Study of the impact of projection-based assistance systems for improving the learning curve in assembly processes

Mark, Benedikt G.; Rauch, Erwin; Matt, Dominik T.

doi:10.1016/j.procir.2020.05.018

Cited by 12 publications

(7 citation statements)

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“…The learning curve is also studied for determining input prices to monitor technical changes in the energy transfer process (Zhou et al , 2019), economic scenario planning (Ramirez et al , 2020). Besides, the learning curve is used for the reduction of labour cost and product assembly (Ören and Erol, 2009), product assembly (Mark et al , 2020), analysis of the impact of renewable energy systems (Hong et al , 2020), virtual lab experiments (Francis et al , 2016) and cost/time optimization problems (Abdelkhalek et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Determining aircraft maintenance times in civil aviation under the learning effect

Atıcı

Şenol

2022

AEAT

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Purpose Scheduling of aircraft maintenance operations is a gap in the literature. Maintenance times should be determined close to the real-life to schedule aircraft maintenance operations effectively. The learning effect, which has been studied extensively in the machine scheduling literature, has not been investigated on aircraft maintenance times. In the literature, the production times under the learning effect have been examined in numerous studies but for merely manufacturing and assembly lines. A model for determining base and line maintenance times in civil aviation under the learning effect has not been proposed yet. It is pretty challenging to determine aircraft maintenance times due to the various aircraft configurations, extended maintenance periods, different worker shifts and workers with diverse experience and education levels. The purpose of this study is to determine accurate aircraft maintenance times rigorously with a new model which includes the group learning effect with the multi-products and shifts, plateau effect, multi sub-operations and labour firings/rotations. Design/methodology/approach Aircraft maintenance operations are carried out in shifts. Each maintenance operation consists of many sub-operations that are performed by groups of workers. Thus, various models, e.g. learning curve for maintenance line (MLC), MLC with plateau factor (MPLC), MLC with group factor (MGLC) were developed and used in this study. The performance and efficiency of the models were compared with the current models in the literature, such as the Yelle Learning model (Yelle), single learning curve (SLC) model and SLC with plateau factor model (SLC-P). Estimations of all these models were compared with actual aircraft maintenance times in terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square of the error (MSE) values. Seven years (2014–2020) maintenance data of one of the top ten maintenance companies in civil aviation were analysed for the application and comparison of learning curve models. Findings The best estimations in terms of MAD, MAPE and MSE values are, respectively, gathered by MGLC, SLC-P, MPLC, MLC, SLC and YELLE models. This study revealed that the models (MGLC, SLC-P, MPLC), including the plateau factor, are more efficient in estimating accurate aircraft maintenance times. Furthermore, MGLC always made the closest estimations to the actual aircraft maintenance times. The results show that the MGLC model is more accurate than all of the other models for all sub-operations. The MGLC model is promising for the aviation industry in determining aircraft maintenance times under the learning effect. Originality/value In this study, learning curve models, considering groups of workers working in shifts, have been developed and employed for the first time for estimating more realistic maintenance times in aircraft maintenance. To the best of the authors’ knowledge, the effect of group learning on maintenance times in aircraft maintenance operations has not been studied. The novelty of the models are their applicability for groups of workers with different education and experience levels working in the same shift where they can learn in accordance with their proportion of contribution to the work and learning continues throughout shifts. The validity of the proposed models has been proved by comparing actual aircraft maintenance data. In practice, the MGLC model could efficiently be used for aircraft maintenance planning, certifying staff performance evaluations and maintenance trainings. Moreover, aircraft maintenance activities can be scheduled under the learning effect and a more realistic maintenance plan could be gathered in that way.

