Single Minute Exchange of Die (SMED)

Conrad, Ralph W.

doi:10.1007/978-3-662-48552-1_8

Cited by 4 publications

(2 citation statements)

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“…In traditional, non-CPPS based factories, the "Single Minute Exchange of Die" (SMED) method is used in order to minimize changeover time and well known in Lean Management [37]. Positive effects of quick set-up include increased equipment utilization (OEE) and improved transparency in planning and production.…”

Section: Suitability Of Machine Learning For Quick Changeovermentioning

confidence: 99%

Advances in Machine Learning Detecting Changeover Processes in Cyber Physical Production Systems

Engelmann

Miller

Bräutigam

et al. 2020

JMMP

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The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.

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Section: Suitability Of Machine Learning For Quick Changeovermentioning

confidence: 99%

Advances in Machine Learning Detecting Changeover Processes in Cyber Physical Production Systems

Engelmann

Miller

Bräutigam

et al. 2020

JMMP

View full text Add to dashboard Cite

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“…Especially small and medium-sized enterprises (SMEs) have played an essential role in supplying large enterprises with (customized) parts for products/intermediate goods, which are produced in small batch-sizes in a high mix, low volume (HMLV) production. In that, efficient retooling and the optimization of setting-up times is mandatory, as this currently takes around 1.8 h of lost manufacturing time [7]. Additionally, precise real-time localization of goods is foreseen to significantly cost-optimize intra-logistics.…”

Section: Introductionmentioning

confidence: 99%

Reference Network and Localization Architecture for Smart Manufacturing based on 5G

Ludwig¹,

Aschenbrenner²,

Scharle³

et al. 2022

Preprint

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5G promises to shift Industry 4.0 to the next level by allowing flexible production. However, many communication standards are used throughout a production site, which will stay so in the foreseeable future. Furthermore, localization of assets will be equally valuable in order to get to a higher level of automation. This paper proposes a reference architecture for a convergent localization and communication network for smart manufacturing that combines 5G with other existing technologies and focuses on high-mix low-volume application, in particular at small and medium-sized enterprises. The architecture is derived from a set of functional requirements, and we describe different views on this architecture to show how the requirements can be fulfilled. It connects private and public mobile networks with local networking technologies to achieve a flexible setup addressing many industrial use cases.

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