Multi-role fighter aircraft are facilitated by operational and platform capabilities that are in turn supported by basic aircraft function. Thermal management (TM) is a basic aircraft function. Effective TM design at the aircraft concept stage can determine if it can support the intended operational and aircraft capabilities early in the aircraft project. In this study, a three-session workshop with a cross-functional team for TM design was conducted at Saab AB. The outcomes from the workshop resulted in a framework for a detailed understanding of the steps to be carried out iteratively for TM design by a cross-functional team. It also provides the dependencies for these steps and the various functional groups that need to be involved in each step. The steps can be used to iterate TM design at the aircraft concept stage and understand the implications on aircraft and operational capabilities. Further, the workshop methodology presented can be used to obtain similar frameworks for design of other basic functions at the aircraft concept stage.

High melting point materials such as ceramics and metal carbides are in general difficult to manufacture due to their physical properties, which imposes the need for new manufacturing methods where electron beam powder bed fusion (EB-PBF) seems promising. Most materials that have been successfully printed with EB-PBF are metals and metal alloys with good electrical conductivity, whereas dielectric materials such as ceramics are generally difficult to print. Catastrophic problems such as smoking and spattering can occur during the EB-PBF processing owing to inappropriate physical properties such as lack of electrical, and thermal conductivity and high melting point, which are challenging to overcome by process optimization. Due to these difficulties, a limited level of understanding has been achieved regarding melting ceramics and refractory alloys. Herein, three different substrates of niobium carbide (NbC) are melted using EB-PBF. The established process parameter window shows a good correlation between EB-PBF process parameters, surface, and melt characteristics, which can be used as a baseline for a printing process. Melting NbC is proven feasible using EB-PBF; the work also points out challenges related to arc trips and spattering, as well as future investigations necessary to create a stable printing process. Additive manufacturing offer new ways of manufacturing ceramics and metal carbides otherwise hard to produce. This study presents one of the first attempts at melting niobium carbide using electron beam powder bed fusion by identifying process window and investigating how the different process parameters affect the melt characteristics, as well as identifying potential issues regarding printing metal carbides.image (c) 2024 WILEY-VCH GmbH

In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology.

Additive manufacturing is an innovative production approach aimed at creating products that traditionaltechniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing process, data is either used (such as materials properties, printer characteristics and settings)or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research andindustry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semantics-aware data management, and enhance the understanding and optimization of additive manufacturingprocesses. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph.

This scientific paper introduces a Design Automation (DA) framework that streamlines the Design for Additive Manufacturing (DfAM) process. The framework is designed to simplify the creation and evaluation of different design options by automating geometry creation using high-level CAD templates and setting up and connecting Computer-Aided Engineering (CAE) models to perform functional and manufacturing evaluations. By considering manufacturing constraints early in the design process, the framework aims to investigate various design alternatives and facilitate design changes late in the development process without additional manual work. This framework provides a comprehensive view of the entire DfAM process, integrating everything from functional requirements to manufacturing evaluation and preparation into the same design automation framework. To demonstrate the usefulness of the framework, the authors used it to design a hydraulic pump. Compared to the original design, the design created with the proposed framework reduces pressure drop by more than 50% and reduces the pump's weight by 35%. Furthermore, on an assembly level, the framework consolidates four components into two and eliminates two sealings. In summary, the Design Automation framework introduced in this paper simplifies the DfAM process by enabling automation of geometry creation and the setup and connection of CAE models. The framework facilitates the exploration of different design alternatives early in the process, considering manufacturing constraints, and enables design changes later in the development process without manual work. The benefits of the framework are illustrated through the design of a hydraulic pump, where it achieved significant improvements in performance, weight, and assembly complexity. 

Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (approximate to 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.

Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well.

Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.

Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.

Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.

Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.

Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

This paper presents five industrial cases where design automation (DA) systems supported by design optimization has been developed, and aims to summarize the lesson learned and identify needs for future development of such projects. By mapping the challenges during development and deployment of the systems, common issues were found in technical areas such as model integration and organizational areas such as knowledge transfer. The latter can be seen as a two-layered design paradox; one for the product that the DA system is developed for, and one for the development of the DA system.

Purpose - The purpose of this paper is to present a Design for Additive Manufacturing (DfAM) methodology that connects several methods, from geometrical design to post-process selection, into a common optimisation framework.

Design/methodology/approach - A design methodology is formulated and tested in a case study. The outcome of the case study is analysed by comparing the obtained results with alternative designs achieved by using other design methods. The design process in the case study and the potential of the method to be used in different settings are also discussed. Finally, the work is concluded by stating the main contribution of the paper and highlighting where further research is needed.

Findings - The proposed method is implemented in a novel framework which is applied to a physical component in the case study. The component is a structural aircraft part that was designed to minimise weight while respecting several static and fatigue structural load cases. An addition goal is to minimise the manufacturing cost. Designs optimised for manufacturing by two different AM machines (EOS M400 and Arcam Q20+), with and without post-processing (centrifugal finishing) are considered. The designs achieved in this study show a significant reduction in both weight and cost compared to one AM manufactured geometry designed using more conventional methods and one design milled in aluminium.

Originality/value - The method in this paper allows for the holistic design and optimisation of components while considering manufacturability, cost and component functionality. Within the same framework, designs optimised for different setups of AM machines and post-processing can be automatically evaluated without any additional manual work.