This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the on-going research at Linköping University is to shift from manual modelling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high-level geometry templates. Instead of repeatedly modelling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high-fidelity geometry in the MDO loop. Quantification of the terms flexibility and robustness is also presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.

It has become a common practice to conduct simulation-based design of industrial robotic cells, where Mechatronic system model of an industrial robot is used to accurately predict robot performance characteristics like cycle time, critical component lifetime, and energy efficiency. However, current robot programming systems do not usually provide functionality for finding the optimal design of robotic cells. Robot cell designers therefore still face significant challenge to manually search in design space for achieving optimal robot cell design in consideration of productivity measured by the cycle time, lifetime, and energy efficiency. In addition, robot cell designers experience even more challenge to consider the trade-offs between cycle time and lifetime as well as cycle time and energy efficiency. In this work, utilization of multi-objective optimization to optimal design of the work cell of an industrial robot is investigated. Solution space and Pareto front are obtained and used to demonstrate the trade-offs between cycle-time and critical component lifetime as well as cycle-time and energy efficiency of an industrial robot. Two types of multi-objective optimization have been investigated and benchmarked using optimal design problem of robotic work cells: 1) single-objective optimization constructed using Weighted Compromise Programming (WCP) of multiple objectives and 2) Pareto front optimization using multi-objective generic algorithm (MOGA-II). Of the industrial robotics significance, a combined design optimization problem is investigated, where design space consisting of design variables defining robot task placement and robot drive-train are simultaneously searched. Optimization efficiency and interesting trade-offs have been explored and successful results demonstrated.

Cable and hose routing is a complex and time-consuming process that often involves several conflicting objectives. Complexity increases further when routes of multiple components are to be considered through the same space. Extensive work has been done in the area of automatic routing where few proposals optimize multiple hoses together. This paper proposes a framework for the routing of multiple pre-formed hoses in an assembly using a unique permutation process where several alternatives for each hose are generated. A combinatorial optimization process is then used to find Pareto-optimal solutions for the multi-route assembly. This is coupled with a scoring model that predicts the overall fitness of a solution based on designs previously scored by the engineer as well as an evaluation system where the engineer can score new designs found through the use of the framework to update the scoring model. The framework is evaluated using a testcase from a car manufacturer showing a severalfold time reduction compared to a strictly manual process. Considering the time savings, the proposed framework has the potential to greatly reduce the overall routing processes of hoses and cables.

Finger design automation is highly demanded from robot industries to fulfill the requirements of the agile market. Nevertheless, literature lacks a promising approach to automate the design process of reliable fingers for industrial robots. Hence, this work proposes the generic optimized finger design (GOFD) method which automates the design process of single- and multi-function finger grippers. The proposed method includes an optimization algorithm to minimize the design process time. The method is utilized to generate fingers for several groups of objects. Results show that the GOFD method outperforms existing methods and is able to reduce the design time by an average of 16,600 s. While the proposed method substantially reduces the design process time of fingers, the quality of grasps is comparable to the traditional exhaustive search method. The grasp quality of GOFD deviates only 0.47% from the absolute best grasp known from the exhaustive search method in average. The designed fingers are lastly manufactured and experimentally verified.

This paper presents a framework to develop the automated design of fixtures using the combination ofdesign automation (DA), multidisciplinary optimization and robotic simulation. MDO necessitates the useof concurrent and parametric designs which are created by DA and knowledge-based engineering tools. Thisapproach is designed to decrease the time and cost of the fixture design process by increasing the degree ofautomation. AutoFix provides methods and tools for automatically optimizing resource-intensive fixturedesign utilizing digital tools from different disciplines.

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.

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.

This paper presents an automated design and evaluation framework, by integrating design tools from various engineering domains for rapid evaluation of design alternatives. The presented framework enables engineers to perform simulation based optimizations. As a proof of concept a seven degree of freedom modular robot is designed and optimized using the automated framework. The designed robot is then manufactured to evaluate the framework using preliminary tests.

