The product lifecycle of automotive parts encompasses the comprehensive oversight of a product from its initial design to its disposal. This framework is particularly important in the automotive industry, which faces complex design and manufacturing challenges. However, traditional product lifecycle approaches often fall short in addressing the need for efficient and accurate production, testing, and maintenance due to the complexity of component structures. In response, deep learning has emerged as a transformative technology, recognized for its advanced data processing and analytical capabilities. Yet, despite its growing importance, there is still a notable gap in synthesizing deep learning methodologies and their application in the design, manufacturing, and testing of automotive parts. Based on our research and a thorough review of the existing literature, this paper offers a systematic analysis of deep learning’s role in product lifecycle for automotive parts. The review highlights how deep learning is integrated at various stages of the product lifecycle—design, processing, manufacturing, and inspection—demonstrating its ability to enhance predictive modeling and improve inspection accuracy. This work provides valuable insights into advancing automotive manufacturing technologies, driving innovation, and fostering greater intelligence and efficiency across the sector.
TheisslerAPérez-VelázquezJKettelgerdesM, et al. Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry. Reliab Eng Syst Saf2021; 215: 21.
2.
StarkJ.Product lifecycle management (PLM). Product lifecycle management (volume 1) 21st Century paradigm for product realisation. Geneva, Switzerland: Springer, 2022. pp.1–32.
3.
WangLLiuZLiuA, et al. Artificial intelligence in product lifecycle management. Int J Adv Manuf Technol2021; 114: 771–796.
4.
ZhangYRenSLiuY, et al. A framework for big data driven product lifecycle management. J Clean Prod2017; 159: 229–240.
5.
SchmidhuberJ.Deep learning in neural networks: an overview. Neural Netw2015; 61: 85–117.
6.
JordanMIMitchellTM.Machine learning: trends, perspectives, and prospects. Science2015; 349: 255–260.
7.
SunTFengBHuoJ, et al. Artificial intelligence meets flexible sensors: emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano Micro Lett2023; 16: 14.
8.
WangLZhuZZhaoX.Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method. Reliab Eng Syst Saf2024; 245: 110012.
9.
AbdulrazzaqMMRamahaNTAHameedAA, et al. Consequential advancements of self-supervised learning (SSL) in deep learning contexts. Mathematics2024; 12: 758.
10.
ChintaVSKethi ReddiSYarramsettyN.Optimal feature selection on serial cascaded deep learning for predictive maintenance system in automotive industry with fused optimization algorithm. Adv Eng Inform2023; 57: 102105.
11.
SongJXieXMaL.A semantics-guided safety enhancement framework for AI-enabled cyber-physical systems. IEEE Trans Softw Eng2023; 49: 4058–4080.
12.
GutfleischOWillardMABrückE, et al. Magnetic materials and devices for the 21st century: stronger, lighter, and more energy efficient. Adv Mater2011; 23: 821–842.
13.
SchmidtJMarquesMRGBottiS, et al. Recent advances and applications of machine learning in solid-state materials science. NPJ Comp Mater2019; 5: 484–489. DOI: 10.1038/s41524-019-0221-0.
14.
AdekomayaO.Adaption of green composite in automotive part replacements: discussions on material modification and future patronage. Environ Sci Pollut Res2020; 27: 8807–8813.
15.
KhdoudiABarkaNMasrourT, et al. Online prediction of automotive tempered glass quality using machine learning. Int J Adv Manuf Technol2023; 125: 1577–1602.
16.
WangRYaoDZhangJ, et al. Sound-insulation prediction model and multi-parameter optimisation design of the composite floor of a high-speed train based on machine learning. Mech Syst Signal Process2023; 200: 110631.
17.
WeiJChuXSunX, et al. Machine learning in materials science. Infomat2019; 1: 338–358.
18.
BengioYCourvilleAVincentP.Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell2013; 35: 1798–1828.
19.
JolliffeITCadimaJ.Principal component analysis: a review and recent developments. Philos Trans R Soc Math Phys Eng Sci2016; 374: 20150202.
20.
LiCKimIY.Multi-material topology optimization for automotive design problems. Proc IMechE, Part D: J Automobile Engineering2018; 232: 1950–1969.
21.
FuZLiuWHuangC, et al. A review of performance prediction based on machine learning in Materials Science. Nanomater2022; 12: 2957–2957. DOI: 10.3390/nano12172957.
