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Tarkian, Mehdi
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Publications (10 of 30) Show all publications
Nambiar, S., Ananno, A. A., Titus, H., Wiberg, A. & Tarkian, M. (2024). Multidisciplinary Automation in Design of Turbine Vane Cooling Channels. International Journal of Turbomachinery, Propulsion and Power, 9(1), Article ID 7.
Open this publication in new window or tab >>Multidisciplinary Automation in Design of Turbine Vane Cooling Channels
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2024 (English)In: International Journal of Turbomachinery, Propulsion and Power, ISSN 2504-186X, Vol. 9, no 1, article id 7Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
multidisciplinary automation, design automation, mesh automation, knowledge-based engineering, turbine vane cooling design
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-201145 (URN)10.3390/ijtpp9010007 (DOI)001192494000001 ()
Funder
Vinnova, 2020-04251
Note

Funding: VINNOVA

Available from: 2024-02-23 Created: 2024-02-23 Last updated: 2024-05-21Bibliographically approved
Nambiar, S., Wiberg, A. & Tarkian, M. (2023). Automation of unstructured production environment by applying reinforcement learning. Frontiers in Manufacturing Technology, 3
Open this publication in new window or tab >>Automation of unstructured production environment by applying reinforcement learning
2023 (English)In: Frontiers in Manufacturing Technology, E-ISSN 2813-0359, Vol. 3Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
Reinforcement Learning, Unity Game Engine, Mobile Robot, Mannequin, Production Environment
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-195616 (URN)10.3389/fmtec.2023.1154263 (DOI)
Funder
Vinnova, 2020-05173
Available from: 2023-06-22 Created: 2023-06-22 Last updated: 2024-05-21Bibliographically approved
Villena Toro, J. & Tarkian, M. (2023). Model Architecture Exploration Using Chatgpt for Specific Manufacturing Applications. In: ASME IDETC-CIE: . Paper presented at IDETC-CIE 43rd Computers and Information in Engineering. , 2
Open this publication in new window or tab >>Model Architecture Exploration Using Chatgpt for Specific Manufacturing Applications
2023 (English)In: ASME IDETC-CIE, 2023, Vol. 2Conference paper, Published paper (Refereed)
Abstract [en]

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.

Keywords
fixture layout, machine learning, ChatGPT, manufacturing planning, model exploration
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-199611 (URN)10.1115/DETC2023-116483 (DOI)978-0-7918-8729-5 (ISBN)
Conference
IDETC-CIE 43rd Computers and Information in Engineering
Funder
Vinnova, 2020-02974
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13
Villena Toro, J., Wiberg, A. & Tarkian, M. (2023). Optical character recognition on engineering drawings to achieve automation in production quality control. Frontiers in Manufacturing Technology, 3
Open this publication in new window or tab >>Optical character recognition on engineering drawings to achieve automation in production quality control
2023 (English)In: Frontiers in Manufacturing Technology, E-ISSN 2813-0359, Vol. 3Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
optical character recognition, image segmentation, object detection, engineering drawings, quality control, keras-ocr
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-195416 (URN)10.3389/fmtec.2023.1154132 (DOI)
Funder
Vinnova, 2021-02481
Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2023-12-13Bibliographically approved
Nambiar, S., Albert, A. P., Rimmalapudi, V. V., Acharya, V., Tarkian, M. & Kihlman, H. (2022). Autofix – Automated Design of Fixtures. In: : . Paper presented at International Design Conference - Design 2022, 23 - 26 May, 2022 (pp. 543-552). Cambridge University Press, 2
Open this publication in new window or tab >>Autofix – Automated Design of Fixtures
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2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cambridge University Press, 2022
Keywords
design automation, design optimisation, knowledge-based engineering (KBE), fixtures, robotic simulation
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-195445 (URN)10.1017/pds.2022.56 (DOI)2-s2.0-85131360012 (Scopus ID)
Conference
International Design Conference - Design 2022, 23 - 26 May, 2022
Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2024-05-21Bibliographically approved
Villena Toro, J. & Tarkian, M. (2022). Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study. In: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering ConferenceAugust 14–17, 2022St. Louis, Missouri, USA: Volume 3B: 48th Design Automation Conference (DAC). Paper presented at ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. St. Louis, MO, USA, B
Open this publication in new window or tab >>Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study
2022 (English)In: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering ConferenceAugust 14–17, 2022St. Louis, Missouri, USA: Volume 3B: 48th Design Automation Conference (DAC), St. Louis, MO, USA, 2022, Vol. BConference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
St. Louis, MO, USA: , 2022
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-196468 (URN)10.1115/DETC2022-88971 (DOI)978-0-7918-8623-6 (ISBN)
Conference
ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Projects
iProd
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-12-13
Gustafsson, E., Persson, J. & Tarkian, M. (2021). Combinatorial Optimization of Pre-Formed Hose Assemblies. In: Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2021): Volume 3B: 47th Design Automation Conference (DAC). Paper presented at ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference August 17-19, 2021, Virtual, Online. The American Society of Mechanical Engineers, Article ID V03BT03A033.
Open this publication in new window or tab >>Combinatorial Optimization of Pre-Formed Hose Assemblies
2021 (English)In: Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2021): Volume 3B: 47th Design Automation Conference (DAC), The American Society of Mechanical Engineers , 2021, article id V03BT03A033Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
The American Society of Mechanical Engineers, 2021
Keywords
multiobjective optimization, design automation, hose routing, path planning Topics:Optimization, Cables, Engineers, Manufacturing, Design automation, Pareto optimization, Path planning
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-184180 (URN)10.1115/DETC2021-71408 (DOI)978-0-7918-8539-0 (ISBN)
Conference
ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference August 17-19, 2021, Virtual, Online
Note

