AUTHOR]

The ISO Technical Committee 213 (TC 213) develops standards about geometrical product specifications and verification (GPS&V). Those standards define both the meaning (semantic) and the writing (syntax) of specification indications used in Technical Product Documentation to control the size, geometry and surface texture deviation allowance on a mechanical part. The specification indications are defined as graphical symbols whose dimension and proportion are described in different ISO standards. The arrangement of those symbols is standardized so that reliable if not univocal information can be conveyed from the writer of the specification indication to the readers of the specification indication. This set of standardized rules constitutes a Graphical Language, primarily developed for humans widely used on two dimensional drawings or in three dimensional models. However, since the specifications are becoming more and more complex it is foreseen that the machines will be more involved in the communication process of the geometrical tolerancing information in the future. This paper describes in some details a context-free grammar that has been written to test the feasibility of a machine-readable Textual Language development.The grammar is based on the Universal Coded Character set (UCS). It shall be able to convey the same information as the current Graphical Language defined in ISO standards. The paper also reports on some testing tools that have been developed and made publicly available through a web interface. While this paper is only concerned with the syntax of the specification indications, some proposals are presented for an extended use of the grammar to check also semantic rules. [ABSTRACT FROM AUTHOR]

The reflective optical mirror group is the core component of precision optical instruments, and their assembly accuracy will directly impact the imaging of optical instruments. In recent years, with the rapid development of digital twin technology in the field of assembly, the application of digital twin to assembly process optimization is currently a research hotspot. This paper proposes an assembly process optimization method for a reflective optical mirror group based on an accurate digital twin model. By accurately simulating the geometrically distributed errors generated by machining and the physical properties of the assembled parts of the reflective optical mirror group during assembly and use, an accurate digital twin model is established to accurately characterize the geometrical and physical properties of the reflective optical mirror group. Using the accurate digital twin modeling method, the mirror deformation and stress distribution of a reflective optical mirror group are accurately predicted, and the main process parameters in the assembly process are optimized according to the prediction results, so that the target value of the imaging phase of the mirror group after assembly is improved by 24%, and the encircled energy is improved by 15.1%. The optimization calculation method proposed in this paper can obtain the optimized assembly process parameters more accurately, and the research results provide effective support for improving the assembly accuracy and performance of optical mirrors and precision digital twin modeling research. [ABSTRACT FROM AUTHOR]

Precision assembly process planning technology is one of the major challenges in the field of assembly. Compared with general assembly process planning, precision assembly process planning needs to consider a more complex assembly process, higher precision requirements, and more difficult process control. For this reason, it is of great significance to implement an intelligent assembly process planning and decision-making method for the development of precision assembly technology. This paper realizes the intelligent planning of precision assembly processes through the decision-making route of knowledge modeling and knowledge reasoning. Firstly, a theoretical system of a knowledge-driven precision assembly process decision-making framework is proposed. Secondly, a key precision assembly process decision-making technology is described in detail, which introduces a new approach for knowledge updates based on matching rules. Finally, the effectiveness of the knowledge update method is demonstrated through experimental cases. The knowledge-driven decision-making method combines the accuracy of domain experts' empirical knowledge with the efficiency of intelligent reasoning algorithms. The knowledge update method ensures the currency of knowledge used for decision making. Therefore, the proposed precision assembly decision-making system framework and knowledge updating techniques in this paper are of great significance for knowledge-driven precision assembly process planning. [ABSTRACT FROM AUTHOR]

The deployment of machine learning models in manufacturing systems presents unique challenges, necessitating robust testing procedures to ensure reliable and efficient operation. This paper proposes an automated testing framework specifically designed to address these challenges, focusing on verifying the correct utilization of data sources, validating model functionality, and assessing the compatibility of the target machine with the deployed model. By automating the testing process, this framework aims to enhance the reliability and effectiveness of machine learning model deployment in manufacturing systems. Through a comprehensive literature review, the paper explores existing methodologies and identifies gaps in current practices. The proposed framework incorporates various test types, including unit tests, integration tests, regression tests, and performance tests, each tailored to the specific requirements of manufacturing systems. Experimental results demonstrate the framework's effectiveness in detecting errors and failures during the deployment process. Overall, this research contributes to advancing the field of machine learning deployment in manufacturing systems and provides practical insights for practitioners seeking to optimize the reliability and efficiency of their deployed models. [ABSTRACT FROM AUTHOR]

This paper addresses the limitations of camera-based random bin picking (RBP) strategies by proposing an innovative strategy that relies solely on a 6-axis load cell instead of cameras and image processing algorithms. Specific applications of RBP include loading and unloading industrial assembly machines, transferring objects between industrial workstations, and picking objects to be assembled directly. Camera-based RBP systems are limited in industrial settings due to challenges related to image processing robustness, high costs of hardware and software, the need for 3D models and extensive training datasets. These limitations are inherently tied to the reliance on camera-based technology and image processing AI. This paper presents a novel camera-less RBP strategy, eliminating the need for visual information processing altogether and circumventing these limitations. The strategy is largely agnostic to factors such as bin size, object material and gripper size, and as such can be modularly configured to the user's needs. This strategy is demonstrated using a collaborative robot, highlighting the advantages in a collaborative environment. The proposed strategy is evaluated through feasibility case studies on several objects of different geometries, namely navel oranges, cardboard boxes and resin bottles, and performance metrics are benchmarked. The strategy has an average success rate of 99%, average cycle time of 26.08 seconds per object and an average grip success rate of 47%. [ABSTRACT FROM AUTHOR]

