Skip to main content

A review of machine learning for the optimization of production processes

The International Journal of Advanced Manufacturing Technology volume 104, pages 1889–1902 (2019)Cite this article

Abstract

Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

References

  1. Adibi MA, Shahrabi J (2014) A clustering-based modified variable neighborhood search algorithm for a dynamic job shop scheduling problem. Int J Adv Manuf Technol 70(9):1955–1961

    Google Scholar 

  2. Adibi MA, Zandieh M, Amiri M (2010) Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Syst Appl 37(1):282–287

    Google Scholar 

  3. Ahmad R, Kamaruddin S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63(1):135–149

    Google Scholar 

  4. Apte C, Weiss S, Grout G Predicting defects in disk drive manufacturing: a case study in high-dimensional classification. in: CAIA. IEEE Computer Society Press, Los Alamitos, pp 212–218

  5. Arif F, Suryana N, Hussin B (2013) Cascade quality prediction method using multiple pca+id3 for multi-stage manufacturing system. IERI Procedia 4:201–207

    Google Scholar 

  6. Assarzadeh S, Ghoreishi M (2008) Neural-network-based modeling and optimization of the electro-discharge machining process. Int J Adv Manuf Technol 39(5-6):488–500

    Google Scholar 

  7. Batista G, Prati R, Monard M (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newslett 6(1):20–29

    Google Scholar 

  8. Bellini A, Filippetti F, Tassoni C, Capolino GA (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Ind Electron 55(12):4109–4126

    Google Scholar 

  9. Bouacha K, Terrab A (2016) Hard turning behavior improvement using nsga-ii and pso-nn hybrid model. Int J Adv Manuf Technol 86(9-12):3527–3546

    Google Scholar 

  10. Braha D (2001) Data mining for design and manufacturing: Methods and applications massive computing, vol 3. Springer, Boston

    Google Scholar 

  11. Calder J, Sapsford R (2006) Statistical techniques. In: Sapsford R, Jupp V (eds) Data collection and analysis. Sage Publications Ltd, London, pp 208–242

  12. Cao WD, Yan CP, Ding L, Ma Y (2016) A continuous optimization decision making of process parameters in high-speed gear hobbing using ibpnn/de algorithm. Int J Adv Manuf Technol 85(9-12):2657–2667

    Google Scholar 

  13. Cassady CR, Kutanoglu E (2005) Integrating preventive maintenance planning and production scheduling for a single machine. IEEE Trans Reliab 54(2):304–309

    Google Scholar 

  14. Ceglarek D, Prakash PK (2012) Enhanced piecewise least squares approach for diagnosis of ill-conditioned multistation assembly with compliant parts. Proc Inst Mech Eng Part B: J Eng Manuf 226(3):485–502

    Google Scholar 

  15. Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46 (5):445–464

    Google Scholar 

  16. Chen H, Boning D (2017) Online and incremental machine learning approaches for ic yield improvement. In: 2017 IEEE/ACM International conference on computer-aided design (ICCAD), Irvine, pp pp 786–793

  17. Chen SH, Perng DB (2011) Directional textures auto-inspection using principal component analysis. Int J Adv Manuf Technol 55(9):1099–1110

    Google Scholar 

  18. Chen WC, Fu GL, Tai PH, Deng WJ (2009) Process parameter optimization for mimo plastic injection molding via soft computing. Expert Syst Appl 36(2):1114–1122

    Google Scholar 

  19. Chen Z, Li X, Wang L, Zhang S, Cao Y, Jiang S, Rong Y (2018) Development of a hybrid particle swarm optimization algorithm for multi-pass roller grinding process optimization. Int J Adv Manuf Technol 99(1-4):97–112

    Google Scholar 

  20. Cheng H, Chen H (2014) Online parameter optimization in robotic force controlled assembly processes. In: 2014 IEEE International conference on robotics and automation (ICRA). Piscataway, pp 3465–3470