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Section: Literature Reviewmentioning

confidence: 99%

Determining aircraft maintenance times in civil aviation under the learning effect

Atıcı

Şenol

2022

AEAT

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“…Transforaminal perkütan endoskopik lombar diskektomide belirli tekrar sonrasında öğrenme kararlı bir düzeye ulaşır [66]. Ayrıca öğrenme eğrisi, enerji transfer sürecinde teknik değişimlerin izlenmesi için girdi fiyatların belirlenmesinde [67], ekonomik senaryo planlamada [68], iş gücü maliyetinin azaltılmasında [69], uçak gövde maliyetinin belirlenmesinde [70], ürün montajında [71], yenilebilir enerji sistemlerinin şebeke üzerindeki etkisinin analizinde [72], sanal laboratuvar deneylerinde [73] ve maliyet/zaman optimizasyon problemlerinde [74] kullanılmıştır.…”

Section: Li̇teratür Araştirmasiunclassified

Öğrenme Eğrilerinin Karşılaştırılması

Atıcı

2021

Türk Doğa Ve Fen Dergisi

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Öz: Şirketlerin iç ve dış süreçlerinde öğrenme eğrisi önemli bir yer tutar. Üretim sektöründe üretim miktarı veya işin tekrar sayısı arttıkça üretim süresi belirli bir oranda azalır. Bu çalışmada farklı öğrenme eğri modelleri için üretim miktarının süre üzerindeki etkisi incelenmiştir. Öğrenme eğrisi modellerinin karşılaştırılmasında kullanılan veriler Matlab 2020b programında düzgün dağılımla rastgele üretilmiştir. Üretilen veri seti, öğrenme eğrisi eşitlikleri ve literatürde önerilen katsayılar kullanılarak üretim için gerekli iş gücü süresi Matlab 2020b yazılımında hesaplanmıştır. Öğrenme eğrisi modelleri için tekrar sayısına bağlı olarak gerekli iş gücü miktarının değişimi araştırılmıştır. İncelenen tüm öğrenme modelleri, üretim miktarının artmasıyla üretim süresinin kısalacağını öngörmüştür. Üretim süresinin belirlenmesinde kullanılacak öğrenme eğrisi modeli üretim işlemine uygun olarak seçilmelidir. Birey ve grup arasında tecrübe aktarımının modellenmesine imkân tanıyan insan öğrenme optimizasyon algoritması iş postaları halinde çalışılan bakım hatlarında üretim süresinin belirlenmesine yönelik büyük bir potansiyele sahiptir.

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“…Projection-Based Assistance System: A projection-based assistance system is a system, which projects information, e.g. instructions, directly on the operator's field of vision on the workplace (Mark et al 2020b). They can be flexibly designed in order to be moved to any workplace (Hinrichsen et al 2018).…”

Section: Cognitive Worker Assistance Systemsmentioning

confidence: 99%

Industrial Assistance Systems to Enhance Human–Machine Interaction and Operator’s Capabilities in Assembly

Mark

Rauch

Matt

2021

Implementing Industry 4.0 in SMEs

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In the context of digitalization and Industry 4.0, the world of work is changing comprehensively. Smaller lot sizes and increasing variability of products in the modern industrial production present new challenges for operators working in manual assembly. Industrial assistance systems help the worker during these production tasks to enhance their capabilities. The development of these systems is not only characterized by questions of the potential feasibility of new technical systems but also by the possibilities of a closer cooperation between humans and machines with the aim to synergize the outstanding abilities of humans with the special features of machines to bring together the best from both worlds. This chapter presents solutions for human–machine interaction and automation and delivers insight into different possibilities to enhance the various types of operator’s skills in industrial assembly. With this knowledge, each worker can be individually equipped with suitable supporting systems in order to be best prepared for future challenges in the daily production.

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Study of the impact of projection-based assistance systems for improving the learning curve in assembly processes

Cited by 12 publications

References 13 publications

Determining aircraft maintenance times in civil aviation under the learning effect

Determining aircraft maintenance times in civil aviation under the learning effect

Öğrenme Eğrilerinin Karşılaştırılması

Industrial Assistance Systems to Enhance Human–Machine Interaction and Operator’s Capabilities in Assembly

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