This paper presents a multidisciplinary design optimization framework applied to the development of unmanned aerial vehicles with a focus on radar signature and sensor performance requirements while simultaneously considering the flight trajectory. The primary emphasis herein is on the integration and development of analysis models for the calculation of the radar cross section and sensor detection probability, whereas traditional aeronautical disciplines such as aerodynamics and mission simulation are also taken into account in order to ensure a flyable concept. Furthermore, this work explores the effect of implementing trajectory constraints as a supplementary input to the multidisciplinary design optimization process and presents a method that enables the optimization of the aircraft under a three-dimensional flight scenario. To cope with the additional computational cost of the high-fidelity radar cross section and sensor calculations, the use of metamodels is also investigated and an efficient development methodology that can provide high-accuracy approximations for this particular problem is proposed. Overall, the operation and performance of the framework are evaluated against five surveillance scenarios, and the obtained results show that the implementation of trajectory constraints in the optimization has the potential to yield better designs by 12–25% when compared to the more “traditional” problem formulations.

With industries striving towards increased customisation of complex products through engineer-to-order, methods are continuously sought to rationalise the product development process. To this end, a framework is proposed using CAD configurators, utilising design automation and knowledge-based engineering to integrate sales and design processes in product development. The application of this framework to the design of spiral staircases is described and analysed, with results showing decreased lead-times and a decreased risk for design errors.

Maintaining high product quality while reducing cost is essential for mass-customised products, requiring continuous improvement of the product development process. To this end, design automation should be utilised in all stages of a product’s develop process and lay the foundation for automation of repetitive tasks throughout the process from interaction with the customer to design and production in order to mitigate errors and minimise costs. In this paper, a design automation and production preparation framework is proposed that can facilitate automation from initial stages via CAD to production. Examples of the framework are shown in the shape of proof-of-concepts systems developed by master students in the context of a course in design automation at Linköping University. Included disciplines such as automated planning of robot assembly paths, CNC manufacturing files and production drawings are described, based on design automation, Knowledge-Based Engineering, and design optimisation. Additionally, variations of the framework are implemented at three SMEs, and the results thereof are presented. The proposed frameworks enable interaction and connection between the "softer", human centred, aspects of customer interaction within sales, with more traditional "harder" engineering disciplines in design and manufacturing.

In a product development process, it is crucial to understand and evaluate multiple and synergic aspects of systems such as performance, cost, reliability, and safety. These aspects are mainly considered during later stages of the design process. However, in order to improve the foundations for decision-making, this article presents methods that are intended to increase the engineering knowledge in the early design phases. In complex products, different systems from a multitude of engineering disciplines have to work tightly together. Collaborative design is described as a process where a product is designed through the collective and joint efforts of domain experts. A collaborative multidisciplinary design optimization process is therefore proposed in the conceptual design phase in order to increase the likelihood of more accurate decisions being taken early on. The performance of the presented framework is demonstrated in an industrial application to design aircraft systems in the conceptual phase.

When conducting design on novel mechatronic products, it can be valuable to test and evaluate the performance and properties of the concepts throughout the design process by producing them as downscaled prototypes, see Jouannet et al. [1]. This is especially true when the product is of unconventional design and the designer can get increased confidence of the proposed concept by testing it as a sub scaled version. Nonetheless, the process of realization of new concept should be done in a rapid fashion in order not to halt the design process and simultaneously increasing explicit knowledge about the concept. A case study will be illustrated which demonstrates how fully automated design and construction of downscaled prototypes is performed.

In the design of complex engineering products it is essential to handle cross-couplings and synergies between subsystems. An emerging technique, which has the potential to considerably improve the design process, is multidisciplinary design optimization (MDO).

MDO requires a concurrent and parametric design framework. Powerful tools in the quest for such frameworks are design automation (DA) and knowledge based engineering (KBE). The knowledge required is captured and stored as rules and facts to finally be triggered upon request. A crucial challenge is how and what type of knowledge should be stored in order to realize generic DA frameworks.

In the endeavor to address the mentioned challenges, this thesis proposes High Level CAD templates (HLCts) for geometry manipulation and High Level Analysis templates (HLAts) for concept evaluations. The proposed methods facilitate modular concept generation and evaluation, where the modules are first assembled and then evaluated automatically. The basics can be compared to parametric LEGO® blocks containing a set of design and analysis parameters. These are produced and stored in databases, giving engineers or a computer agent the possibility to first select and place out the blocks and then modify the shape of the concept parametrically, to finally analyze it. The depicted methods are based on physic-based models, meaning less design space restrictions compared to empirical models.

A consequence of physic-based models is more time-consuming evaluations, reducing the probability of effective implementation in an iterative intensive MDO. To reduce the evaluation time, metamodels are used for faster approximations. Their implementation, however, is not without complications. Acquiring accurate metamodels requires a non-negligible investment in terms of design space samplings. The challenge is to keep the required sampling level as low as possible.

It will be further elaborated that many automated concurrent engineering platforms have failed because of incorrect balance between automation and manual operations. Hence, it is necessary to find an equilibrium that maximizes the efficiency of DA and MDO.

To verify the validity of the presented methods, three application examples are presented and evaluated. These are derived from industry and serve as test cases for the proposed methods.

THIS THESIS EXPLORES novel CAD modeling methods for design reuse and tomation realization. It will be demonstrated that by applying the described methods, CAD models can be utilized as framework integrators in order to generate geometric input for various engineering analysis tools. Multidisciplinary design can as a result be facilitated in early design due to decreased manual model re-definitions. Furthermore, due to the complex dependency between analysis tools, certain product characteristics can only be evaluated by applying a holistic design approach. Therefore, by applying multidisciplinary design, the level of knowledge about the product will increase.

To simulate and evaluate the properties and behavior of an engineering product during design, the geometry has to be constantly re-estimated. CAD tools can be employed to produce the requested geometry. However simplifications introduced in the geometry, due to incomplete and imprecise knowledge available in early design, result in inaccurate geometries. Thus re-modeling has to occur in a frequent rate in order to achieve sufficiently accurate models. Hence CAD tool are traditionally applied in later stages of design when the geometry of the product is more or less defined and CAD is applied to generate drafting and technical drawings for manufacturing purposes

It is therefore proposed that geometries for repetitive components are stored in so called high level templates and instantiated in the CAD model parametrically. Upon instantiation, each instance can be modified parametrically. Given the fact that the instantiation process is automated, the deletion and replacement procedures are also automatic, enabling easier model modifications in the design process.

To estimate the gained advantage when applying the proposed methods, holistic design frameworks are implemented. The frameworks consist of a combination of various engineering tools which are integrated through a user interface. Given that an information flow between the design tools is implemented, many aspects of design is computed and optimized concurrently. Consequently in order to draw general conclusion concerning geometric modeling, two different design applications with dissimilar requirements are studied in this work, namely aircraft and industrial robots.

The ever fast growing information technology is enabling a re-definition of the early stages of aircraft design which have been restricted to mostly statistical and empirical approaches because of lengthy and costly simulation times. A novel framework is being developed at Linköping University offering greater comprehension by enabling a holistic view of the aircraft systems with a multidisciplinary analysis approach, involving a centralized easy to use interface. This paper will depict the modules involving an aerodynamic panel code solver integrated to a parametric CAD model. The connection between these tools is made fully automatic, meaning that any modification on the CAD model will with a press of button be aerodynamically analyzed in the panel solver, including re-meshing and re-writing of a input file and finally re-distributing the output file to all other modules involved in the framework.

This paper presents an approach of integration between multiple analysis tools that covers several engineering disciplines, used for robot design and optimization. There are three main components in this approach namely a highly flexible geometric model, a parametric dynamic simulation model, and a framework for integration of the models and execution of an optimization process through a user friendly interface. To illustrate the presented methodology an integrated analysis tool for an industrial robot is developed combining dynamic and geometric models in a parametric design approach. An optimization case is conducted to visualize the automation capabilities of the proposed framework, and enhance the early design phases for industrial robots.

This paper presents a multidisciplinary design optimization framework for modular industrial robots. An automated design framework, containing physics based high fidelity models for dynamic simulation and structural strength analyses are utilized and seamlessly integrated with a geometry model.