Today there are many ideas investigated to find ways for industries to become more environmentally friendly and sustainable. To reduce the energy consumption of machine tools and thus increase the efficiency of production, this paper discusses the idea of a heat accumulator based on a phase change material (PCM). In the context of biologisation in production technology, this work is based on the principle of heat storage in equi-warm organisms. Since machine tools deform under thermal influences and thus affect the machining process, they are often operated in idle mode before the work process to reach a thermally stable state and reduce thermally induced deformation during the production process. The machine is actively tempered using the cooling circuit to keep the temperature plateau constant. The idea of the PCM unit presented in this paper is to store this excess and otherwise dissipated energy. This can be used before the machines next process cycle to induce heat into the machine and reach the stable temperature plateau without operating the machine. The conditions under which the energy consumption can be reduced using this method and thus making the operation more sustainable are discussed and first simulations show that the goal is achievable. [ABSTRACT FROM AUTHOR]

In automated material flow, in a wide variety of areas, the primary goal is usually to handle a wide spectrum of components as time- and cost-efficiently as possible. In view of the current and future challenges in industrial production, it is becoming apparent that ecological requirements are becoming increasingly important in automation solutions. For example, in form of resource efficiency, transformability and material efficiency. In this context, especially materials handling technology is subject of various optimization approaches, as no value is added to the part handled. The question: "How does material flow occur in nature?" thus offers biologically inspired approaches to thinking about transport in the industrial sector. This paper first presents a selection of concepts or existing mechanisms that are adaptable in materials- handling technology and have been developed based on a biological model. In the second part of this paper, a new concept is presented that is modeled on peristalsis as a transport mechanism. The approach presented here uses tensegrity-structures for assembly, which are characterized by their high material efficiency and flexibility. The transport movement is achieved by peristaltic typical contraction or relaxation of the respective structure parts. [ABSTRACT FROM AUTHOR]

Biological transformation represents one of the most promising optimization strategies for a sustainable economy. The rapid convergence of biotechnology, information technology and production technology provides a vast area of innovation. From an economic and innovation policy perspective, cooperation structures and value creation systems will change significantly in a biointelligent transformation process. In the future, many industries will produce personalized products in a much more decentralized manner based on locally provided, renewable resources using autonomous non-expert systems. Such a profound change affects not only products and processes but also the organization of companies and how innovations are evaluated and implemented. In this paper, we thus discuss the need for a reorientation of innovation management in companies in the context of biological transformation. This article focused the central question of how the innovation management can change in the context of biointelligence so that companies can be helped to invest strategically in biointelligent innovations. We then outline the basic elements of a framework and discuss key challenges for future research and development. In this paper, we present initial thoughts on a framework for the management of biointelligent innovation that aims to solve the complex requirements of BioIntelligence. Our approach is based on a holistic view of different methods and techniques to provide a comprehensive solution to the challenges. We discuss the basic concepts and goals of this framework as well as potential application areas and challenges. Furthermore, we present first approaches for implementation and evaluation. [ABSTRACT FROM AUTHOR]

This paper is aimed at the application of frugal engineering methods for enhancing the resilience of manufacturing supply chains. The frugal engineering methods are presented by discovering the cross-section of biological designs and equitable social innovations. Multiple global trends such as urbanization, climate changes, pandemics, growing global population, and others pose serious challenges to resiliency in manufacturing enterprises including supply chains. These challenges call for the development of novel and 'out-of-box' approaches to solutions. Biology achieves the co-existence of millions of species across varying habitats and disturbances for survival while maximizing the available resources through the utilization of frugality or frugal engineering principles. Strategies applied in biology deliver a collective balance for multifunctionality, resilience, and frugality while balancing the trade-offs between efficacy, effectiveness, and efficiency. Frugality in this context is defined as the philosophy to minimize the use of available resources while maximizing the productive outputs. This concept of frugality has been increasingly used over the past few years, especially in business and sociological contexts to deliver social innovations. The paper discusses examples from biology and social innovations and critical analysis of the example case studies to propose a framework for frugal engineering-driven strategies for supply chain resilience in manufacturing. A hybrid model is proposed for a regionally resilient distributed network of micro supply chains that complement the giga-global supply chains. [ABSTRACT FROM AUTHOR]

This paper describes and discusses the engineering knowledge, mainly production engineering knowledge, which is expected to be used for surgical support systems in the near future and beyond. With regard to artificial joint replacement, the paper discusses bone resection characteristics, focusing on the experimental results of cutting force and temperature rise. In addition, registration techniques, and measurement and resection accuracy are described. In the area of microsurgery, neurosurgery and ophthalmology are covered, and the introduction of AI is also introduced. In ophthalmology, the automation of peeling procedures is discussed. In remote surgery, experimental results on the relationship between force feedback and time delay, which is a crucial problem in remote surgery, are introduced. Furthermore, as future surgical support systems, the application of high intensity focused ultrasound (HIFU) and the use of microrobots are discussed. Finally, this paper describes the situations in which surgical robots are expected to play an active role. [ABSTRACT FROM AUTHOR]

