Prosocial behavior among human workers in robot-augmented production teams : a field-in-the-lab experiment

Human-machine interaction has raised a lot of interest in various academic disciplines, but it is still unclear how human-human interaction is affected when robots join the team. Robotics has already been integral to manufacturing since the 1970s. With the integration of AI, however, they are increasingly working alongside humans in shared spaces. We conducted an experiment in a learning factory to investigate how a change from a human-human work context to a hybrid human-robot work context affects participants\u27 valuation of their production output as well as their pro-sociality among each other. Learning factories are learning, teaching, and research environments in engineering university departments. These factory environments allow control over the production environment and incentives for participants. Our experiment suggests that the robot\u27s presence increases sharing behavior among human workers, but there is no evidence that rewards earned from production are valued differently. We discuss the implications of this approach for future studies on human-machine interaction

Design, Implementation and Evaluation of Reinforcement Learning for an Adaptive Order Dispatching in Job Shop Manufacturing Systems

Modern production systems tend to have smaller batch sizes, a larger product variety and more complex material flow systems. Since a human oftentimes can no longer act in a sufficient manner as a decision maker under these circumstances, the demand for efficient and adaptive control systems is rising. This paper introduces a methodical approach as well as guideline for the design, implementation and evaluation of Reinforcement Learning (RL) algorithms for an adaptive order dispatching. Thereby, it addresses production engineers willing to apply RL. Moreover, a real-world use case shows the successful application of the method and remarkable results supporting real-time decision-making. These findings comprehensively illustrate and extend the knowledge on RL

Automated Derivation of Optimal Production Sequences from Product Data

Customer specific, individual products nowadays lead to larger product variance and shorter time to market. This requires efficient production system planning. In addition, due to a larger system complexity, each iteration of the planning process itself gets soaringly complex. Time constraints and complexity, therefore, emphasize the necessity of supporting humans in planning modern production systems. Especially the determination of the production sequence holds immense potential and tends to get even more complex within specific production technologies. Exemplarily, this article focuses on welding sequences. Here, domain knowledge from product development and production planning needs to be holistically integrated. Furthermore, implicit, historic knowledge needs to be formalized and used in today’s planning tasks. This article introduces a methodical approach and a corresponding toolchain to derive optimal production sequences from customer product data which is validated using welding processes. For this, firstly, a reference system is build up consisting of historic product data (e.g. part list, CAD data) and corresponding production system characteristics (e.g. number and specifications of machines). The main aspect is to use similarities between the new product variant and assemblies from the reference system, to determine implications of product specifications on the process sequence. Overall, such restrictions can be displayed using Model-Based Systems Engineering. Relevant information (e.g. weld seam lengths) can be used to compute the optimal weld seam order regarding minimal cycle times, for example. This requires a parametric encoding of product and production system. In a nutshell, this approach covers the automated derivation of an optimal production sequence for new product variants, based on system information and product similarities, to tackle time constraints and complexity by suggesting initial planning drafts

Activation of Rac-1 and RhoA contributes to podocyte injury in chronic kidney disease

Rho-family GTPases like RhoA and Rac-1 are potent regulators of cellular signaling that control gene expression, migration and inflammation. Activation of Rho-GTPases has been linked to podocyte dysfunction, a feature of chronic kidney diseases (CKD). We investigated the effect of Rac-1 and Rho kinase (ROCK) inhibition on progressive renal failure in mice and studied the underlying mechanisms in podocytes. SV129 mice were subjected to 5/6-nephrectomy which resulted in arterial hypertension and albuminuria. Subgroups of animals were treated with the Rac-1 inhibitor EHT1846, the ROCK inhibitor SAR407899 and the ACE inhibitor Ramipril. Only Ramipril reduced hypertension. In contrast, all inhibitors markedly attenuated albumin excretion as well as glomerular and tubulo-interstitial damage. The combination of SAR407899 and Ramipril was more effective in preventing albuminuria than Ramipril alone. To study the involved mechanisms, podocytes were cultured from SV129 mice and exposed to static stretch in the Flexcell device. This activated RhoA and Rac-1 and led via TGFβ to apoptosis and a switch of the cells into a more mesenchymal phenotype, as evident from loss of WT-1 and nephrin and induction of α-SMA and fibronectin expression. Rac-1 and ROCK inhibition as well as blockade of TGFβ dramatically attenuated all these responses. This suggests that Rac-1 and RhoA are mediators of podocyte dysfunction in CKD. Inhibition of Rho-GTPases may be a novel approach for the treatment of CKD

Prosocial behavior among human workers in robot-augmented production teams—A field-in-the-lab experiment

IntroductionHuman-machine interaction has raised a lot of interest in various academic disciplines, but it is still unclear how human-human interaction is affected when robots join the team. Robotics has already been integral to manufacturing since the 1970s. With the integration of AI, however, they are increasingly working alongside humans in shared spaces.MethodsWe conducted an experiment in a learning factory to investigate how a change from a human-human work context to a hybrid human-robot work context affects participants' valuation of their production output as well as their pro-sociality among each other. Learning factories are learning, teaching, and research environments in engineering university departments. These factory environments allow control over the production environment and incentives for participants.ResultsOur experiment suggests that the robot's presence increases sharing behavior among human workers, but there is no evidence that rewards earned from production are valued differently.DiscussionWe discuss the implications of this approach for future studies on human-machine interaction