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Artificial intelligence in washing machines: a systematic review.

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Title: Artificial intelligence in washing machines: a systematic review.
Authors: Pembe Muhtaroğlu, F. Canan1 (AUTHOR) canan.pembe@tubitak.gov.tr, Bilgen, İsmail1 (AUTHOR) ibilgen@itu.edu.tr, Delibalta, Murat1 (AUTHOR) murat_delibalta@outlook.com, Kiremit, Erman2 (AUTHOR) erman.kiremit@beko.com, Haklıdır, Mehmet1 (AUTHOR) mehmet.haklidir@tubitak.gov.tr
Source: Neural Computing & Applications. Apr2026, Vol. 38 Issue 8, p1-33. 33p.
Subjects: Artificial intelligence, Washing machines, Laundry industry, Machine learning, Acquisition of data, Condition-based maintenance, Resource allocation, Automation
Abstract: Washing machines have undergone remarkable advancements, transforming laundry from a manual task to an automated process. Recent advancements in artificial intelligence drive the development of fully autonomous washing machines requiring minimal human intervention. Integrating artificial intelligence into washing machines offers substantial improvements in estimating laundry properties, optimizing washing performance and resource consumption, and enabling predictive maintenance. The integration process involves several steps. First, sensor data is collected and preprocessed. Next, artificial intelligence models are developed. Finally, these models are deployed onto the washing machine's microcontroller unit. This review paper explores the state-of-the-art applications of artificial intelligence in washing machines, examining existing studies and highlighting research gaps. We investigate various methodologies employed to improve washing machine autonomy and efficiency, and suggest future research directions to address remaining challenges. To the best of our knowledge, this is the first comprehensive review focusing specifically on artificial intelligence applications in washing machines, providing a foundation for future advancements and innovations in this rapidly evolving field. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192924297
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Apr2026, Vol. 38 Issue 8, p1-33. 33p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Washing+machines%22">Washing machines</searchLink><br /><searchLink fieldCode="DE" term="%22Laundry+industry%22">Laundry industry</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink>
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  Data: Washing machines have undergone remarkable advancements, transforming laundry from a manual task to an automated process. Recent advancements in artificial intelligence drive the development of fully autonomous washing machines requiring minimal human intervention. Integrating artificial intelligence into washing machines offers substantial improvements in estimating laundry properties, optimizing washing performance and resource consumption, and enabling predictive maintenance. The integration process involves several steps. First, sensor data is collected and preprocessed. Next, artificial intelligence models are developed. Finally, these models are deployed onto the washing machine's microcontroller unit. This review paper explores the state-of-the-art applications of artificial intelligence in washing machines, examining existing studies and highlighting research gaps. We investigate various methodologies employed to improve washing machine autonomy and efficiency, and suggest future research directions to address remaining challenges. To the best of our knowledge, this is the first comprehensive review focusing specifically on artificial intelligence applications in washing machines, providing a foundation for future advancements and innovations in this rapidly evolving field. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1007/s00521-026-12027-w
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        Text: English
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        PageCount: 33
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Washing machines
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      – SubjectFull: Laundry industry
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      – SubjectFull: Machine learning
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      – SubjectFull: Acquisition of data
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      – SubjectFull: Condition-based maintenance
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      – SubjectFull: Resource allocation
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      – SubjectFull: Automation
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            NameFull: Pembe Muhtaroğlu, F. Canan
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              Text: Apr2026
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