Mutualistic and Adaptive Human-Machine Collaboration Based on Machine Learning in an Injection Moulding Manufacturing Line

Bettoni, Andrea; Montini, Elias; Righi, Massimiliano; Villani, Valeria; Tsvetanov, Radostin; Borgia, Stefano; Secchi, Cristian; Carpanzano, Emanuele

doi:10.1016/j.procir.2020.04.119

Cited by 46 publications

(18 citation statements)

References 22 publications

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“…In particular, regarding the use of the adaptability capability to improve human factors, the majority of works have been focusing on physical ergonomics, and especially on measuring the status of the operator. Indeed, works such as (Bragança et al, 2019;Kim et al, 2021;Bettoni et al, 2020) developed systems to measure the physical (and mental) workload on the human operator, and based on this information the cobot dynamically adapts to him/her, by taking physically demanding tasks (Peternel et al, 2018) , or by bringing the operator into a suitable ergonomic working pose (Kim et al, 2021) . It should be noted that the majority of this methods are also adopted to reduce the mental workload on the operator, as seen in Faber et al, 2017) .…”

Section: Human Factors and Cobot Capabilitiesmentioning

confidence: 99%

“…This feature reduced the perceived response time, improving the trust in the system; moreover, the authors stated that communication is fundamental to improve the sense of safety. Connectivity is also used to adapt the other elements of automation composing the system to the operator and improve the physical ergonomics and mental workload, as seen in (Bettoni et al, 2020) . Lastly, other forms of nonverbal communication towards the operator are based on the motion of the cobot, as seen in (Terzioglu et al, 2020) , where the authors adopted arm motions and hand gestures (with the gripper) to develop a non-verbal communication used to increase the sense of trust and acceptance.…”

Section: Human Factors and Cobot Capabilitiesmentioning

confidence: 99%

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Human factors in cobot era: a review of modern production systems features

et al. 2022

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Collaborative robots are increasingly common in modern production systems, since they allow to merge the productivity of automated systems with the flexibility and dexterity of manual ones. The direct interaction between the human and the robot can be the greatest advantage and the greatest limit of collaborative systems at the same time, depending on how it affects human factors like ergonomics and mental stress. This work presents an overview of collaborative robotics considering three main dimensions: robot features, modern production systems characteristics and human factors. A literature review on how such dimensions interact is addressed and a discussion on the current state of the art is presented, showing the topics that have been already widely explored and the research gaps that should be fulfilled in the future.

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Section: Human Factors and Cobot Capabilitiesmentioning

confidence: 99%

Section: Human Factors and Cobot Capabilitiesmentioning

confidence: 99%

Human factors in cobot era: a review of modern production systems features

et al. 2022

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“…While several past studies suggested that the activities of automation would replace human labor [5], other studies revealed that the knowledge worker and machine will complement each other [24][25][26]. Very little of the existing literature could highlight how knowledge workers' activities and automation have changed the entire structure of the organization.…”

Section: Contribution To the Literaturementioning

confidence: 99%

“…The study revealed that automation can substitute humans but also provide a contribution to the process of human labor to enhance human skills, providing a new interconnectedness that eases the flow of knowledge. The humanmachine interface has a high potential for improving employees' knowledge by syncing with the Internet of things devices to give new insight to information and data which boosts human efficiency [24]. The human-machine interface provides an atmosphere easy for the transfer of knowledge [25].…”

Section: Technology Acceptance Modelmentioning

confidence: 99%

The Impact of Automation and Knowledge Workers on Employees’ Outcomes: Mediating Role of Knowledge Transfer

Mote

Karadaş

2022

Sustainability

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While there are existing empirical reviews regarding the relation between knowledge workers (KW) and knowledge transfer (KT), no study has examined the association of automation and knowledge transfer. The study describes knowledge workers as employees embedded with special abilities and having the main role of transfering their abilities to other workers in the organization. Additionally, automation (AUT) is described in the study as a technological process embedded with tacit knowledge. Therefore, using knowledge worker productivity theory and the technology acceptance model, this study aims to recommend and test a research model which examines the mediating role of knowledge transfer between automation, knowledge workers, and employee outcomes (employee creativity (EC) and innovative performance (IP)). The study evaluates the significant influence of knowledge transfer on innovative performance and creativity of other employees within the organization. This study applied a judgmental non-probability sampling research strategy to gather data from employees at an industrial firm in Japan by administering a questionnaire via Google Form. The data were analyzed with partial least squares based on structural equation modeling aimed at testing the predictive power and relationships of the model estimates. The results found support for the mediating role of knowledge transfer between automation and employee creativity as well as between automation and innovative performance. However, the study found partial support that knowledge transfer mediates the relationship between knowledge worker and employee creativity, as well as knowledge worker and innovative performance. In the same, automation and knowledge workers have significant impacts on knowledge transfer. Knowledge transfer has a significant impact on employee creativity and innovative performance.

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“…Shrinkage behavior of plastic parts is related to many factors such as processing equipment, material properties, mold structure, plastic parts geometry, and injection molding process parameters [ 29 , 30 , 31 ]. For gears whose dimensions have been determined in practical production, the key concern is the influence of injection molding process parameters on shrinkage.…”

Section: Introductionmentioning

confidence: 99%

A Practical Numerical Approach to Characterizing Non-Linear Shrinkage and Optimizing Dimensional Deviation of Injection-Molded Small Module Plastic Gears

2021

Polymers

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This paper was aimed at finding out the solution to the problem of insufficient dimensional accuracy caused by non-linear shrinkage deformation during injection molding of small module plastic gears. A practical numerical approach was proposed to characterize the non-linear shrinkage and optimize the dimensional deviation of the small module plastic gears. Specifically, Moldflow analysis was applied to visually simulate the shrinkage process of small module plastic gears during injection molding. A 3D shrinkage gear model was obtained and exported to compare with the designed gear model. After analyzing the non-linear shrinkage characteristics, the dimensional deviation of the addendum circle diameter and root circle diameter was investigated by orthogonal experiments. In the end, a high-speed cooling concept for the mold plate and the gear cavity was proposed to optimize the dimensional deviation. It was confirmed that the cooling rate is the most influential factor on the non-linear shrinkage of the injection-molded small module plastic gears. The dimensional deviation of the addendum circle diameter and the root circle diameter can be reduced by 22.79% and 22.99% with the proposed high-speed cooling concept, respectively.

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Mutualistic and Adaptive Human-Machine Collaboration Based on Machine Learning in an Injection Moulding Manufacturing Line

Cited by 46 publications

References 22 publications

Human factors in cobot era: a review of modern production systems features

Human factors in cobot era: a review of modern production systems features

The Impact of Automation and Knowledge Workers on Employees’ Outcomes: Mediating Role of Knowledge Transfer

A Practical Numerical Approach to Characterizing Non-Linear Shrinkage and Optimizing Dimensional Deviation of Injection-Molded Small Module Plastic Gears

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