This paper investigates a Life Cycle Assessment (LCA) based methodology to determine baseline emissions for an aerospace manufacturer. Aerospace manufacturing entails high complexity and low throughput of a vast array of components. Rather than establishing the environmental footprint through waste, electric, and gas data alone; an LCA cradle-to-gate approach extends to a product's raw material acquisition and shipment to provide a comprehensive set of environmental performance indicators. The approach combines several products that follow a similar manufacturing process and can be denoted as a product family. This paper discusses the ability that LCA product families must develop baseline emissions for products validated with a case study of an aerospace component. The methodology can be extended to other product families manufactured within the facility which when combined will accumulate to a site-wide environmental footprint. The paper further evaluates how this methodology can identify environmental hotspots at a process and product level. The aerospace component case study incorporates several manual and automated stages. This work aims to demonstrate the ease of determining baseline emissions using an LCA product family and enable aerospace manufacturing companies to adopt a similar approach to establishing environmental hotspots. This can drive strategic internal change for sustainable manufacturing aligning with company environmental, social, and financial frameworks. [ABSTRACT FROM AUTHOR]

The increasing market share of electric vehicles leads to a growing demand for raw materials such as lithium and cobalt, where the supply situation is fraught with risk. Product-specific challenges regarding high voltage, product state/variance, and labor shortage require flexible automated non-/ semi-/ destructive disassembly. However, there is a need to implement industrial, scalable automation approaches to deal with this problem. This paper addresses the development of a flexible robotic cell for the fully automated disassembly of battery modules from battery systems. The paper presents all required tools and processes for battery diagnoses, machine learning-based object recognition, loosening and removing fasteners, opening sealings, gripping components, separating cables and plugs, and removing the battery modules. Based on this, a sorted disposal/recycling of the material can be realized. Finally, the holistic robot cell, including a safety concept, is described using the example of an actual disassembly process. [ABSTRACT FROM AUTHOR]

The global discourse surrounding plastics has been marked by a profound perceptual schism, also for plastic packaging in the fresh food industry. The public opinion expresses mounting concerns in terms of such plastic packaging solutions. However, in many cases the unique material properties and the well-established methodology of Life Cycle Assessment (LCA) actually demonstrate the environmental advantage of plastics for food packaging. This paper delves into the chasm between the two perspectives, leveraging empirical evidence to resolve the divide. While performing both a consumer analysis and LCA, this paper underscores the potential for innovative, yet practical design solutions to harmonise the public opinion with counterintuitive positive environmental impacts. A transformative design solution that centres around the concept of shape and material renewal is proposed. It demonstrates how a simple, yet effective redesign can enhance both the environmental impact and consumer acceptance in the industry for mass-produced fresh food packaging. It emphasises the role of LCE in design, with a focus on the pre-consumer phase. The findings provide a practical approach, emphasising the need to reconcile theory with consumer desires, to forge a sustainable path forward in packaging design. [ABSTRACT FROM AUTHOR]

In the recent decades, sustainability has emerged as a crucial concern for governments and industries worldwide. While the political sphere appears to take the lead in the pursuit of a greener world from a societal standpoint, industries play a pivotal role in implementing innovative technologies. Certain industries, however, still heavily rely on natural gas consumption for their operations. Metal manufacturers, in particular, face significant energy demands due to their melting processes, which have historically necessitated the use of combustion. This paper focuses on examining the current workflow of secondary aluminum manufacturing, with a specific emphasis on rolled sheet production. By analyzing combustion intensive equipment involved in the process, the paper identifies inherent deficiencies and energy losses throughout the production process and propose strategies that can effectively reduce the industry's global carbon footprint. Results show that optimizing work flow to retain embodied energy and heat of intermediate products, as well as smart waste heat reuse can result in significant savings in secondary aluminum manufacturing. [ABSTRACT FROM AUTHOR]

Sustainable development should meet the needs of the present without compromising future generation needs. While political and standards approaches insist on regulatory directives, this paper proposes a quasi-satisficing methodology considering the sustainability topic within a larger encompassing framework. Starting from three reflections originated by the classical thermodynamics, the effect of knowledge accumulation on the Production Possibility Frontier (PPF), and a model for the human wellbeing in manufacturing ecosystem, this paper proposes a new production model and a heuristic approach to drive the decision process towards sustainable manufacturing. [ABSTRACT FROM AUTHOR]

To implement environmentally sustainable, resource-efficient manufacturing, the quantification of the environmental impacts is a central task. This task requires suitable methods. Various lifecycle phases, including remanufacturing concepts, and the associated increasing complexity demand a change from static to dynamic assessment methods. Flexible changes must be considered. Thereby, data quality has a major impact on the subsequent assessment results. Though the available data amount in a manufacturing environment is constantly increasing, the influence of data quality on the respective method – and thus, the reliability of the results – remains a key issue. To address this problem, this paper investigates the necessary developments regarding data quality for environmental assessment methods in dynamically changing manufacturing settings. First, relevant data quality dimensions are identified. Subsequently, a cross-comparison of data quality dimensions and different approaches of environmental evaluation in manufacturing is developed. The necessary changes to data quality requirements in operational environmental assessments are derived. Finally, a concept for the improvement of data quality in dynamic assessments is proposed and discussed based on the use case of battery modules' remanufacturing. This paper contributes to the development of environmental assessment methods that are evolving from static, historical data-based evaluations to dynamic assessment tools of flexible productions. [ABSTRACT FROM AUTHOR]