  21. Chien CF, Chuang SC (2014) A framework for root cause detection of sub-batch processing system for semiconductor manufacturing big data analytics. IEEE Trans Semicond Manuf 27(4):475–488

    Google Scholar 

  22. Chien CF, Hsu CY, Chen PN (2013) Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flex Serv Manuf J 25(3):367–388

    Google Scholar 

  23. Chien CF, Liu CW, Chuang SC (2017) Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement. Int J Prod Res 55(17):5095–5107

    Google Scholar 

  24. Chien CF, Wang WC, Cheng J (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33(1):192–198

    Google Scholar 

  25. Colosimo BM, Pagani L, Strano M (2015) Reduction of calibration effort in fem-based optimization via numerical and experimental data fusion. Struct Multidiscip Optim 51(2):463–478

    Google Scholar 

  26. Coppel R, Abellan-Nebot JV, Siller HR, Rodriguez CA, Guedea F (2016) Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches. Int J Adv Manuf Technol 84(9-12):2219–2238

    Google Scholar 

  27. Demetgul M, Tansel IN, Taskin S (2009) Fault diagnosis of pneumatic systems with artificial neural network algorithms. Expert Syst Appl 36(7):10,512–10,519

    Google Scholar 

  28. Denkena B, Dittrich MA, Uhlich F (2016) Self-optimizing cutting process using learning process models. Procedia Technol 26:221–226

    Google Scholar 

  29. Dhas JER, Kumanan S (2011) Optimization of parameters of submerged arc weld using non conventional techniques. Appl Soft Comput 11(8):5198–5204

    Google Scholar 

  30. Diao G, Zhao L, Yao Y (2015) A dynamic quality control approach by improving dominant factors based on improved principal component analysis. Int J Prod Res 53(14):4287–4303

    Google Scholar 

  31. Fernandes C, Pontes AJ, Viana JC, Gaspar-Cunha A (2018) Modeling and optimization of the injection-molding process: a review. Adv Polym Technol 37(2):429–449

    Google Scholar 

  32. Franciosa P, Palit A, Vitolo F, Ceglarek D (2017) Rapid response diagnosis of multi-stage assembly process with compliant non-ideal parts using self-evolving measurement system. Procedia CIRP 60:38–43

    Google Scholar 

  33. Gao RX, Yan R (2011) Wavelets. Springer, Boston

    MATH  Google Scholar 

  34. Genna S, Simoncini A, Tagliaferri V, Ucciardello N (2017) Optimization of the sandblasting process for a better electrodeposition of copper thin films on aluminum substrate by feedforward neural network. Procedia CIRP 62:435–439

    Google Scholar 

  35. Grzegorzewski P, Kochański A, Kacprzyk J (2019) Soft Modeling in Industrial Manufacturing. Springer, Berlin

    Google Scholar 

  36. Gupta AK, Guntuku SC, Desu RK, Balu A (2015) Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks. Int J Adv Manuf Technol 77(1-4):331–339

    Google Scholar 

  37. Harding JA, Shahbaz M, Kusiak A (2006) Data mining in manufacturing: a review. J Manuf Sci Eng 128(4):969–976

    Google Scholar 

  38. He QP, Qin SJ, Wang J (2005) A new fault diagnosis method using fault directions in fisher discriminant analysis. AIChE J 51(2):555–571

    Google Scholar 

  39. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A: Math Phys Eng Sci 454(1971):903–995

    MathSciNet  MATH  Google Scholar 

  40. Huang SH, Pan YC (2015) Automated visual inspection in the semiconductor industry: a survey. Comput Ind 66:1–10

    Google Scholar 

  41. Irani KB, Cheng J, Fayyad UM, Qian Z (1993) Applying machine learning to semiconductor manufacturing. IEEE Expert 8(1):41–47

    Google Scholar 

  42. Jäger M, Knoll C, Hamprecht FA (2008) Weakly supervised learning of a classifier for unusual event detection. IEEE Trans Image Process: Publ IEEE Signal Process Soc 17(9):1700–1708