Funding agencies: This work has been financed by Vinnova and by governmentand industry cooperation on vehicles of the future, within the research project AUTOPACK 2017-03065

Available from: 2022-04-06 Created: 2022-04-06 Last updated: 2022-04-07
Poot, L. P. & Tarkian, M. (2021). Design and Production Automation for Mass Customisation: An Initial Framework Proposal Evaluated in Engineering Education and SME Contexts. In: Linda Newnes, Susan Lattanzio, Bryan R. Moser, Josip Stjepandić, Nel Wognum (Ed.), Transdisciplinary Engineering for Resilience: Responding to System Disruptions. Paper presented at 28th ISTE International Conference on Transdisciplinary Engineering, Virtual, July 5 – July 9, 2021 (pp. 71-80). Amsterdam: IOS Press
Open this publication in new window or tab >>Design and Production Automation for Mass Customisation: An Initial Framework Proposal Evaluated in Engineering Education and SME Contexts
2021 (English)In: Transdisciplinary Engineering for Resilience: Responding to System Disruptions / [ed] Linda Newnes, Susan Lattanzio, Bryan R. Moser, Josip Stjepandić, Nel Wognum, Amsterdam: IOS Press, 2021, p. 71-80Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2021
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 16
Keywords
Design Automation, Mass Customisation, Product Configuration, Production Preparation, Transdisciplinary Engineering Education
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-181813 (URN)10.3233/ATDE210084 (DOI)978-1-64368-208-2 (ISBN)978-1-64368-209-9 (ISBN)
Conference
28th ISTE International Conference on Transdisciplinary Engineering, Virtual, July 5 – July 9, 2021
Projects
e-FACTORY
Available from: 2021-12-13 Created: 2021-12-13 Last updated: 2022-03-04Bibliographically approved
Wehlin, C., Vidner, O., Poot, L. & Tarkian, M. (2021). Integrating Sales, Design and Production: A Configuration System for Automation in Mass Customization. In: Proceedings of ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2021): Volume 3B: 47th Design Automation Conference (DAC). Paper presented at ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Virtual, Online, August 17–19, 2021. New York: The American Society of Mechanical Engineers, Article ID V03BT03A042.
Open this publication in new window or tab >>Integrating Sales, Design and Production: A Configuration System for Automation in Mass Customization
2021 (English)In: Proceedings of ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2021): Volume 3B: 47th Design Automation Conference (DAC), New York: The American Society of Mechanical Engineers , 2021, article id V03BT03A042Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
New York: The American Society of Mechanical Engineers, 2021
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-181811 (URN)10.1115/DETC2021-68426 (DOI)9780791885390 (ISBN)
Conference
ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Virtual, Online, August 17–19, 2021
Projects
e-FACTORY
Funder
VinnovaSwedish Research Council Formas
Available from: 2021-12-13 Created: 2021-12-13 Last updated: 2022-03-04Bibliographically approved
Poot, L. P., Wehlin, C., Tarkian, M. & Ölvander, J. (2020). Integrating sales and design: Applying CAD configurators in the product development process. In: Proceedings of the Design Society: DESIGN Conference. Paper presented at 16th International Design Conference, Online, October 26-29 2020 (pp. 345-354). Cambridge University Press, 1
Open this publication in new window or tab >>Integrating sales and design: Applying CAD configurators in the product development process
2020 (English)In: Proceedings of the Design Society: DESIGN Conference, Cambridge University Press, 2020, Vol. 1, p. 345-354Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cambridge University Press, 2020
Series
Proceedings of the Design Society: DESIGN Conference, ISSN 2633-7762 ; 1
Keywords
Computer-aided design (CAD), Design automation, Knowledge-based engineering (KBE), Product configuration, Mass customisation
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-176082 (URN)10.1017/dsd.2020.129 (DOI)
Conference
16th International Design Conference, Online, October 26-29 2020
Available from: 2021-06-04 Created: 2021-06-04 Last updated: 2022-03-04Bibliographically approved
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