22.
TohidiSSharifiY.Load-carrying capacity of locally corroded steel plate girder ends using artificial neural network. Thin-Walled Struct2016; 100: 48–61.
23.
FangZRoyKChenB, et al. Deep learning-based procedure for structural design of cold-formed steel channel sections with edge-stiffened and un-stiffened holes under axial compression. Thin-Walled Struct2021; 166: 108076.
24.
FangZRoyKMaresJ, et al. Deep learning-based axial capacity prediction for cold-formed steel channel sections using deep belief network. Structures2021; 33: 2792–2802.
25.
FangXYGongJEZhangF, et al. Machine learning assisted materials design of high-speed railway wheel with better fatigue performance. Eng Fract Mech2023; 292: 109586.
26.
ThikePHZhaoZShiP, et al. Significance of artificial neural network analytical models in materials' performance prediction. Bull Mater Sci2020; 43: 211. DOI: 10.1007/s12034-020-02154-y.
27.
MaYGuoZYXiaBB.Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat Biotechnol2022; 40: 838–839.
28.
ZhangPXueJZhangP, et al. Social-aware pedestrian trajectory prediction via states refinement LSTM. IEEE Trans Pattern Anal Mach Intell2022; 44: 2742–2759.
29.
WangYHuangGSongS, et al. Regularizing deep networks with semantic data augmentation. IEEE Trans Pattern Anal Mach Intell2022; 44: 3733–3748.
30.
ZhuJGeZSongZ, et al. Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data. Annu Rev Control2018; 46: 107–133.
31.
ChoiYLiuCR.Effects of machining parameters on surface integrity of hard machined surfaces. Proc IMechE, Part B: J Engineering Manufacture2010; 224: 699–708.
32.
WangGLiWLRaoF, et al. Multi-parameter optimization of machining impeller surface based on the on-machine measuring technique. Chin J Aeronaut2019; 32: 2000–2008.
33.
ZhengHLiuWXiaoC.Structural relationship model for design defect and influencing factors in the concurrent design process. Int J Prod Res2018; 56: 4897–4924.
34.
TresserSDolevABucherI.Dynamic balancing of super-critical rotating structures using slow-speed data via parametric excitation. J Sound Vib2018; 415: 59–77.
35.
RedaKYanY.Vibration measurement of an unbalanced metallic shaft using electrostatic sensors. IEEE Trans Instrum Meas2019; 68: 1467–1476.
36.
KimDHKimTJYWangX, et al. Smart machining process using machine learning: a review and perspective on machining industry. Int J Precis Eng Manuf Technol2018; 5: 555–568.
37.
DuCHoCLKaminskiJ.Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process. Adv Manuf2021; 9: 206–215.
38.
ElangovanMSakthivelNRSaravanamuruganS, et al. Machine learning approach to the prediction of surface roughness using statistical features of vibration signal acquired in turning. Procedia Comput Sci2015; 50: 282–288.
39.
JurkovicZCukorGBrezocnikM, et al. A comparison of machine learning methods for cutting parameters prediction in high speed turning process. J Intell Manuf2018; 29: 1683–1693.
40.
RouniyarAKShandilyaP.Optimization of process parameters in magnetic field assisted powder mixed EDM of aluminium 6061 alloy. Proc IMechE, Part C: J Mechanical Engineering Science2021; 235: 2998–3014.
41.
TeixidorDGrzendaMBustilloA, et al. Modeling pulsed laser micromachining of micro geometries using machine-learning techniques. J Intell Manuf2015; 26: 801–814.
42.
ChenYDingYZhaoF, et al. Surface defect detection methods for industrial products: a review. Appl Sci2021; 11: 11.
43.
AbagiuMMCojocaruDMantaF, et al. Detecting machining defects inside engine piston chamber with computer vision and machine learning. Sensors2023; 23: 785–785. DOI: 10.3390/s23020785.
44.
YangJLiSWangZ, et al. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials2020; 13: 5755. DOI: 10.3390/ma13245755.
45.
ParlakİEEmelE. Deep learning-based detection of aluminum casting defects and their types. Eng Appl Artif Intell2023; 118: 105636.
46.
RenSHeKGirshickR, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell2017; 39: 1137–1149.
47.