Over the last years two factors have deeply affected research in Manufacturing: the growing interest around Additive Manufacturing (AM) processes and the need to reduce the anthropogenic environmental impact. As result, a large papers concerning the environmental impact performance of AM compared to conventional processes have been published. Specifically, very complex models accounting for the impact of each life cycle stage of AMed components have been released. Results revealed that AM, at present, guarantees energy savings only within some domains, and the potential saving depends on several factors: product complexity, eco-properties of the material, energy intensity of the material deposition, light-weighting enabled by AM approaches and extent of the use phase. Above all, the result of the comparative analysis depends both on the considered factors and on the selected system boundaries. The already proposed models are very complex and many inventory data are needed, this could make them unapplicable at industrial level. In the presented paper, simplified models are proposed and the performances of these are quantified with varying the analyzed scenario (considered factors and selected system boundary). Results revealed that, for given scenarios, simplified models characterized by low computational effort, can provide reliable results. Guidelines for the implementation of different models with varying the system boundary are provided for the cumulative energy comparison of Electron Beam Melting and conventional turning for the production of titanium alloys components. [ABSTRACT FROM AUTHOR]

This paper discusses the first development of the industrial PhD project research project sponsored by Simonelli Group at Politecnico di Milano, aimed to develop and patent a heating system technology that enhances the energy efficiency of professional coffee machines. This paper crosses the issue of the sustainable design of products through a qualitative life cycle assessment on three professional coffee machines of Simonelli Group. The LCA analysis considers the manufacturing, use, and disposal phases. The analysis was performed using the Simapro software that integrates the Ecoinvent3 database. The life cycle impact assessment is carried out in accordance with other case studies in the literature, relying on the parameter "GWP" in accordance with the IPCC method. Laboratory tests are used to assess the kWh of energy consumed during the use phase of professional coffee machines. Following that," use phase scenarios" were created for the life cycle impact assessment, based on existing scenarios in the literature, and elaborated with the company's marketing support. The findings indicate that the most critical environmental phase is the use phase and that the heating elements are the most energy-consuming items in professional coffee machines. Click here and insert your abstract text. [ABSTRACT FROM AUTHOR]

Industrial technologies have evolved towards circular-oriented manufacturing. New approaches in the management of production systems are needed to guarantee the success of such a circular approach. This paper identifies three research questions related to the complexities of decision-making within CEs and defines the concept of Green Gateways in the circular economy. A Green Gateway is a type of decision that supports to realizing the full potential of products and materials. Indeed, the potential value of products and materials must be assessed and leveraged and the concept of Green Gateway will be useful to identify common decisions and to define a common framework in the future. The integration of digital information and digital twins of products, processes, and circular value and supply chains emerges as a key factor in guiding decision-makers effectively, especially for Green Gateways where adequate information, tools and methods must be used to manage value retention options, product flows, production and pricing decisions jointly with the multiple objectives of profitability and sustainability. Additionally, this paper explores specific examples of circular economy use cases, drawing attention to their similarities and highlighting key insights. [ABSTRACT FROM AUTHOR]

Current challenges such as climate change, the limits of natural resources, and increasing customer-specific sustainability demands require a transformation of value creation. The aim is to move from global sourcing to more local value creation by creating synergistic energy and circular material flows and making better use of capacities while taking urban structures into account. This paper discusses an approach of Smart Glocal Production that combines spatially networked production in global enterprise networks. Based on a literature review, examples of Smart Glocal Production in Germany and Austria are presented. The core of the paper is a novel assessment approach for manufacturing companies to determine their readiness level for glocal production. On this basis, smart measures can be derived with special consideration of digitization initiatives to support manufacturing companies on their way to a more sustainable value creation in industrial areas. [ABSTRACT FROM AUTHOR]

The upscaling of technology encompasses many facets a designer concerned with reducing environmental impacts must manage. Therefore, previous research proposed an environmental assessment for upscaling (EAU) methodology. This method hinges on Life Cycle Assessment (LCA) approaches (e.g., prospective, absolute) and coordinates the environmental modelling from an ecodesign perspective. The originality of this paper is to present an illustrative application on photovoltaic systems. The focus is on the modelling practices (step 3 of the method) using upscaling design levers and generating parametrized scenarios meant to be interpretable for a design team. Examples of data collection and upscaling modelling are provided for the upscaling of silicon-based photovoltaic technology in France from 2021 to 2050. This case study shows that integrating upscaling parameters, such as industrial process evolutions, the technology deployment strategy, and the socio-technical context associated with the technology upscaling phenomenon influence LCA results significantly. The paper, therefore, discusses recommendations for design teams to support them in assessing the environmental implications of their technology choices. [ABSTRACT FROM AUTHOR]

With Industry 5.0 on the horizon, research and innovation is prioritising the transition to sustainable, human-centred and resilient manufacturing. In this context, Zero Defect Manufacturing (ZDM) strategies combine tools, resources and rules with the aim of avoiding defects and increasing the sustainable performance of complex manufacturing systems. Five distinct strategies for ZDM have been identified in the literature: (I) detection, (II) prediction, (III) prevention, (IV) repair, and (V) mitigation or compensation. The main motivation of this paper is to discuss an extension of these five existing dimensions by three more, which will enable the transformation of classical ZDM towards a more sustainable production. The main objective of this paper is to conduct a systematic literature review that covers the state of the art of ZDM and highlights the need to add the three (Zero3) dimensions: i) Zero Resource Loss (resource and emission improvement) ii) Zero Human Potential Loss (productivity and stability improvement) iii) Zero Data Gap (increase in efficiency of data use). [ABSTRACT FROM AUTHOR]

