Integrating the setup planning with fixture design practice by concurrent consideration of machining and fixture design principles

Manafi, D.; Nategh, Mohammad Javad

doi:10.1080/00207543.2020.1736357

Cited by 8 publications

(6 citation statements)

References 51 publications

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“…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%

“…Mihaylov [13] implemented an add-in in Solidworks to automate the 3-2-1 location principle using if-else statements, focusing on prismatic simple shapes. Manafi and Nategh [14] account for setup planning in prismatic shapes with multiple work faces.…”

Section: Rule Formulationmentioning

confidence: 99%

“…The limitation would be on the previous data. This fact introduces a considerable advantage versus CBR [22,24] or rule-based formulation [10,11,13,14]. C2.…”

Section: About Neural Network In Automatic Fixture Layoutmentioning

confidence: 99%

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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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Section: Previous Work On Fixture Layoutmentioning

confidence: 99%

Section: Rule Formulationmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The machining fixture designing involves detailed and accurate calculations of geometric dimensioning and tolerancing aspects 23 involving principles of positioning, locating and clamping. 24 Table 4 depicts the design process of a milling fixture when visualised spatially. With the advent of various CAD geometric modelling softwares, the design process is exponentially simplified, accelerated and the lead time for the tool design is minimised drastically.…”

Section: Case Study 1: Design Ideation Of a Milling Fixturementioning

confidence: 99%

Spatial visualization for development of visual thinking and cognitive abilities among mechanical engineering students through tool design ideation

Sharma¹

2023

International Journal of Mechanical Engineering Education

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With the exposure of the mechanical engineering undergraduate students to the concepts of spatial visualization, this work assesses the effect of spatial visualization skills on the visual thinking abilities. Influence of spatial visualization on visual thinking and cognitive abilities like reasoning, abstract thinking, planning, comprehension of complex ideas, quick learning and learning from experience, is analysed through FMEA (failure modes and effects analysis). The RPNs (risk-priority-numbers) have been calculated and priority is set for proposing the corrective actions. Further to this, case studies in tool design have been picked for exhibition of spatial visualization skill set. Influence of tool design on visual thinking capability of the students is understood through various case-studies. Thus, tool, die and mold design ideation is used as a platform for the proposing improvements in visual thinking and cognitive abilities among mechanical engineering students.

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“…In the research study [ 8 ], the fixture modeling module was developed to assign the appropriate locating and clamping data for the prismatic part using information from the process/setup plan and the part geometrical database. In the study [ 9 ], the integrated method was developed for locating prismatic parts using the 3–2–1 locating principle. Three locating surfaces were identified for each setup to avoid the stack-up of errors.…”

Section: Introductionmentioning

confidence: 99%

Locating Chart Choice Based on the Decision-Making Approach

et al. 2022

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Modern manufacturing engineering requires quick and reasonable solutions during the production planning stage, ensuring production efficiency and cost reduction. This research aims to create a scientific approach to the rational choice of a locating chart for complexly shaped parts. It is an important stage during the manufacturing technology and fixture design process. The systematization of the designed and technological features of complexly shaped parts and the definition of the features that impact a locating chart create the fundamentals for justification. A scientific approach has been developed using the complex combination of the part’s features and a decision-making approach using the example of bracket-type parts. The matrix of design and technological features of parts was developed including steel AISI 3135 and cast iron DIN 1691. The classification of locating charts for bracket-type parts was defined. A mathematical model of the rational choice of the locating chart according to the structural code of the workpiece was verified in case studies from the practice. As a result, a decision-making approach was applied to the rational choice of the locating chart for any bracket-type part. The proposed solutions improve the production planning stage for machine building, automotive, and other industries.

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Integrating the setup planning with fixture design practice by concurrent consideration of machining and fixture design principles

Cited by 8 publications

References 51 publications

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

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

Spatial visualization for development of visual thinking and cognitive abilities among mechanical engineering students through tool design ideation

Locating Chart Choice Based on the Decision-Making Approach

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