WangRGuoQLuS, et al. Tire defect detection using fully convolutional network. IEEE Access2019; 7: 43502–43510.
JangDWKangJSLimJY.A feasible strain-history extraction method using machine learning for the durability evaluation of automotive parts. J Mech Sci Technol2021; 35: 5117–5125.
52.
CoulibalyAHoussinRMutelB.Maintainability and safety indicators at design stage for mechanical products. Comput Ind2008; 59: 438–449.
53.
ElnajjarESelimMYEHamdanMO.Experimental study of dual fuel engine performance using variable LPG composition and engine parameters. Energy Convers Manag2013; 76: 32–42.
54.
OuisseMCoganS.Robust design of spot welds in automotive structures: a decision-making methodology. Mech Syst Signal Process2010; 24: 1172–1190.
55.
SunGDengMZhengG, et al. Design for cost performance of crashworthy structures made of high strength steel. Thin-Walled Struct2019; 138: 458–472.
56.
ZengWKhalidMASHanX, et al. A study on extreme learning machine for gasoline engine torque prediction. IEEE Access2020; 8: 104762–104774.
57.
HangaiYOzawaSOkadaK, et al. Machine learning estimation of Plateau stress of aluminum foam using X-ray computed tomography images. Materials2023; 16: 1894–1894. DOI: 10.3390/ma16051894.
58.
AnKBackJLeeSK, et al. Active vibration control for reduction of interior noise caused by R-MDPS of electric power steering in electric vehicle. Appl Acoust2023; 211: 109544.
59.
BozcaMFietkauP.Empirical model based optimization of gearbox geometric design parameters to reduce rattle noise in an automotive transmission. Mech Mach Theory2010; 45: 1599–1612.
60.
MengFTaoGZhangT, et al. Optimal shifting control strategy in inertia phase of an automatic transmission for automotive applications. Mech Syst Signal Process2015; 60-61: 742–752.
61.
MrzygłódBHawrylukMGronostajskiZ, et al. Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks. Arch Civil Mech Eng2018; 18: 1079–1091.
62.
GuerreroNMAparicioJValero-CarrerasD.Combining data envelopment analysis and machine learning. Mathematics2022; 10: 909.
63.
SinghAPurawatSRaoA, et al. Modular performance prediction for scientific workflows using machine learning. Future Gener Comput Syst2021; 114: 1–14.
64.
XiaoQLiCTangY, et al. Energy efficiency modeling for configuration-dependent machining via machine learning: a comparative study. IEEE Trans Autom Sci Eng2021; 18: 717–730.
WangMLLiuXTWangYS, et al. Reliability analysis and evaluation of key parts for automobiles on the basis of dimensional changes during high-speed operation. J Test Eval2015; 43: 1464–1471.
67.
GargSSinghJSinghDV.Availability analysis of crank-case manufacturing in a two-wheeler automobile industry. Appl Math Model2010; 34: 1672–1683.
68.
HuCZhouZZhangJ, et al. A survey on life prediction of equipment. Chin J Aeronaut2015; 28: 25–33.
69.
LvYZhengPYuanJ, et al. A predictive maintenance strategy for multi-component systems based on components' remaining useful life prediction. Mathematics2023; 11: 3884.
70.
MartinodRMBistorinOCastañedaLF, et al. Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions. Comput Ind Eng2018; 124: 100–112.
71.
ZhangCQiFZhangN, et al. Maintenance policy optimization for multi-component systems considering dynamic importance of components. Reliab Eng Syst Saf2022; 226: 108705.
72.
HsuHYSrivastavaGWuHT, et al. Remaining useful life prediction based on state assessment using edge computing on deep learning. Comput Commun2020; 160: 91–100.
73.
O'DonnellJYoonHS. Determination of time-to-failure for automotive system components using machine learning. J Comput Inf Sci Eng2020; 20: 1–24. DOI: 10.1115/1.4046818.
74.
BarbosaJFCorreiaJAFOJúniorRCSF, et al. Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network. Int J Fatigue2020; 135: 105527.
75.
YanHQinYXiangS, et al. Long-term gear life prediction based on ordered neurons LSTM neural networks. Measurement2020; 165: 108205.
76.
TianZZuoMJ.Health condition prediction of gears using a recurrent neural network approach. IEEE Trans Reliab2010; 59: 700–705.
77.