The push to decarbonize has spurred the demand for clean energy technologies such as electric vehicles (EVs) and wind turbines (WTs). These technologies rely on rare earth permanent magnets (REPMs), namely Neodymium-Iron-Boron (NdFeB) magnets that in turn rely on rare earth elements (REEs), including Neodymium (Nd), and Dysprosium (Dy). As the demand for clean energy technologies increases, so will the demand for Nd and Dy-containing REPMs. Both Nd and Dy are critical elements that are prone to supply chain risks. As our reliance on them increases, it becomes essential to anticipate future market dynamics for these elements. This paper aims to address the current gap in the literature in the context of forecasting future Nd and Dy demand quantities. The Bass Diffusion Model is a widely used approach to forecast the adoption of new products or technologies and, to predict demand trends. Over the years, the model has found applicability in a wide variety of sectors including consumer durables, medical services, and agricultural innovations. This paper demonstrates the use of the Bass Diffusion Model to estimate future global Nd and Dy demands, enabling a better understanding of swiftly evolving clean energy technology and REPM-related market dynamics. [ABSTRACT FROM AUTHOR]

The problem of effectively managing Configuration Lifecycle Management (CLM) processes for customizable products in the manufacturing industry presents challenges in striking the right balance between product customization and standardization, particularly for complex products like wind turbines. This paper addresses this issue through a comprehensive analysis and enhancement of CLM processes in a case company that manufactures, install, and service energy solutions focusing on wind turbines. This paper addresses the challenges in CLM processes through a comprehensive analysis and enhancement within a case company specializing in the manufacturing, installation, and servicing of energy solutions, with a specific emphasis on wind turbines. Specifically, within the CLM framework, the study delves into the Input Signal Management (ISM) process, addressing challenges associated with handling specific requirements, features, or functionalities that customers or stakeholders want in the product. The study maps the current state of the processes, identifies weaknesses, and proposes two suggestions to enhance the so-called Input Signal Management (ISM) process. The ISMs are the specific requirements, features, or functionalities that customers or stakeholders want to be included in the product. The challenge in the ISM process lies in handling a large number of requests, ensuring consistency, avoiding conflicts, and making timely decisions. Hence, the paper proposes two key enhancements to the process: the development of guidelines for data creation and categorization of ISM requests, and the prototyping of a Natural Language Processing (NLP)-based system for expedited request assessment. The paper emphasizes the significance of optimizing CLM processes for effective configuration management. Incorporating data-driven methods, the ISM process can become agile, efficient, and customer-centric, leading to better management of customization, reduced response times, and increased customer satisfaction. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), often referred to as 3D printing, is a manufacturing technique that involves the creation of complex objects layer-by-layer while providing design flexibility. The broad spectrum of additive manufacturing applications in aerospace, medicine, automotive industries, etc., using high-value materials necessitates a precise final product with minimal wastage. However, the print's dependence on optimizing parameters such as nozzle diameter, printing speed, extrusion speed, nozzle temperature and other multifaceted factors can contribute to deviations from the intended design. This paper introduces a closed-loop in-situ assessment of layer height deviations using point cloud data to ensure real-time printed part modification. The printing is done using a direct-fed pellet screw extruder mounted on top of 6 degrees of freedom robotic arm. A structured light 3D scanner is used to obtain point cloud data. A Robot Operating System (ROS) based digital toolchain is employed to have seamless communication between all nodes of the AM system. The layer-by-layer printing is observed using an in-house CAM solution, which examines the point cloud data and compares it to the original CAD model. The deviations are analyzed, and the path of the next layer is re-planned to adjust for the digression in the previous layer. This control loop ensures that the final product meets the desired quality standards at minimum cost and efficient time and increases the opportunity for rapid prototyping and iterative design improvements. [ABSTRACT FROM AUTHOR]

Tolerancing is important task at design stage to ensure products quality. It is predicted that tolerancing method will change in accordance with the development of computer technology in recent years. Tolerance analysis using skin model shapes, which is parts model with form defects, will be one of the new method. If skin model shapes are generated randomly with both tool and cutter marks caused in machining processes, both designing and manufacturing parameters will be determined rationally based on results of the new tolerance analysis. For this purpose, an appropriate method to generate skin model shapes are required. In previous study, we have proposed a generation method using dual-tree complex wavelet transform to generate skin model shape randomly. The method has three parameters to be tuned for any surfaces, which has characteristics such as both tool mark and cutter mark. To generate more realistic skin model shapes, the parameters should be optimized corresponding to any surfaces. In this paper, the optimization method is developed using genetic algorithms. [ABSTRACT FROM AUTHOR]

Mechanical engineering drawings are commonly used in multiple industries, carrying essential information about the design and technical specifications of the parts they define. With large industry sectors shifting to advanced manufacturing processes and technologies such as additive manufacturing, arises the need for systems checking whether legacy parts described by engineering drawings can be optimally produced. To this end, the digitisation of engineering drawings has become the key issue for the following computer-aided engineering tasks towards advanced manufacturing. This paper presents a pipeline for information extraction in raster mechanical engineering drawings through a combination of traditional and deep-learning-based computer vision techniques. Object detection and text recognition techniques are implemented to achieve automatic interpretation of engineering drawings. A dataset of 217 industrial engineering drawings is created to evaluate the proposed method. Different types of information within engineering drawings, including information blocks, views, dimensions, and Geometric Dimensioning and Tolerancing (GD&T) information, are extracted automatically. A case study of engineering drawing digitisation for a complex part is presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The digitalisation of education has developed in many areas in recent years with applications like web-based training and virtual seminars. In technical teaching areas, however, they are often only available as stand-alone solutions. Due to the possibilities brought about by the rapidly progressing development of artificial intelligence, the potential for digital teaching must be recognised and used. It is necessary to establish demand-oriented, intelligent control loops that support communication, knowledge transfer and learning level control. This will enable future generations to learn individually and thus increase the efficiency and sustainability of the teaching-learning process. As part of a current research project, a multimedia study format for the field of production measurement technology is being designed at Chemnitz University of Technology. A virtual tutor is an innovative element that guides, reminds, supports, informs, collects points and motivates learners individually with gamification elements. This paper presents the requirements, implementation possibilities and transferability of the current research of virtual-tutor-supported teaching to Geometrical Product Specification. It shows how knowledge elements of the ISO GPS system are linked with new digital teaching-learning methods that promote interaction between the participants and especially the transfer into applicable knowledge. In addition, examples of gamification elements are presented. [ABSTRACT FROM AUTHOR]

