kth.sePublications KTH
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Assessing the influence of expert video aid on assembly learning curves
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-6064-5634
Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy.ORCID iD: 0000-0003-2121-6276
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0002-0723-1712
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
Show others and affiliations
2021 (English)In: Article in journal (Refereed) Submitted
Abstract [en]

Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves represents the long term production efficiency of the operators, the latter effect might require more attention from industry and researchers.

Place, publisher, year, edition, pages
2021.
Keywords [en]
manufacturing; assembly; expert video aid; learning curve; functional analysis of variance
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-300197OAI: oai:DiVA.org:kth-300197DiVA, id: diva2:1588621
Note

QC 20210929

Available from: 2021-08-27 Created: 2021-08-27 Last updated: 2022-06-25Bibliographically approved
In thesis
1. Introducing a procedural knowledge model for enhancing industrial process adaptiveness
Open this publication in new window or tab >>Introducing a procedural knowledge model for enhancing industrial process adaptiveness
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Industrial processes are mainly based on procedural knowledge that must be continually elicited from experienced operators and learned by novice operators. In the context of Industry 4.0, machines already play a key role in knowledge transfer; however, new models and methods based on the artificial intelligence advances of the past few years need to be developed and applied. The future of human-machine collaboration is not limited to physical applications, but it has the potential to harness both the strength of human skills, experience and the computational power provided by the surrounding machines for truly adaptive industrial processes. The winning recipe is a balance between letting humans exploit their inherent experience and letting machines integrate the missing skills to preserve production standards. This work introduces a procedural knowledge model to be used for the design of industrial and scientific adaptive processes and it paves the way to transforming human-machine collaboration into an efficient solution to make industrial and scientific processes resilient to a constantly changing world.

Abstract [sv]

Industriella processer baseras huvudsakligen på den procedurella kunskapen som fortlöpande måste tas fram och anpassas av erfarna operatörer och läras in av nybörjare. Inom ramen för Industri 4.0 spelar maskiner redan en nyckelroll i kunskapsöverföring; dock behöver nya modeller och metoder utvecklas och användas, som baseras på de senaste årens framsteg inom artificiell intelligens. Framtiden för samarbete mellan människa och maskin är inte begränsad till fysiska applikationer, utan den har potential att utnyttja såväl styrkan i mänsklig kompetens och erfarenhet som den beräkningskraft som de omgivande maskinerna tillhandahåller, för att åstadkomma verkligt anpassningsbara industriella processer. Det vinnande receptet är att hitta en balans mellan att låta människor utnyttja sina egna erfarenheter och att låta maskiner tillhandahålla de saknade färdigheterna för att kunna följa produktionsstandarder. I detta arbete introduceras en procedurell kunskapsmodell som kan användas för utformning av industriella och vetenskapliga, anpassningsbara processer och banar väg för att omvandla samarbete mellan människor och maskiner till effektiva lösningar för att göra industriella och vetenskapliga processer följsamma i en ständigt föränderlig värld.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2021. p. 184
Series
TRITA-ITM-AVL ; 2021:35
Keywords
procedural knowledge, industrial process, industry 4.0, adaptiveness, human-machine collaboration
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production Engineering
Identifiers
urn:nbn:se:kth:diva-300208 (URN)978-91-7873-963-9 (ISBN)
Public defence
2021-09-17, https://kth-se.zoom.us/j/63998476971, Stockholm, 10:00 (English)
Opponent
Supervisors
Available from: 2021-08-30 Created: 2021-08-27 Last updated: 2022-06-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records

de Giorgio, AndreaMaffei, AntonioMonetti, Fabio MarcoRoci, MalvinaOnori, MauroWang, Lihui

Search in DiVA

By author/editor
de Giorgio, AndreaCacace, StefaniaMaffei, AntonioMonetti, Fabio MarcoRoci, MalvinaOnori, MauroWang, Lihui
By organisation
Production EngineeringDigital Smart ProductionManufacturing and Metrology SystemsSustainable Production Systems
Production Engineering, Human Work Science and Ergonomics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 348 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf