Application of automation for in-line quality inspection, a zero-defect manufacturing approach

Azamfirei, Victor; Psarommatis, Foivos; Lagrosen, Yvonne

doi:10.1016/j.jmsy.2022.12.010

Cited by 74 publications

(14 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the presented research, we followed a user-centered approach while implementing and training two working memory architectures on an industrial RB-KAIROS + robot ( Robotnik Valencia, Spain, 2023 ): One of these architectures was based on GRU ( Cho et al, 2014 ), a commonly used state-of-the-art architecture, the other was based on WorkMATe ( Kruijne et al, 2021 ), a biologically-inspired alternative. Emphasizing the humans’ perspective on HRI, we considered potential users’ ideas and perceptions of robot navigation already before initiating the implementation and training processes as recommended by previous research (e.g., Mahmood et al, 2000 ; Ben Allouch et al, 2009 ; Schiffhauer et al, 2016 ; Bernotat and Eyssel, 2017a ; Diehl et al, 2017 ; Azamfirei et al, 2023b ; Psarommatis et al, 2023b ; Lacroix et al, 2023 ). This approach was innovative because, so far, only little attention has been paid to the humans’ perspective when robot working memory was implemented and trained.…”

Section: Discussionmentioning

confidence: 99%

“…This could help to encounter the cause for possible resentments against robots and thus to enhance acceptance and comfort of HRI in shared environments. To really grasp the dynamics that underlie an efficient and comfortable HRI in various settings, however, we recommend a more detailed consideration of various groups of potential users (see also Azamfirei et al, 2023b ; Psarommatis et al, 2023b ). To illustrate, in our research, we opted for heterogeneous samples to avoid possible biases and to reflect a general view on HRI with an industrial robot that is equipped with human-aware robot navigation.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Remember me - user-centered implementation of working memory architectures on an industrial robot

Bernotat,

Landolfi,

Pasquali

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

The present research is innovative as we followed a user-centered approach to implement and train two working memory architectures on an industrial RB-KAIROS + robot: GRU, a state-of-the-art architecture, and WorkMATe, a biologically-inspired alternative. Although user-centered approaches are essential to create a comfortable and safe HRI, they are still rare in industrial settings. Closing this research gap, we conducted two online user studies with large heterogeneous samples. The major aim of these studies was to evaluate the RB-KAIROS + robot’s appearance, movements, and perceived memory functions before (User Study 1) and after the implementation and training of robot working memory (User Study 2). In User Study 1, we furthermore explored participants’ ideas about robot memory and what aspects of the robot’s movements participants found positive and what aspects they would change. The effects of participants’ demographic background and attitudes were controlled for. In User Study 1, participants’ overall evaluations of the robot were moderate. Participant age and negative attitudes toward robots led to more negative robot evaluations. According to exploratory analyses, these effects were driven by perceived low experience with robots. Participants expressed clear ideas of robot memory and precise suggestions for a safe, efficient, and comfortable robot navigation which are valuable for further research and development. In User Study 2, the implementation of WorkMATe and GRU led to more positive evaluations of perceived robot memory, but not of robot appearance and movements. Participants’ robot evaluations were driven by their positive views of robots. Our results demonstrate that considering potential users’ views can greatly contribute to an efficient and positively perceived robot navigation, while users’ experience with robots is crucial for a positive HRI.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Remember me - user-centered implementation of working memory architectures on an industrial robot

Bernotat,

Landolfi,

Pasquali

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…The creation and incorporation of efficient quality inspection techniques into AM processes, as well as any possible effects they may have on energy usage, should be the main areas of future study. Investigating in-process quality inspection methods might be very helpful since they might make it possible to see problems and take action before they result in component failure or further energy loss [204].…”

Section: Quality Inspection In Additive Manufacturingmentioning

confidence: 99%

Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research

May

Psarommatis

2023

Energies

Self Cite

View full text Add to dashboard Cite

Additive manufacturing (AM) offers unique capabilities in terms of design freedom and customization, contributing to sustainable manufacturing. However, energy efficiency remains a challenge in the widespread adoption of AM processes. In this paper, we present a comprehensive review of the current research on energy efficiency in AM, addressing challenges, opportunities, and future directions. Our analysis reveals a lack of standardization in the measurement and reporting of energy consumption, making it difficult to evaluate and compare the energy performance of various systems. We propose a holistic framework to address energy efficiency throughout the entire life cycle of the AM process, highlighting the importance of design optimization, material selection, advanced control systems, and energy management strategies. The paper also emphasizes the need for further research on the interactions between process parameters, along with the potential of integrating renewable energy sources into AM systems. This review offers valuable insights for both academics and industry professionals, calling for standardized methodologies and a focus on energy management to optimize energy efficiency in AM processes, ultimately enhancing competitiveness and sustainability in modern manufacturing.

show abstract

“…, 2022). In manufacturing, automated product inspection tools built on AI are challenging traditional manual product quality assurance processes (CBInsight, 2022; Azamfirei et al. , 2023).…”

mentioning

confidence: 99%

“…In product and service development, AI has found applications, amongst others, in agriculture to improve irrigation and seeding (Rural Industries, 2016;Talaviya et al, 2020), medicine to improve diagnostic instrumentation (Choy et al, 2018;Davenport and Kalakota, 2019) and in care settings to assist individual support tasks (Loveys et al, 2022). In manufacturing, automated product inspection tools built on AI are challenging traditional manual product quality assurance processes (CBInsight, 2022;Azamfirei et al, 2023).…”

mentioning

confidence: 99%

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Cebulla¹,

Szpak²,

Knight³

2023

IJWHM

View full text Add to dashboard Cite

PurposeArtificial Intelligence (AI) systems play an increasing role in organisation management, process and product development. This study identifies risks and hazards that AI systems may pose to the work health and safety (WHS) of those engaging with or exposed to them. A conceptual framework of organisational measures for minimising those risks is proposed.Design/methodology/approachAdopting an exploratory, inductive qualitative approach, the researchers interviewed 30 experts in data science, technology and WHS; 12 representatives of nine organisations using or preparing to use AI; and ran online workshops, including with 12 WHS inspectors. The research mapped AI ethics principles endorsed by the Australian government onto the AI Canvas, a tool for tracking AI implementation from ideation via development to operation. Fieldwork and analysis developed a matrix of WHS and organisational–managerial risks and risk minimisation strategies relating to AI use at each implementation stage.FindingsThe study identified psychosocial, work stress and workplace relational risks that organisations and employees face during AI implementation in a workplace. Privacy, business continuity and gaming risks were also noted. All may persist and reoccur during the lifetime of an AI system. Alertness to such risks may be enhanced by adopting a systematic risk assessment approach.Originality/valueA collaborative project involving sociologists, economists and computer scientists, the study relates abstract AI ethics principles to concrete WHS risks and hazards. The study translates principles typically applied at the societal level to workplaces and proposes a process for assessing AI system risks.

show abstract

Application of automation for in-line quality inspection, a zero-defect manufacturing approach

Cited by 74 publications

References 111 publications

Remember me - user-centered implementation of working memory architectures on an industrial robot

Remember me - user-centered implementation of working memory architectures on an industrial robot

Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Contact Info

Product

Resources

About