The proposed frameworkutilizes well-established methods such as metamodeling and multi-level optimization inorder to speed up the design optimization process. The contributionof the paper is to show that by applying amerger of well-established methods, the computational cost can be cutsignificantly, enabling search for truly novel concepts.

This paper presents a multidisciplinary design optimization (MDO) framework for automated design of a modular industrial robot. The developed design framework seamlessly integrates High Level CAD templates (HLCt) and physics based high fidelity models for automated geometry manipulation, dynamic simulation, and structural strengthanalysis. In the developed framework, methods such as surrogate models and multilevel optimization are employed in order to speed up the design optimization process. This work demonstrates how a parametric geometric model, based on the concept of HLCt, enables a multidisciplinary framework for multi-objective optimization of a modular industrial robot, which constitutes an example of a complex heterogeneous system.

Intricate and complex dependencies between multiple disciplines require iterative intensive optimization processes. To this end, multidisciplinary design optimization (MDO) has been established as a convincing concurrent technique to manage inherited complexities.

This paper presents a high level CAD and CAE design automation methodology which enables fast, efficient concept generation for MDO. To increase the evaluation speed, global metamodels are introduced to replace computationally expensive CAD and CAE models. In addition, various techniques are applied to drastically decrease the number ofsamplings required to create the metamodels. In the final part of the paper, a multi-level optimization strategy is proposed to find the optimal concept.

As proof of concept, a real world design problem, from ABB industrial robotics, is presented.

The ever fast growing information technology is enabling the re-definition of early stages of aircraft design which have been restricted mostly by statistical and empirical approaches because of lengthy and costly simulation times. This paper will describe a methodology based on parametric and generic CAD modeling methods which will save time when performing repetitive tasks automatically. Furthermore, it enables greater comprehension by giving a holistic view of the aircraft systems because of the proposed multidisciplinary and modular framework. The framework involves a centralized easy to use interface which is coupled to a series of analyses tools. The proposed methods have been applied in student projects of the course -Aircraft Conceptual Design- from Linköping University where it has shown great promises.

This paper presents a novel approach for designing modular robots. There are two main components in this approach namely the modeling methodology of the robot and a framework for simulation of the models and execution of an optimization process.

To illustrate the presented methodology an integrated analysis tool for an industrial robot is developed combining dynamic and geometric models in a parametric design approach. An optimization case is conducted to visualize the automation capabilities of the proposed framework, and enhance the design for modular industrial robots.

Product platform design is a well recognized methodology to effectively increase range and variety of products and simultaneously decrease internal variety of components by utilizing modularization. The tradeoff between product performance and product family commonality has to be carefully balanced in order to for the company to meet market requirements and simultaneously obtain economy of scale. This paper presents a framework based on high fidelity analyses tools that concurrently optimize an industrial robot family as well as the common platform. The product family design problem is formally stated as a multi-objective optimization problem, which is solved using a multi-objective Genetic Algorithm.

Development of modern aircrafts has become more and more expensive and time consuming. In order to minimize the development cost, an improvement of the conceptual design phase is needed. The desired goal of the project is to enhance the functionality of an in house produced framework conducted at theDepartment of Machine Design, Link¨ping University, consisting of parametric aircraft models used for conceptual design. The former part of the work consists of the construction of geometric aircraft control surfaces such as flaps, aileron, rudder and elevator parametrically in CATIA V5. The next part of the work involves designing and simulating an Inverse dynamic model in Dymola software. An Excel interface has been developed between CATIA and Dymola. Parameters can be varied in the interface as per user specification; these values are sent to CATIA or Dymola and vice versa. The constructed concept model of control surfaces has been tested for different aircraft shapes and layout. An interface is developed between CATIA, Dymola and Tornado. An optimization case is performed to visualize the automation capability of choosing an actuator from a database for the proposed framework, and enhance the early design phases for aircraft conceptual design.

