Autofix—an expert CAD system for jigs and fixtures

Pham, Duc Truong; Lazaro, A. de Sam

doi:10.1016/0890-6955(90)90184-k

Cited by 48 publications

(5 citation statements)

References 1 publication

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“…Rule proposition for plane workpieces have been studied for more than 30 years, for instance, Pham and de Sam Lazaro [9] developed a CAD-based expert system to be used in conjunction with their FEM analysis package for the purpose of analyzing prismatic workparts, while Lin and Yang [10] implemented a rule-based modular fixture location system for simple blocks. Qin et al [11] propose a novel approach by breaking down the locating principle into a mathematical formulation.…”

Section: Rule Formulationmentioning

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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Section: Rule Formulationmentioning

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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“…In general, the requirements for reconfigurable and/or automated fixturing techniques are similar to those for the traditional fixtures. The requirements for reconfigurable fixturing include the following Pham and Lazaro (1990).…”

Section: Automated Fixturing Requirementsmentioning

confidence: 99%

Flexible fixturing for workpiece positioning and constraining

Shirinzadeh

2002

Assembly Automation

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Flexible fixturing is an important aspect of any flexible manufacturing system (FMS) and computer integrated manufacturing (CIM) environment. The production analysis for fixturing within an FMS environment is presented. Various approaches to flexible fixturing are briefly described. The reconfigurable fixturing is one of the most appropriate flexible fixturing techniques for CIM environment. Reconfigurable and/or automated modular fixturing employs a number of fixture modules that are set up, adjusted and changed to form different fixture layout. The requirements for locating and constraining workpieces are presented. In addition, computer‐aided planning and analysis of fixture set up are discussed.

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“…The literature on locating thick-walled rigid objects is sufficiently rich and will not be expanded upon in this paper [14][15][16]. Herein, we will only refer to research on the reconflguration of thin-walled flexible objects [5,[17][18][19].…”

Section: Fixture Reconfigurationmentioning

confidence: 99%