    MathSciNet  Google Scholar 

  43. Jian C, Gao J, Ao Y (2017) Automatic surface defect detection for mobile phone screen glass based on machine vision. Appl Soft Comput 52:348–358

    Google Scholar 

  44. Kamsu-Foguem B, Rigal F, Mauget F (2013) Mining association rules for the quality improvement of the production process. Expert Syst Appl 40(4):1034–1045

    Google Scholar 

  45. Kang P, Lee H.j, Cho S, Kim D, Park J, Park CK, Doh S (2009) A virtual metrology system for semiconductor manufacturing. Expert Syst Appl 36(10):12,554–12,561

    Google Scholar 

  46. Kant G, Sangwan KS (2015) Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Procedia CIRP 31:453–458

    Google Scholar 

  47. Karimi MH, Asemani D (2014) Surface defect detection in tiling industries using digital image processing methods: analysis and evaluation. ISA Trans 53(3):834–844

    Google Scholar 

  48. Kashyap S, Datta D (2015) Process parameter optimization of plastic injection molding: a review. Int J Plast Technol 19(1):1–18

    Google Scholar 

  49. Khakifirooz M, Chien CF, Chen YJ (2018) Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0. Appl Soft Comput 68:990–999

    Google Scholar 

  50. Khan AA, Moyne JR, Tilbury DM (2008) Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares. J Process Control 18(10):961–974

    Google Scholar 

  51. Kitayama S, Natsume S (2014) Multi-objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization. Simul Modell Pract Theory 48:35–44

    Google Scholar 

  52. Kitayama S, Onuki R, Yamazaki K (2014) Warpage reduction with variable pressure profile in plastic injection molding via sequential approximate optimization. Int J Adv Manuf Technol 72(5):827–838

    Google Scholar 

  53. Köksal G, Batmaz İ, Testik MC (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38(10):13,448–13,467

    Google Scholar 

  54. Konrad B, Lieber D, Deuse J (2013) Striving for zero defect production: Intelligent manufacturing control through data mining in continuous rolling mill processes. In: Windt K (ed) Robust manufacturing control, lecture notes in production engineering. Springer, Berlin, pp 215–229

    Google Scholar 

  55. Krishnan SA, Samuel GL (2013) Multi-objective optimization of material removal rate and surface roughness in wire electrical discharge turning. Int J Adv Manuf Technol 67(9-12):2021–2032

    Google Scholar 

  56. Kumar N, Mastrangelo C, Montgomery D (2011) Hierarchical modeling using generalized linear models. Qual Reliab Eng Int 27(6):835–842

    Google Scholar 

  57. Lei Y, He Z, Zi Y (2008) A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst Appl 35(4):1593–1600

    Google Scholar 

  58. Liang Z, Liao S, Wen Y, Liu X (2017) Component parameter optimization of strengthen waterjet grinding slurry with the orthogonal-experiment-design-based anfis. Int J Adv Manuf Technol 90(1-4):831–855

    Google Scholar 

  59. Lieber D, Stolpe M, Konrad B, Deuse J, Morik K (2013) Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. Procedia CIRP 7:193–198

    Google Scholar 

  60. Liggins II M, Hall D, Llinas J (2017) Handbook of multisensor data fusion: theory and practice. CRC Press, Boca Raton

  61. Luo W, Rojas J, Guan T, Harada K, Nagata K (2014) Cantilever snap assemblies failure detection using svms and the rcbht. In: 2014 IEEE International conference on mechatronics and automation (ICMA), Piscataway, pp 384–389

  62. Majumder A (2015) Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters. Proc Inst Mech Eng Part B: J Eng Manuf 229 (9):1504–1516

    Google Scholar 

  63. Masci J, Meier U, Ciresan D, Schmidhuber J, Fricout G (2012) Steel defect classification with max-pooling convolutional neural networks. In: The 2012 international joint conference on neural networks (IJCNN). IEEE, Piscataway, pp 1–6

