Autofix – Automated Design of Fixtures

Nambiar, Sanjay; Albert, A. P.; Rimmalapudi, V. V. R. C.; Acharya, V.; Tarkian, Mehdi; Kihlman, M.

doi:10.1017/pds.2022.56

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…While designing a customized product, a requirement is the creation of a detailed description of the specific processing steps required throughout the design process [7]. A DA framework can be developed using this knowledge in the design process to automate tedious and repetitive tasks, thereby allowing the designers to focus more on the operations that require creativity, intuition, skill, and cooperation to be solved [8].…”

Section: Design Automationmentioning

confidence: 99%

“…The division of the knowledge base and the functions that use it, known as the interface engine, is one of the KBS's defining characteristics [13]. The DA framework can be maintained more quickly and effectively because of this separation of the interface engine and knowledge base, which enables future knowledge base updates without compromising the interface engine [8].…”

Section: Knowledge-based Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

Multidisciplinary Automation in Design of Turbine Vane Cooling Channels

Nambiar,

Ananno,

Titus

et al. 2024

IJTPP

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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.

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Section: Design Automationmentioning

confidence: 99%

Section: Knowledge-based Engineeringmentioning

confidence: 99%

Multidisciplinary Automation in Design of Turbine Vane Cooling Channels

Nambiar,

Ananno,

Titus

et al. 2024

IJTPP

View full text Add to dashboard Cite

show abstract

“…The current paper belongs to the third mentioned category. [11] Planning algorithm, clamping force evaluation 2 Mild Strong Parvaz and Nategh [12] 3 locating rules + 2 validation conditions 3 Strong Strong Mihaylov [13] If-else logic on CAD 1 Weak Strong Manafi and Nategh [14] Machining rules in setup planning 2 Mild Mild Optimization Sundararaman et al [15] GA + RSM 1 Weak Strong Ahmad et al [16] GA + static forces analysis 4 Weak Strong Chen et al [17] GA + static forces analysis 4 Weak Strong Ma et al [18] GA + radial-basis function 2 Weak Strong Rex et al [19] GA + RSM vs ANN comparison 1 Weak Strong Li et al [20] Whale optimization + strain energy analysis 2 Weak Strong Nambiar et al [21] Fixture position optimization 1 gives an overview of publications on the topic and the different approaches taken historically to formulating the fixture layout problem. The publications are classified attending to their category, the main developed tool framework, and the challenges mentioned.…”

Section: Previous Work On Fixture Layoutmentioning

confidence: 99%

“…Other publications choose a more complex workpiece and use simple static analysis to retrieve displacements and forces. Once the clamping system has been determined, Nambiar et al (2022) suggest utilizing an optimization algorithm to determine the optimal position of automated fixtures designs considering robot path clearance.…”

Section: Fixture Layout Optimizationmentioning

confidence: 99%

Application of optimized convolutional neural network to fixture layout in automotive parts

Toro

Wiberg

Tarkian

2023

Int J Adv Manuf Technol

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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 (≈ 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.

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Autofix – Automated Design of Fixtures

Cited by 2 publications

References 13 publications

Multidisciplinary Automation in Design of Turbine Vane Cooling Channels

Multidisciplinary Automation in Design of Turbine Vane Cooling Channels

Application of optimized convolutional neural network to fixture layout in automotive parts

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