Towards Active Learning Based Smart Assistant for Manufacturing

Zajec, Patrik; Rožanec, Jože M.; Novalija, Inna; Fortuna, Blaž; Mladenić, Dunja; Kenda, Klemen

doi:10.1007/978-3-030-85910-7_31

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Figure 4 shows a variety of engineering activities covered in the publications. The green ones on the left were also mentioned in the 'AI-LCE' paper [46]. In seven papers, we found more categories like 'Manufacturing' and five more categories that we kept in 'Others'.…”

Section: Fig 4 Engineering Activitiesmentioning

confidence: 70%

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Artificial Intelligence-Based Life Cycle Engineering in Industrial Production: A Systematic Literature Review

et al. 2022

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This paragraph of the first footnote will contain support information, including sponsor and financial support acknowledgement.ABSTRACT.For the last few years, cases of applying artificial intelligence (AI) to engineering activities towards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematically reach higher and productivity levels, owing to its holistic perspective and consideration of economic and environmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE) we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AI techniques, (2) the current AI-improved LCE subfields and (3) the subfields with highly enhanced by AI. A specific set of inclusion and exclusion criteria were used to identify and select academic papers from several fields, i.e. production, logistics, marketing and supply chain and after the selection process described in the paper we ended up with 42 scientific papers. The study and analysis show that there are many AI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, and Infrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall, the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance and Repair are the top explored LCE subfields whereas logistics and Procurement are the least explored subareas. Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strong research funding and focus on Industry 4.0; Germany is standing out with numbers of publications. The in-depth analysis of selected and relevant scientific papers are helpful in getting a more correct picture of the area which enables a more systematic approach to AI-LCE in the future.

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Section: Fig 4 Engineering Activitiesmentioning

confidence: 70%

“…France 2 [15], [25] Italy 2 [2], [53] Poland 2 [30], [40] South Africa 2 [21], [54] Bosnia and Herzegovnia 2 [20] India 2 [36], [22] Sweden 2 [1], [47] Finland 1 [49] Hungary 1 [37] Japan 1 [51] Russia 1 [31] Slovakia 1 [27] Slovenia 1 [46] Spain 1 [17] Total 42…”

Section: Fig 5 the Number Of Papers By Engineering Activitiesmentioning

confidence: 99%

Artificial Intelligence-Based Life Cycle Engineering in Industrial Production: A Systematic Literature Review

et al. 2022

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“…They realized that delays in some situations can create a more human-like environment, and hence a better symbiosis between humans and chatbots. • Zajec et al [103] have developed an assistant with machine learning capabilities applied to help humans in inventory forecasting decisions. After the algorithm was trained for a long time, decision-makers are assisted in their analyses and final decisions towards turning more accurate the activities of production planning and order fulfilment.…”

Section: Related Workmentioning

confidence: 99%

Softbots 4.0: Supporting Cyber-Physical Social Systems in Smart Production Management

Rabelo¹,

Zambiasi²,

Romero³

2023

Int J Ind Eng Manag

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This paper presents the results of a research work that aimed at investigating the usage of softbots -as digital intelligent software assistants -as a supporting technology to help in excelling smart production management composed of cyber-physical systems (CPS). This work also attempts to differentiate the many equivalent terms used in the literature to softbots, trying to demonstrate that all of them are just types of softbots. Softbots 4.0 acts as a smart humanmachine interface, representing a digital virtual assistant to handle human-related issues when implementing the concept of Operator 4.0. Yet, the concept of Softbot 4.0 is framed against the RAMI 4.0 model in a way to show how it can also be taken as an (intelligent) manufacturing entity. The main goal of Softbots 4.0 is to support the Operator 4.0 in interfacing with smart machines, robots, and systems, aiding people in the automatic, planned or pro-active execution of different, repetitive or complex tasks, efficiently in a more symbiotic human-machine environment. Five different cases have been selected for the assessment, implemented as software prototypes in different production management and shop floor control scenarios close to real CPS. The high potential of the softbots approach could be observed, especially when combined with other enabling technologies for Industry 4.0. A global assessment and reflections on these experiments are discussed at the end.

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Advancing Manufacturing Through Artificial Intelligence: Current Landscape, Perspectives, Best Practices, Challenges, and Future Direction

Rakholia,

Suárez-Cetrulo,

Singh

et al. 2024

IEEE Access

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Towards Active Learning Based Smart Assistant for Manufacturing

Cited by 4 publications

References 12 publications

Artificial Intelligence-Based Life Cycle Engineering in Industrial Production: A Systematic Literature Review

Artificial Intelligence-Based Life Cycle Engineering in Industrial Production: A Systematic Literature Review

Softbots 4.0: Supporting Cyber-Physical Social Systems in Smart Production Management

Advancing Manufacturing Through Artificial Intelligence: Current Landscape, Perspectives, Best Practices, Challenges, and Future Direction

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