Individualizing assembly processes for geometric quality improvement
Doktorsavhandling, 2021

Dimensional deviations are a consequence of the mass production of parts. These deviations can be controlled by tightening production tolerances. However, this solution is not always desired because it usually increases production costs.
The availability of massive amounts of data about products and automatized production has opened new opportunities to improve products' geometrical quality by individualizing the assembly process.
This individualization can be conducted through several techniques, including selective assembly, locator adjustments, weld sequence optimization, and clamping sequence optimization in a smart assembly line for spot-welded sheet metal assemblies.

This study focuses on two techniques of individualizing the assembly process, selective assembly, and individualized locator adjustments in assembly fixtures. The existing studies and applications of these methods are reviewed, and the research gaps are defined.

The previous applications of selective assembly are limited to linear and rigid assemblies. This study develops the application of selective assembly for sheet metal assemblies. This research addresses another research gap regarding the selective assembly of sheet metals by reducing the calculation cost associated with this technique.

This study also develops a new locator adjustment method. This method utilizes scanned geometries of mating parts to predict the required adjustments. Afterward, a method for individualized adjustments is also developed. Considering applied and residual stresses during the assembly process as constraints is another contribution of this research to locator adjustments. These methods are applied to three industrial sample cases and the results evaluated.
The results illustrate that individualization in locator adjustments can increase geometrical quality improvements three to four times.

Accumulation of the potential improvements from both techniques in a smart assembly line is also evaluated in this study. The results indicate that combining the techniques may not increase the geometrical quality significantly relative to using only individualized locator adjustments.

A crucial factor in the achievable improvements through individualization is the utilized assembly fixture layout. This study develops a method of designing the optimal fixture layout for sheet metal assemblies. Different design and production strategies are investigated to acquire the maximum potential for geometrical improvements through individualization in self-adjusting smart assembly lines.

Selective Assembly

Individualization of production

Locator Adjustments

sheet metal assembly processes

Variation Simulations

Geometry Assurance

Virtual Development Laboratory (VDL) Via Zoom (Passcode: 493098)
Opponent: Professor Darek Ceglarek, University of Warwick, United Kingdom.

Författare

Abolfazl Rezaei Aderiani

Chalmers, Industri- och materialvetenskap, Produktutveckling

Developing a selective assembly technique for sheet metal assemblies

International Journal of Production Research,; Vol. 57(2019)p. 7174-7188

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Individualizing Locator Adjustments of Assembly Fixtures Using a Digital Twin

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Optimal design of fixture layouts for compliant sheet metal assemblies

International Journal of Advanced Manufacturing Technology,; Vol. 110(2020)p. 2181-2201

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Evaluating different strategies to achieve the highest geometric quality in self-adjusting smart assembly lines

Robotics and Computer-Integrated Manufacturing,; Vol. 71(2021)

Artikel i vetenskaplig tidskrift

Produktionsindividualisering: Framtidens produktion för en högre kvalitetsnivå.

Production individualization: The future of production for a higher level of quality.

A principle challenge in production is geometrical deviations of the produced products from the designed product. The functionality and aesthetic qualities of the product can be affected by these deviations besides the additional costs that they impose on the production. These deviations can be reduced by employing higher quality production machines and tools, but this solution increases the production cost and may not be reasonable.
Traditionally the deviations of production have been treated as uncertainties and noises. Therefore, most of the solutions have focused on minimizing the sensitivity of produced products to these noises. However, thanks to robotized production lines, scanning technologies, and machine learning techniques, a new opportunity has arisen to identify and treat these deviations for every product individually.
The geometrical deviation of each part can be scanned by taking several pictures of the parts. This thesis evaluates the means of utilizing the scanned forms to achieve the highest geometrical quality in non-rigid assemblies. These assemblies are ubiquitous in the automotive and aerospace industries. Predicting behaviors of non-rigid assemblies is far more complicated than rigid assemblies due to the variety of factors involved.
Two techniques of selective assembly and individualized locator adjustments are developed and evaluated to be used in individualizing the assembly process of sheet metal assemblies. The results manifest the techniques developed are promising in achieving a significant geometrical quality improvement.
The effects of other production factors including the assembly fixtures are evaluated on the potential improvements. Thus, the improvements can further increase by the specific design of fixtures for individualized assembly processes.

Smart Assembly 4.0

Stiftelsen för Strategisk forskning (SSF) (RIT15-0025), 2016-05-01 -- 2021-06-30.

Ämneskategorier

Maskinteknik

Produktionsteknik, arbetsvetenskap och ergonomi

Styrkeområden

Produktion

ISBN

978-91-7905-444-1

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4911

Utgivare

Chalmers tekniska högskola

Virtual Development Laboratory (VDL) Via Zoom (Passcode: 493098)

Online

Opponent: Professor Darek Ceglarek, University of Warwick, United Kingdom.

Mer information

Senast uppdaterat

2021-03-24