Sensor-driven Analysis of Manual Assembly Systems

Kärcher, Susann; Cuk, Emir; Denner, Timo; Görzig, David; Günther, Lisa C.; Hansmersmann, A.; Riexinger, Günther; Bauernhansl, Thomas

doi:10.1016/j.procir.2018.03.241

Cited by 16 publications

(3 citation statements)

References 9 publications

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“…The objective is to create transparency and to identify optimization potentials in assembly, data-based. A comprehensive description of the technical development can be found in Kärcher et al [15].…”

Section: Validationmentioning

confidence: 99%

Capability-Based Implementation of Digital Service Innovation in SMEs

Görzig

Kärcher

Bauernhansl

2019

IFIP Advances in Information and Communication Technology

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In order to be successful in the market in the long term, enterprises must not only continuously optimize their operations but also constantly innovate. It is necessary to develop radically new digital services which disrupt their existing business model. For small and medium-sized enterprises (SMEs), it is a major challenge to put the generated solutions into practice. They have to cope with digitalization while having limited resources. The presented paper starts by examining the challenges for SMEs in the context of digitalization. Thus, an analysis of the approaches used in practice for implementing digital services in 36 SMEs is carried out. Essential differences between start-ups and established SMEs are identified. On this basis, a capability-based, agile methodology is presented which supports enterprises in advancing newly developed, digital services from prototype status to the customer's environment. Finally, the methodology is put into practical application within an innovation project in an SME.

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Section: Validationmentioning

confidence: 99%

Capability-Based Implementation of Digital Service Innovation in SMEs

Görzig

Kärcher

Bauernhansl

2019

IFIP Advances in Information and Communication Technology

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“…In the 'data gathering' phase actual data is collected by a system in which sensors are installed on components, tools and equipment to automatically identify process steps and record times [19]. In particular, acceleration sensors, magnetometers and gyroscopes are used for this purpose.…”

Section: Modeling Manual Assembly System In Context Of Deriving Best ...mentioning

confidence: 99%

Modeling Manual Assembly System to Derive Best Practice from Actual Data

Kärcher

Görzig

Bauernhansl

2019

IFIP Advances in Information and Communication Technology

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In manual assembly systems, there is often little transparency and great potential for optimization, especially in assembly systems with small batch sizes. In this paper, a model is developed that supports an approach to automated assembly optimization. For this optimization, actual data is collected in manual assemblies. Based on the data, the optimized assembly sequence is derived by developing a best practice. Best practice describes a combination of assembly processes performed by the workers during the data collection. The model shows the relationships and the dependencies in the assembly systems and allows to improve it. First, the considered assembly system is defined as a socio-technical system and general modeling principles are prepared. After presenting the benchmark approach to derive the best practice, the requirements for the model are identified. Then, the model is developed in four steps: The system boundary is defined, the features are described, and the model is formalized. Finally, the model is applied and tested in an example project and its purposefulness is shown.

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“…This is also the case for different sensors that can be used to characterize motion or provide feedback. A trend exists to assist human motion with sensors and algorithms for different motor skills, ranging from sport applications [ 20 ] to music education [ 21 ] to surgery [ 22 ] to industry tasks [ 23 , 24 , 25 , 26 ]. However, so far, these applications rarely implement insights from motor learning to optimize the learning process.…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Vandevoorde

Vollenkemper

Schwan

et al. 2022

Sensors

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Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

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Sensor-driven Analysis of Manual Assembly Systems

Cited by 16 publications

References 9 publications

Capability-Based Implementation of Digital Service Innovation in SMEs

Capability-Based Implementation of Digital Service Innovation in SMEs

Modeling Manual Assembly System to Derive Best Practice from Actual Data

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

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