A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

This roadmap presents the transformational research ideas proposed by “BATTERY 2030+,” the European large-scale research initiative for future battery chemistries. A “chemistry-neutral” roadmap to advance battery research, particularly at low technology readiness levels, is outlined, with a time horizon of more than ten years. The roadmap is centered around six themes: 1) accelerated materials discovery platform, 2) battery interface genome, with the integration of smart functionalities such as 3) sensing and 4) self-healing processes. Beyond chemistry related aspects also include crosscutting research regarding 5) manufacturability and 6) recyclability. This roadmap should be seen as an enabling complement to the global battery roadmaps which focus on expected ultrahigh battery performance, especially for the future of transport. Batteries are used in many applications and are considered to be one technology necessary to reach the climate goals. Currently the market is dominated by lithium-ion batteries, which perform well, but despite new generations coming in the near future, they will soon approach their performance limits. Without major breakthroughs, battery performance and production requirements will not be sufficient to enable the building of a climate-neutral society. Through this “chemistry neutral” approach a generic toolbox transforming the way batteries are developed, designed and manufactured, will be created

From composite material technologies to composite products: A cross-sectorial reflection on technology transitions and production capability

Materials, since the dawn of time, have played a crucial role in the development of civilization. Pre-history ages are fundamentally characterized by the material humans mastered, while the transitions to new materials have always marked a different socio-technical order. In this work we are going to investigate a relatively new material class, composites, in order to explain the issues the industry is currently facing. We are going to discuss material in the context of developing products that take full advantage of the benefits that composites can offer. The main idea behind this work is to understand how composite material technologies create growth and how the properties of those materials influence production capability and manufacturability. This work is the result of the EPSRC Centre for Innovative Manufacturing in Composites Platform research in the UK. It started with the bold intention to go beyond conventional research in composite material and explore the mechanisms of industrial change and growth through material. An examination of cases from a diverse range of sectors, acted as a platform to initiate a conversation on the issues practitioners are facing when adapting their products, or processes, to composite technologies, or when moving from a craftsman approach to state-of-theart material and process technologies. This paper presents insights from a sector/market agnostic point of view to probe the socio-technical considerations related to the diffusion of manufacturing innovation concerning composites and their production capabilities.The paper makes three main contributions. First, it presents a discussion on the capability issues regarding composites. Second, it presents empirical evidence on industrializing in composite material technologies. Finally, building on empirical evidence and previous literature, it describes the feedback loops during the composite product development process. The paper concludes with a reflection on current theories of innovation management on composite material technologies

AI-based design methodologies for hot form quench (HFQ®)

This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits. To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality. The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces

Lithography parametric yield estimation model to predict layout pattern distortions with a reduced set of lithography simulations

A lithography parametric yield estimation model is presented to evaluate the lithography distortion in a printed layout due to lithography hotspots. The aim of the proposed yield model is to provide a new metric that enables the possibility to objectively compare the lithography quality of different layout design implementations. Moreover, we propose a pattern construct classifier to reduce the set of lithography simulations necessary to estimate the litho degradation. The application of the yield model is demonstrated for different layout configurations showing that a certain degree of layout regularity improves the parametric yield and increases the number of good dies per wafer. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)Peer ReviewedPostprint (author’s final draft

NOVEL METHODOLOGY FOR REAL-TIME STRUCTURAL ANALYSIS ASSISTANCE IN CUSTOM PRODUCT DESIGN

Mass-customization is related to optimizing the balance between flexibility, strongly required by the customer-focused industries and manufacturing efficiency, which is critical for market competitiveness. In the conventional industries, the process of designing, validating and manufacturing a product is long and expensive. Some of the common approaches for addressing those issues are parametric product modeling and Finite Element Analysis (FEA). However, the costs involved are still relatively high because of the very special expertise needed and the cost of the specialized software. Also, the specific design of the product cannot be validated in a real-time, which often leads to making hard compromises between the specific customer requirements and the structural properties of the product in its exploitation. In this paper, we propose the novel methodology for real-time structural analysis assistance for custom product design. We introduce the concept of so-called compiled FEA model, a Machine Learning (ML) model, consisting of dataset of characteristic product parameters and associated physical quantities and properties, selected ML algorithms and the sets of associated hyperparameters. A case study of creating a compiled FEA model for the case of internal orthopedic fixator is provided

Model-based design of AM components to enable decentralized digital manufacturing systems

Additive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy

Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective

The development of new batteries has historically been achieved through discovery and development cycles based on the intuition of the researcher, followed by experimental trial and error—often helped along by serendipitous breakthroughs. Meanwhile, it is evident that new strategies are needed to master the ever-growing complexity in the development of battery systems, and to fast-track the transfer of findings from the laboratory into commercially viable products. This review gives an overview over the future needs and the current state-of-the art of five research pillars of the European Large-Scale Research Initiative BATTERY 2030+, namely 1) Battery Interface Genome in combination with a Materials Acceleration Platform (BIG-MAP), progress toward the development of 2) self-healing battery materials, and methods for operando, 3) sensing to monitor battery health. These subjects are complemented by an overview over current and up-coming strategies to optimize 4) manufacturability of batteries and efforts toward development of a circular battery economy through implementation of 5) recyclability aspects in the design of the battery