This paper describes a methodology for automation of measurements in Computer-Aided Design (CAD) software by enabling the use of supervised learning algorithms. The paper presents a proof of concept of how dimensions are placed automatically in the drawing at predicted positions. The framework consists of two trained neural networks and a rule-based system. Four steps compound the methodology. 1. Create a data set of labeled images for training a pre-built convolutional neural network (YOLOv5) using CAD automatic procedures. 2. Train the model to make predictions on 2D drawing imagery, identifying their relevant features. 3. Reuse the information extracted from YOLOv5 in a new neural network to produce measurement data. The output of this model is a matrix containing measurement location and size data. 4. Convert the final data output into actual measurements of an unseen geometry using a rule-based system for automatic dimension generation. Although the rule-based system is highly dependent on the problem and the CAD software, both supervised learning models exhibit high performance and reusability. Future work aims to make the framework suitable for more complex products. The methodology presented is promising and shows potential for minimizing human resources in repetitive CAD work, particularly in the task of creating engineering drawings.

Selecting an appropriate machine learning model architecture for manufacturing tasks requires expertise in both computer science and manufacturing. However, integrating state-of-the-art machine learning models and manufacturing processes is often challenging due to the distance between these fields. OpenAI’s popular language model, ChatGPT, has the potential to bridge this gap.

This paper proposes guidelines and questions to explore model architecture options and extract valuable information from ChatGPT’s natural language processing capabilities. While ChatGPT is a powerful tool, it is important to verify any answers obtained against reliable sources before making any decisions. The guidelines compose a flowchart with four queries to give ChatGPT enough context and exisiting input data information. ChatGPT suggestions will be directed towards input processing, output, and architecture proposals. The last query produces keywords based on the chat for a background study on the topic.

A manufacturing case study was conducted to demonstrate the effectiveness of these guidelines. The study involved creating a model to forecast fixturing locations for welding processes in the automotive sector. After conducting four separate interviews with ChatGPT, the authors discuss the selection of architecture based on ChatGPT suggestions and contrast it with previous literature.

The proposed guidelines are expected to be useful in a variety of manufacturing contexts, as they offer a structured approach to exploring model architecture options using ChatGPT’s capabilities, ultimately leading to new and innovative applications of machine learning in this field.

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.

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.

Companies manufacturing customized engineer-to-order (ETO) products are decelerated by repetitive work, misinterpretations and uncoordinated processes which prohibits the achievement of mass customization. Being able to deliver customized product with low costs and fast delivery times, the concept of mass customization, is a prerequisite for maintained competitiveness with the demands from the market today. This paper presents a product configuration system (PCS) for customized products using design automation enabled by knowledge-based engineering (KBE) and enterprise-wide optimization (EWO). With this approach, the process from sales to delivery of customized products can be extensively rationalized. The PCS consists of two modules. The first being a configurator for use in the sales quotation stage. Here, customer requirements are captured, and used to generate alternatives feasible for the customer context. Thereby, correct quotations can be generated at the sales instance. The second module is the enterprise-wide configurator where accepted orders are concurrently optimized for their detailed and final design, considering the current state of the production and concurrent sales cases in the company. In other terms, instead of adapting the supply chain according to the design of the products in the order entry, the design of the products in the order entry are adapted according to the state of the supply chain. Thereby, resources can be efficiently utilized to the benefit of both the customer and the company, with reduced costs and delivery times. An implementation of the PCS in a case concerning spiral staircases, an ETO product, has shown potential of substantially reducing resources and errors and enable a reliable process supporting achievement of mass customization.

With the increasing complexity and dynamism in today’s product design and manufacturing, more optimal, robust and practical approaches and systems are needed to support product design and manufacturing activities. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing presents a focused collection of quality chapters on state-of-the-art research efforts in multi-objective evolutionary optimisation, as well as their practical applications to integrated product design and manufacturing. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing consists of two major sections. The first presents a broad-based review of the key areas of research in multi-objective evolutionary optimisation. The second gives in-depth treatments of selected methodologies and systems in intelligent design and integrated manufacturing. Recent developments and innovations in multi-objective evolutionary optimisation make Multi-objective Evolutionary Optimisation for Product Design and Manufacturing a useful text for a broad readership, from academic researchers to practicing engineers.