The ISO 17450-2:2012 defines the verification operation. The ISO 17450-1:2011 defines the extracted features and the extraction operations on the "non-ideal surface model or on the real surface of the workpiece". The ISO 12781-2:2011 and 12180-2:2011 provide dedicated extraction strategies for the flatness and cylindricity. But no real measurement process is explicit here. However, one of the key factors in an inspection plan is the choice of the measurement strategy. Some sampling strategies, such as Hammersley sequence, offer robust and efficient technics to ensure good distribution of measurement points. But measurements are always defined with a "blind sampling strategy". So, in this paper, the author proposed to use adaptative sampling strategies, based on the Active learning method based on Kriging for the Inspection of Large Surfaces (AK-ILS) and developed an original Active Learning method based on Multi-Layer-Perceptron (AL-MLP), to classify the conform / non-conform parts and to ensure the reconstruction of the surfaces, with smart Skin Models Shapes and a minimal sampling size. [ABSTRACT FROM AUTHOR]

The dependencies between process parameters and the resulting geometrical accuracy of additively manufactured parts are usually highly non-linear and thus complex to investigate and mathematically quantify. Therefore, the application of artificial intelligence techniques is promising to generate mathematical models that reduce effort and increase the prediction quality. The overall goal is to establish a procedure to automatically determine the optimal settings of the manufacturing process parameters to guarantee the highest geometrical accuracy of parts in additive manufactured production. This paper presents the first step towards this fully automatic procedure – the training and evaluation of a mathematical model based on artificial neural networks to quantify the effects of varying process parameters of a material extrusion process on both macro- and micro-geometrical performances. Therefore, a dataset is established based on the Design of Experiment of an additively manufactured part made from Polylactic Acid filament. The dataset is then used to train an artificial neural network that predicts the dimensional and micro-geometrical deviations of the manufactured parts. Finally, the evaluation of the network's prediction quality and reliability indicate that it is possible to predict the parameters linked to resulting print quality with a mean absolute error from 0.0004 to 0.036. [ABSTRACT FROM AUTHOR]

Metrology is a key tool for tolerancing as it is used to determine whether dimensions are within their tolerance intervals. However, measurement errors cannot be avoided and need being accounted for. The probabilistic approach is applied to both the dimensions and their measurement errors; they are modelled as random variables and characterized by their probability density function. The probability density function of the measurement error is assumed to be known; this work is included in a research project in collaboration with a metrology company, where the engineers are able to provide us with this information. This paper describes a strategy to account for such measurement errors and (partially) correct or mitigate their effects. Through Bayesian inference, the likelihood of true values given measured values is estimated, allowing for a probabilistic correction. The proposed method is applied to numerical examples with simulated data and its relevance is discussed. [ABSTRACT FROM AUTHOR]

Calcium fluoride (CaF2), as a typical brittle optical material, has been applied in various fields due to its excellent optical properties. However, an existing challenge is how to non-destructively characterize the surface integrity of brittle materials, which may easily suffer from subsurface damage (SSD) during machining processes. This paper explores a novel snapshot surface-integrity test method based on single-exposure back-focal-plane (BFP) imaging of CaF2 within ellipsometric parameter spectra corresponding to an extensive range of incident angles. The relationship between the layer thicknesses of surface roughness, SSD, and ellipsometric spectra was analyzed by establishing a four-layer-subsurface optical model combined with quasi-Brewster angle technology. Through polishing CaF2 under different conditions and combining the measurement results of angle-resolved ellipsometry (ARE), it has been demonstrated that the study based on ARE is a successful non-destructive testing method for surface integrity, which could help to further elucidate the surface evolution mechanism of brittle material polishing processes. [ABSTRACT FROM AUTHOR]

The mechanical joining process clinching is becoming increasingly important, especially for multi-material car body elements enabling lightweight design. However, the design of these joining points is usually based on expert knowledge and time-consuming experiments. For this reason, simulations are used in combination with surrogate models to predict the characteristics of clinch points for specific process parameters. Thereby, the process parameters are assumed to be ideal, so that inherent uncertainties are currently neglected. In order to overcome this deficiency and ensure a realistic prediction of clinch point characteristics, a consideration of variations is inevitable. Therefore, this paper analyses the transferability or significance of the nominally predicted values of existing surrogate models for clinch joints, taking into account process parameters with unavoidable variations. This shows, that the nominal parameter configuration affects the scattering of joint characteristics when considering variations. Moreover, implications for the necessary additional sampling in order to increase the significance of existing (nominal) surrogate model predictions are given, while considering the additional computational effort involved. [ABSTRACT FROM AUTHOR]