AbdallaJAHawilehR.Modeling and simulation of low-cycle fatigue life of steel reinforcing bars using artificial neural network. J Frankl Inst Eng Appl Math2011; 348: 1393–1403.
78.
HanTPangJTanACC. Remaining useful life prediction of bearing based on stacked autoencoder and recurrent neural network. J Manuf Syst2021; 61: 576–591.
79.
YanMWangXWangB, et al. Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model. ISA Trans2020; 98: 471–482.
80.
ShaoYNezuKPotIoME.Prognosis of remaining bearing life using neural networks. Proc IMechE, Part I: J Systems and Engineering C2000; 214: 217–230.
81.
GebraeelNLawleyMLiuR, et al. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Trans Ind Electron2004; 51: 694–700.
82.
HuangRXiLLiX, et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mech Syst Signal Process2007; 21: 193–207.
83.
MahamadAKSaonSHiyamaT.Predicting remaining useful life of rotating machinery based artificial neural network. Comput Math Appl2010; 60: 1078–1087.
84.
JinRWuMWuK, et al. Position encoding based convolutional neural networks for machine remaining useful life prediction. IEEE-CAA J Automatica Sin2022; 9: 1427–1439.
85.
DongSLuoT.Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement2013; 46: 3143–3152.
86.
SunCZhangZHeZ. Research on bearing life prediction based on support vector machine and its application. J Phys Conf Ser2011; 305: 012028. DOI: 10.1088/1742–6596/305/1/012028
87.
OrdóñezCSánchez LasherasFRoca-PardiñasJ, et al. A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines. J Comput Appl Math2019; 346: 184–191.
88.
XieRKZhongXCQinSH, et al. Predicting multiaxial fatigue life of FGH96 superalloy based on machine learning models by considering failure process and loading paths. Int J Fatigue2023; 175: 13.
89.
WuDJenningsCTerpennyJ, et al. A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J Manuf Sci Eng Trans ASME2017; 139: 9.
90.
LiuQShiWChenZ.Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model. J Appl Polym Sci2020; 137: 6.
91.
RaufHKhalidMArshadN.A novel smart feature selection strategy of lithium-ion battery degradation modelling for electric vehicles based on modern machine learning algorithms. J Energy Storage2023; 68: 15.
92.
DeutschJHeD.Using Deep Learning-based approach to predict remaining useful life of rotating components. IEEE Trans Syst Man Cybern Syst2018; 48: 11–20.
93.
RenLZhaoLHongS, et al. Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access2018; 6: 50587–50598.
94.
ChengYHuKWuJ, et al. A deep learning-based two-stage prognostic approach for remaining useful life of rolling bearing. Appl Intell2022; 52: 5880–5895.
95.
AltinisikAHugulO.The seven-step failure diagnosis in automotive industry. Eng Fail Anal2020; 116: 104702.
ZhongJHWongPKYangZX.Fault diagnosis of rotating machinery based on multiple probabilistic classifiers. Mech Syst Signal Process2018; 108: 99–114.
98.
VrbaJCejnekMSteinbachJ, et al. A machine learning approach for gearbox system fault diagnosis. Entropy2021; 23: 1130–1130. DOI: 10.3390/e23091130.
99.
SepulvedaNESinhaJ.Parameter optimisation in the vibration-based machine learning model for accurate and reliable faults diagnosis in rotating machines. Machines2020; 8: 66.
100.
XingZQuJChaiY, et al. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine. J Mech Sci Technol2017; 31: 545–553.
101.
RavikumarKNMadhusudanaCKKumarH, et al. Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm. Eng Sci Technol2022; 30: 101048.
102.
LiCSanchezRVZuritaG, et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mech Syst Signal Process2016; 76-77: 283–293.
103.
HanTJiangDZhaoQ, et al. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Trans Inst Meas Contr2018; 40: 2681–2693.
104.
LiuRYangBZhangX, et al. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis. Mech Syst Signal Process2016; 75: 345–370.
105.
TianYFuMWuF.Steel plates fault diagnosis on the basis of support vector machines. Neurocomputing2015; 151: 296–303.
106.
YanKShenWMulumbaT, et al. ARX model based fault detection and diagnosis for chillers using support vector machines. Energy Build2014; 81: 287–295.
107.
YuanSLiM.Fault diagnosis using binary tree and sphere-structured support vector machines. J Mech Sci Technol2012; 26: 1431–1438.
