Human-System Co-Creative Design of Resilience

Sawaragi, Tetsuo

doi:10.1016/j.ifacol.2016.10.630

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Figure 4 shows the relationship between the above three research challenges. The typical background idea is that establishment of resilience requires the co-creation between human beings and systems (Sawaragi [12]). It is needed to design systems that derive and cultivate the existing empirical knowledge, which requires to provide adequate constraints and supports with workers and to derive the workers' responses accordingly.…”

Section: Sources Of the Performance Variabilitymentioning

confidence: 99%

“…Not by eliminating human beings or ultimately limiting their work, but by pursuing system Fig. 4 An overview of the human-system co-creative design of resilience (Sawaragi [12]) design that allows some human judgment left in the loop, resilience can be established through the fusion of knowledge of human beings and automation.…”

Section: Sources Of the Performance Variabilitymentioning

confidence: 99%

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Design of resilient socio-technical systems by human-system co-creation

Sawaragi

2020

Artif Life Robotics

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The concept of resilience is commonly used to represent "an ability to adapt to a changing environment and to survive flexibly despite facing difficulties." This definition of resilience emphasized its aspects of unpredictable responsiveness to external disturbances as well as self-organized phenomena, which seemed to be close to the definition of complex adaptive systems. This article summarizes the technical challenges to consider the implementation of a concept of resilience into systems from a variety of perspectives. Then, challenges to tackle with the resilience to variabilities in production plans, in work quality, in empirical knowledge, in human-automation systems, and in organizations are presented.

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Section: Sources Of the Performance Variabilitymentioning

confidence: 99%

Section: Sources Of the Performance Variabilitymentioning

confidence: 99%

Design of resilient socio-technical systems by human-system co-creation

Sawaragi

2020

Artif Life Robotics

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“…The so-called "narrow window" control is often applied to solve the problem of product quality fluctuation [5,6], but it is bound to increase production costs. Currently, selection and optimization of processing parameters in production are usually conducted by experts and skilled workers, but with staff turnover and retirement, their expert experience might be lost [7][8][9]. Therefore, a solution is needed to digitize expert knowledge and experience.…”

Section: Introductionmentioning

confidence: 99%

Material Quality Filter Model: Machine Learning Integrated with Expert Experience for Process Optimization

Wang

Li²,

Pan

et al. 2023

Metals

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In the process of material production, the mismatch between raw material parameters and manufacturing processing parameters may lead to fluctuations in product properties and ultimately to unstable or unqualified product quality. In this paper, we propose the concept of the Quality Filter model for process optimization. The Quality Filter model uses the property prediction model as a surrogate model and integrates expert experience and process window constraints to construct a loss function. When raw material parameters are supplied, the suitable processing parameters can be automatically matched, and the processing fluctuation can be used to hedge the fluctuations in raw material, thus stabilizing the product quality and improving overall product properties. A trial production data set of 128 samples of wind power steel from a steel plant was used to test the model. We selected the ellipsoid discriminant analysis model with a classification accuracy rate of 82.81% as the surrogate model, which gives a highly interpretable visualization result. Finally, the results show that the properties of the samples that underwent the optimized process are improved.

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