In-Line Sensor Fusion and Predictive Quality Modelling: AI-Driven Quality Control Frameworks for Revitalising U.S. Manufacturing Operations
Keywords:
in-line sensor fusion, predictive quality modelling, quality control frameworks, revitalising u.s. manufacturing operations, machine learningAbstract
Artificial intelligence (AI) and machine learning (ML) are changing the face of a variety of fields including agriculture, social media marketing, healthcare, and manufacturing. One of the most expected applications for ML in the future is the successful automation of the quality control (QC) process for high-volume production in manufacturing. ML could solve the challenges posed by older methods of QC and reduce cost while maintaining or improving on product quality.Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