Spatial data fusion algorithms are widely applied in dimensional metrology for quality assessment or surface reconstruction. Multi-sensor point cloud fusion combines the advantages of multiple sensors by merging their measurements into a single coordinate system and reducing the prediction uncertainty and systematic errors. Algorithms designed for these tasks employ many methods that require thorough evaluations through a common framework. To address this need, this paper proposes a framework for simultaneous registration and approximation, and introduces a reference data generator for unbiased evaluations of data fusion algorithms with heterogeneous and anisotropic noise assumptions for applications involving multiple sensors. The bias for the generated reference data is evaluated close to floating point accuracy, which validates the generation method, and uncertainty evaluation on ICP variants reveals that reference data is more suitable to evaluate point cloud fusion algorithms. The proposed framework and data generator allows developing and validating more accurate data fusion algorithms. [ABSTRACT FROM AUTHOR]

Partitioning is essential in the smart verification process when measurements are conducted using modern instruments, such as a CT scanner. Numerous partitioning methods are commonly designed for scenes rather than for geometrical products. This paper introduces a novel method to partition geometrical elements from the measured data of the workpiece containing geometrical deviation and refines the points attribution problem at part boundaries. Firstly, a partition algorithm based on the coarse boundaries is presented. Then, a boundary refinement process is proposed to improve the partition result. A measured data set is taken as an example to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Sampling-based tolerance analysis is a powerful tool for evaluating the quality of functional products, but requires realistic manufacturing distributions. However, the determination of resulting manufacturing distributions is usually associated with a high financial and time expenditure, especially for novel technologies such as Additive Manufacturing. Usually, sampling techniques are used to reproduce the original distribution of manufacturing variations based on statistical moments. In most cases, simplifying assumptions are made for this purpose, potentially leading to an inadequate representation of the correlation between machine and process parameters in the resulting distribution. In the worst case, this can lead to a falsification of the tolerance analysis results. Aiming to address this challenge, this paper presents an approach to imitate real-world manufacturing distributions using generative Machine Learning techniques based on Deep Learning with small real data sets. This enables a realistic reproduction of quasi-real manufacturing distributions and omits conventional sampling techniques. The general procedure and its applicability are shown via illustrative use cases from the tolerancing domain. [ABSTRACT FROM AUTHOR]

Accurate modeling of geometrical deviations is the premise and critical step for tolerance analysis. Traditional tolerance modeling methods usually simplify deviations to variations in degrees of freedom. Although this facilitates the uniform representation and accumulative calculation, it is not possible to consider the effects of real topography over mating interfaces on tolerance analysis in high-precision scenarios. In this regard, this paper proposed a tolerance modeling method for representing anisotropic rough surfaces information under the framework of Skin Model Shapes. Specifically, the spatial information on rough surfaces is represented by autocorrelation functions. A set of nonlinear equations are constructed according to given autocorrelation functions and then solved to obtain the desired surface topography. In case studies simulated rough surfaces with different input parameters are illustrated and the consistency of this method is verified. The proposed method provides an accurate and practical way to represent anisotropic rough surfaces for tolerance modeling and analysis. [ABSTRACT FROM AUTHOR]

In their works, in 2014, N. Anwer & al. introduced the concept of Skin Model Shape (SMS). Their main idea was to propose parts representations with geometric deviations in the discrete geometry framework for CAT. In this paper, an application of the SMS to a full product/process tolerancing assembly analysis process of composite parts is proposed, with a numerical workflow between softwares. The RTM process simulation of composite parts, with systematic shape distortions estimation, is realized with PAM-RTM/DISTORTION®. The product/process uncertainties propagation ensures to capture the random geometrical variations, using OpenTurns®. The assessment of the tolerances is done with virtual coordinates measurements, from extracted nodal points of the FEA meshing. The final effects of the tolerances of the parts are then measured on a mechanical assembly by a tolerancing stack-ups analysis, using MECAmaster®. [ABSTRACT FROM AUTHOR]

The skin model serves as a foundational concept defined in ISO standards on Geometrical Product Specifications and verification (ISO GPS), facilitating the consideration of geometric variations on the surfaces of mechanical products. However, the lack of a solid scientific foundation and practical operational applications has become a barrier for skin model concept to keep up with the development of standardized feature operations. A previous investigation was devoted to a scientific approach to defining and interpreting the skin model for ISO GPS, grounded in the structure of solids and set theory. In this paper, the use of the Laplace-Beltrami Operator (LBO) is explored as a mathematical operation to enhance the computational representations of the skin model. The describability of skin model is achieved by applying LBO in both continuous and discrete cases. Geometric filter banks, established through LBO, are employed for skin model to identify defects at various scales of observation. An application of LBO is demonstrated through a case study to highlight the use of established geometric filter banks for skin model. Further discussions show that the spectral analysis of a surface using LBO enables the characterization of skin model, contributing to the current and future standardization of geometrical product specification and verification. Finally, conclusion and future works are highlighted. [ABSTRACT FROM AUTHOR]

This paper presents the dynamic evolution of definitions and information related to ISO-conforming geometrical tolerancing (i.e., ISO GPS) and corresponding documents. It aims to elucidate the relationships between different specification types while analyzing the incremental information introduced at each stage. Special attention was devoted to the responsibilities associated with each added piece of information. Furthermore, inter- and intra-company communications of geometric specifications are differentiated. The presented study is based on the specification types introduced by ISO/TS 21619:2019: functional, manufacturing, verification, and contractual, each communicating specific, but obviously interrelated information. This highlights the evolving nature of information in technical product documentation. However, the hierarchy and interconnectivity of the specification types are still open tasks for standardization, research and practice. This study present the evolution and integration of geometric specifications within product development; therefore establishing a foundation for future research in geometric specification management. [ABSTRACT FROM AUTHOR]