108.
ZhengJPanHChengJ.Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech Syst Signal Process2017; 85: 746–759.
109.
ChenSYangRZhongM.Graph-based semi-supervised random forest for rotating machinery gearbox fault diagnosis. Control Eng Pract2021; 117: 9.
110.
HuQSiXSZhangQH, et al. A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests. Mech Syst Signal Process2020; 139: 22.
111.
SharmaASugumaranVBabu DevasenapatiS.Misfire detection in an IC engine using vibration signal and decision tree algorithms. Measurement2014; 50: 370–380.
112.
WangZZhangQXiongJ, et al. Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sens J2017; 17: 5581–5588.
HajnayebAGhasemlooniaAKhademSE, et al. Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis. Expert Syst Appl2011; 38: 10205–10209.
115.
KanePVAndhareAB.Application of psychoacoustics for gear fault diagnosis using artificial neural network. J Low Freq Noise Vib Active Control2016; 35: 207–220.
116.
LiangXYaoJZhangW, et al. A Novel Fault diagnosis of a rolling bearing method based on variational mode decomposition and an artificial neural network. Appl Sci2023; 13: 16.
117.
LiuHZhouJZhengY, et al. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans2018; 77: 167–178.
118.
ShatnawiYAl-khassawenehM.Fault diagnosis in internal combustion engines using extension neural network. IEEE Trans Ind Electron2014; 61: 1434–1443.
119.
WuJDHuangCKChangYW, et al. Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network. Expert Syst Appl2010; 37: 949–958.
120.
WuJDKuoJM.An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network. Expert Syst Appl2009; 36: 9776–9783.
121.
WuJDLiuCH.An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Syst Appl2009; 36: 4278–4286.
122.
XiongJLiCWangCD, et al. Application of convolutional neural network and data preprocessing by mutual dimensionless and similar Gram matrix in fault diagnosis. IEEE Trans Ind Inform2022; 18: 1061–1071.
123.
ZhangYHZhouTTHuangXF, et al. Fault diagnosis of rotating machinery based on recurrent neural networks. Measurement2021; 171: 8.
124.
ZhangZWangYWangK.Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. J Intell Manuf2013; 24: 1213–1227.
125.
ChenZLiCSanchezRV.Gearbox fault identification and classification with convolutional neural networks. Shock Vib2015; 2015: 1–10.
126.
JanssensOSlavkovikjVVervischB, et al. Convolutional neural network based fault detection for rotating machinery. J Sound Vib2016; 377: 331–345.
127.
JiangLZhengCLiY.Rotating machinery fault diagnosis based on transfer learning and an improved convolutional neural network. Meas Sci Technol2022; 33: 105012.
128.
JingLZhaoMLiP, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement2017; 111: 1–10.
129.
LiangHCaoJZhaoX.Multibranch and multiscale dynamic convolutional network for small sample fault diagnosis of rotating machinery. IEEE Sens J2023; 23: 8973–8988.
130.
LinMCHanPYFanYH, et al. Development of compound fault diagnosis system for gearbox based on convolutional neural network. Sensors2020; 20: 14.
131.
PangXXueXJiangW, et al. An investigation into fault diagnosis of planetary gearboxes using a bispectrum convolutional neural network. IEEE/ASME Trans Mechatron2021; 26: 2027–2037.
132.
WuCJiangPDingC, et al. Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Comput Ind2019; 108: 53–61.
133.
XiaMLiTXuL, et al. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans Mechatron2018; 23: 101–110.
134.
ChangHCWangYCShihYY, et al. Fault diagnosis of induction motors with imbalanced data using deep convolutional generative adversarial network. Appl Sci2022; 12: 17.
135.
GuoYZhangJSunB, et al. Adversarial deep transfer learning in fault diagnosis: progress, challenges, and future prospects. Sensors2023; 23: 39.
136.
WangZWangJWangY.An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition. Neurocomputing2018; 310: 213–222.
137.
ChongSLeeSKimB, et al. Applications of machine learning in metal-organic frameworks. Coord Chem Rev2020; 423: 213487.
138.
JuanYDaiYYangY, et al. Accelerating materials discovery using machine learning. J Mater Sci Technol2021; 79: 178–190.
139.
QianXPengSLiX, et al. Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon. Materials Today Physics2019; 10: 100140.
