Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically.Hence,there is a growing demand for advanced fault diagnosis technologies ...Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically.Hence,there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle break-downs.Due to vehicles’increasingly complex and autonomous nature,there is a growing urgency to investigate novel diagnosis methodologies for improving safety,reliability,and maintainability.While Artificial Intelligence(AI)has provided a great opportunity in this area,a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis(VFD)systems is unavailable.Therefore,this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple techniques.We focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines,lifting systems(suspensions and tires),gearboxes,and brakes,among other vehicular subsystems.We then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous cars.The review elucidates the transformation of VFD systems that consequently increase accuracy,economization,and prediction in most vehicular sub-systems due to AI applications.Indeed,the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations:The integration shows that a single technique or method fails its expectations,which can lead to more reliable and versatile diagnostic support.By synthesizing current information and distinguishing forthcoming patterns,this work aims to accelerate advancement in smart automotive innovations,conforming with the requests of Industry 4.0 and adding to the progression of more secure,more dependable vehicles.The findings underscored the necessity for cross-disciplinary cooperation and examined the total potential of AI in vehicle default analysis.展开更多
The present study reports investigations on rheological,mechanical,thermal,tribological and morphological properties of feedstock filaments prepared with polylactic acid-polyether ketone ketone-hydroxyapatite-chitosan...The present study reports investigations on rheological,mechanical,thermal,tribological and morphological properties of feedstock filaments prepared with polylactic acid-polyether ketone ketone-hydroxyapatite-chitosan(PLA-PEKK-HAp-CS)composite for 3D printing of functional prototypes.The study consists of a series of melt processing operations on melt flow index(MFI)setup as per ASTM D-1238 for melt flow certainty followed by fixation of reinforcement composition/proportion as 94%PEKK-4%HAp-2%CS(B)by mass in PLA matrix(A).The blending of reinforcement and preparation of feedstock filament for fused deposition modeling(FDM)set up has been performed on commercial twin screw extruder(TSE).The results of study suggest that feedstock filaments prepared with blend of 95%A-5%B(by mass)at 200℃processing temperature and 100 r/min rotational speed on TSE resulted into better tensile properties(35.9 MPa peak strength and 32.3 MPa break strength)with 6.24%surface porosity,42.67 nm surface roughness(R_(a))and acceptable heat capacity(2.14 J/g).However as regards to tribological behavior,the minimum wear of 316μm was observed for sample with poor tensile properties.As regards to crash application for scaffolds the maximum toughness of 1.16 MPa was observed for 85%A-15%B(by mass)at 200℃processing temperature and 150 r/min rotational speed on TSE.展开更多
The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and em...The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.展开更多
基金funding provided through University Distinguished Research Grants(Project No.RDU223016)as well as financial assistance provided through the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UMP/02/35).
文摘Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically.Hence,there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle break-downs.Due to vehicles’increasingly complex and autonomous nature,there is a growing urgency to investigate novel diagnosis methodologies for improving safety,reliability,and maintainability.While Artificial Intelligence(AI)has provided a great opportunity in this area,a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis(VFD)systems is unavailable.Therefore,this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple techniques.We focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines,lifting systems(suspensions and tires),gearboxes,and brakes,among other vehicular subsystems.We then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous cars.The review elucidates the transformation of VFD systems that consequently increase accuracy,economization,and prediction in most vehicular sub-systems due to AI applications.Indeed,the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations:The integration shows that a single technique or method fails its expectations,which can lead to more reliable and versatile diagnostic support.By synthesizing current information and distinguishing forthcoming patterns,this work aims to accelerate advancement in smart automotive innovations,conforming with the requests of Industry 4.0 and adding to the progression of more secure,more dependable vehicles.The findings underscored the necessity for cross-disciplinary cooperation and examined the total potential of AI in vehicle default analysis.
基金Guru Nanak at Dev Engg. College, Ludhiana (GNDEC) and SERB (File No. IMRC/AISTDF/R&D/P-10/2017) for providing financial/technical assistance to carry out the research
文摘The present study reports investigations on rheological,mechanical,thermal,tribological and morphological properties of feedstock filaments prepared with polylactic acid-polyether ketone ketone-hydroxyapatite-chitosan(PLA-PEKK-HAp-CS)composite for 3D printing of functional prototypes.The study consists of a series of melt processing operations on melt flow index(MFI)setup as per ASTM D-1238 for melt flow certainty followed by fixation of reinforcement composition/proportion as 94%PEKK-4%HAp-2%CS(B)by mass in PLA matrix(A).The blending of reinforcement and preparation of feedstock filament for fused deposition modeling(FDM)set up has been performed on commercial twin screw extruder(TSE).The results of study suggest that feedstock filaments prepared with blend of 95%A-5%B(by mass)at 200℃processing temperature and 100 r/min rotational speed on TSE resulted into better tensile properties(35.9 MPa peak strength and 32.3 MPa break strength)with 6.24%surface porosity,42.67 nm surface roughness(R_(a))and acceptable heat capacity(2.14 J/g).However as regards to tribological behavior,the minimum wear of 316μm was observed for sample with poor tensile properties.As regards to crash application for scaffolds the maximum toughness of 1.16 MPa was observed for 85%A-15%B(by mass)at 200℃processing temperature and 150 r/min rotational speed on TSE.
基金The authors are grateful to the Universiti Malaysia Pahang Al-Sultan Abdullah and the Malaysian Ministry of Higher Education for their generous support and funding provided through University Distinguished Research Grants(Project No.RDU223016)as well as financial assistance provided through the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UMP/02/35).
文摘The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.