This paper proposes a method for adapting the layout of flexible workpieces of various shapes, in order to reduce the difficulty of location design for personalized shapes. Different from the previous location design that relied on the experiences of engineers, this method establishes an optimization model that determines the number of datum points and the optimal solution based on the centroid characteristics of the workpieces. The optimization index of this model is to control the minimum deformation of the overall flexible workpieces and the minimum variance of the deformation at the maximum deformation point. Taking the characteristics of the workpieces as a priori constraints for location determination, this model obtains the optimal solution combined with finite element analysis. Finally, the effectiveness of the method is verified through a numerical example. [ABSTRACT FROM AUTHOR]

Geometric tolerance measurement certification of parts is a key factor in determining product quality, production cost and competitiveness. The existing certification schemes for tolerance measurement rely heavily on the experience and proficiency of surveyors, which increases the uncertainty of the certification results for tolerance measurement. In this paper, the ontology is introduced into the geometric tolerance measurement certification, which provides a feasible method for the automation and intelligence of the geometric tolerance measurement certification of parts. Firstly, the engineering semantics of the tolerance measurement and certification process is extracted from the part drawings, and the OWL ontology representation model of the product is established. Secondly, according to the expert knowledge in the field of tolerance measurement certification, the semantic Web rule language SWRL is used to extract, induce and summarize the terminology relations involved in tolerance measurement certification, and these relations are converted into inference rules. Then, according to the semantic model and relation rules established above, the geometric tolerance measurement certification scheme of parts is automatically generated, so as to construct the ontology knowledge base of geometric tolerance measurement certification of parts. Finally, taking reducer box hole as an example, the automatic generation process of box hole coaxiality measurement authentication scheme is demonstrated, and its effectiveness is verified. [ABSTRACT FROM AUTHOR]

Compared with traditional machining processes, additive manufacturing (AM) has received widespread attention in recent years because of its high degree of modeling freedom. However, due to the multiple manufacturing errors and complex physical state changes involved in the process, the geometric deviation on the AM part surface is a challenge for controlling product geometrical quality. To address this problem, data-driven machine learning (ML) techniques have been widely studied in product quality controlling. However, traditional ML greatly depends on the training sample data, and suffers the risk of violating physical mechanisms due to the lack of domain knowledge. In order to take the best advantage of domain knowledge, prior information and deep learning algorithm, this paper proposes a knowledge-integrated deep learning algorithm and constructs the geometric deviation prediction model of the AM part surface. After that, the method was verified with design of experiments. The results show that compared with the data-driven neural network (DDNN), the knowledge-integrated neural network (KINN) has fewer iterations during the training process, less sample data requirement and more accurate prediction results. [ABSTRACT FROM AUTHOR]

In mechanical engineering, the datum is essential in the tolerance specification, which impacts assembly and functional requirements. The standard mechanical parts, such as shaft and gear, already have a formalized datum reference frame (DRF) design process. However, non-standard parts still mainly rely on manual design. To this end, a knowledge-graph-based DRF reasoning method for non-standard parts is proposed. In this paper, an OWL 2-based knowledge model is constructed according to industrial practice. It combines empirical knowledge, knowledge graphs, and ontology reasoning methods to perform DRF generation automatically and precisely. The finite element method is introduced into this work to evaluate reasoned DRF. The effectiveness and feasibility of the method are demonstrated via a case study. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on the contour method to solve the problem of difficulty in measuring the wear state of diamond beaded wire during processing. The edge contour image of the diamond beaded wire was collected through the built machine vision measurement system. Building on this, a custom convolution kernel was used to extract the beaded corner feature information. Utilizing this corner feature information, the diamond beaded wire abrasive particle exposure height profile was constructed, revealing the wear status of the diamond beaded wire. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles (EVs) necessitates effective end-of-life management strategies for their battery packs. When pursuing a recycling strategy to recover valuable materials such as nickel, manganese, cobalt, and lithium, the first step in most cases is to dismantle the battery at least down to module level. The state-of-the-art disassembly of EV battery packs relies on domain knowledge and is carried out manually for the most part. The potential hazards inherent in the dismantling process and increasing numbers of end-of-life batteries, coupled with a shortage of skilled workers, call for a safer and more efficient approach to dismantling than is currently the case. However, due to the high variety of end-of-life battery packs that return to recycling companies and their high system complexity, fully automating the disassembly process is not economically feasible. Therefore, the paper introduces an approach to extract and transfer domain knowledge from human experts to an accessible and structured information model. This model is intended to serve as the foundation for the development of disassembly assistance systems capable of aiding human operators during manual disassembly tasks and, in the long term, enabling the automation of critical disassembly steps. Furthermore, a theoretical concept for a disassembly station is proposed that allows a multi-faceted data collection process by providing a human-machine interface and multi-sensory equipment. This process encompasses detailed analysis of battery components, manual disassembly procedures, and expert insights. It aims at integrating, categorizing, and formalizing the collected data into a machine-readable format that captures both the procedural and semantic aspects of battery disassembly and enables the digital representation of the product and process. [ABSTRACT FROM AUTHOR]