An intelligent constitutive and collaborative framework by integrating the design, inspection and testing activities using a cloud platform

Saravanan, A.; Jerald, Jason; Rani, A. Delphin Carolina

doi:10.1080/0951192x.2020.1736712

Cited by 3 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the machined part lies within the tolerance span, the product is accepted else rejected/reworked. In addition to it, the specified manufacturing tolerance value must ensure product functionality (Srinivasan et al ., 1996; Saravanan et al ., 2020), interchangeability (Pierre et al ., 2009), and customer requirements (Chandrasegaran et al ., 2013). Generally, design engineers prefer closer tolerances for better product functionality; on the contrary, manufacturing engineers expect liberal tolerances (Yang et al , 1997).…”

Section: Introductionmentioning

confidence: 99%

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Saravanan

Jerald

Rani

2020

AIEDAM

Self Cite

View full text Add to dashboard Cite

The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

show abstract

Section: Introductionmentioning

confidence: 99%

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Saravanan

Jerald

Rani

2020

AIEDAM

Self Cite

View full text Add to dashboard Cite

show abstract

A combinatorial optimization approach for geometric tolerance design considering various taxonomies

Mahesh,

Saravanan

2024

Engineering Optimization

View full text Add to dashboard Cite

Measurement uncertainty estimation of gap and profile in the automotive sector

D’Emilia

Gaspari

Iavicoli

et al. 2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In this paper, a methodology is described aiming at assessing the variability causes, affecting the measurement instruments typically used in the automotive sector for the analysis of dimensional defects of coupled components, to check the conformity of the width of the gap between two adjacent parts and the profile, i.e. the alignment of the two surfaces. The inspection may be performed by means of different gap/flush tools, such as feeler gages, calipers and Laser scanner devices; depending on the choice, a different measurand has to be considered. The obtained measurements need to be consistent, because these devices are used indistinctly, depending on the severity of the control and on the typology of the control itself. The paper proposes a methodology to verify the metrological conformity of measurements provided by both traditional and laser-based instruments, so that it is possible to interpret them in an interchanging manner. A 3D geometric model is realized, used as a basis for the analysis of the most influencing uncertainty causes and measurement biases. The methodology will demonstrate to be able to identify and evaluate in a parametric way, the typical variability causes of the instruments considered. It will also highlight possible improvements in the integration of the obtained results.

show abstract

An intelligent constitutive and collaborative framework by integrating the design, inspection and testing activities using a cloud platform

Cited by 3 publications

References 44 publications

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

A combinatorial optimization approach for geometric tolerance design considering various taxonomies

Measurement uncertainty estimation of gap and profile in the automotive sector

Contact Info

Product

Resources

About