  64. Mayne DQ (2014) Model predictive control: Recent developments and future promise. Automatica 50(12):2967–2986

    MathSciNet  MATH  Google Scholar 

  65. Ming W, Hou J, Zhang Z, Huang H, Xu Z, Zhang G, Huang Y (2015) Integrated ann-lwpa for cutting parameter optimization in wedm. Int J Adv Manuf Technol 120(1):109

    Google Scholar 

  66. Mobley RK (2002) An introduction to predictive maintenance, 2nd edn. Butterworth-Heinemann, Amsterdam

    Google Scholar 

  67. Monostori L (1996) Machine learning approaches to manufacturing. CIRP Ann Manuf Technol 45(Nr.2):675–712

    Google Scholar 

  68. Montgomery DC (2013) Design and analysis of experiments, 8th edn. Wiley, Hoboken

    Google Scholar 

  69. Neugebauer R, Putz M, Hellfritzsch U (2007) Improved process design and quality for gear manufacturing with flat and round rolling. CIRP Ann-Manuf Technol 56(1):307–312

    Google Scholar 

  70. Niggemann O, Lohweg V (2015) On the diagnosis of cyber-physical production systems - state-of-the-art and research agenda. In: AAAI’15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, pp 4119–4126

  71. Norouzi A, Hamedi M, Adineh VR (2012) Strength modeling and optimizing ultrasonic welded parts of abs-pmma using artificial intelligence methods. Int J Adv Manuf Technol 61(1-4):135– 147

    Google Scholar 

  72. Oh S, Han J, Cho H (2001) Intelligent process control system for quality improvement by data mining in the process industry. In: Braha D (ed) Data mining for design and manufacturing, vol 3. Springer, Boston, pp 289–309

    Google Scholar 

  73. Park JK, Kwon BK, Park JH, Kang DJ (2016) Machine learning-based imaging system for surface defect inspection. Int J Precis Eng Manuf-Green Technol 3(3):303–310

    Google Scholar 

  74. Paul A, Strano M (2016) The influence of process variables on the gas forming and press hardening of steel tubes. J Mater Process Technol 228:160–169

    Google Scholar 

  75. Peng A, Xiao X, Yue R (2014) Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system. Int J Adv Manuf Technol 73(1-4):87–100

    Google Scholar 

  76. Perng DB, Chen SH (2011) Directional textures auto-inspection using discrete cosine transform. Int J Prod Res 49(23):7171– 7187

    Google Scholar 

  77. Pfrommer J, Zimmerling C, Liu J, Kärger L, Henning F, Beyerer J (2018) Optimisation of manufacturing process parameters using deep neural networks as surrogate models. Procedia CIRP 72:426–431

    Google Scholar 

  78. Queipo NV, Haftka RT, Shyy W, Goel T, Vaidyanathan R, Kevin Tucker P (2005) Surrogate-based analysis and optimization. Prog Aerosp Sci 41(1):1–28

    MATH  Google Scholar 

  79. Rao RV, Pawar PJ (2009) Modelling and optimization of process parameters of wire electrical discharge machining. Proc Inst Mech Eng Part B: J Eng Manuf 223(11):1431–1440

    Google Scholar 

  80. Ren R, Hung T, Tan KC (2018) A generic deep-learning-based approach for automated surface inspection. IEEE Trans Cybern 48(3):929–940

    Google Scholar 

  81. Rodger JA (2018) Advances in multisensor information fusion: a markov–kalman viscosity fuzzy statistical predictor for analysis of oxygen flow, diffusion, speed, temperature, and time metrics in cpap. Expert Syst 35 (4):e12,270

    Google Scholar 

  82. Rodriguez A, Bourne D, Mason M, Rossano GF, Wang J (2010) Failure detection in assembly: Force signature analysis. In: 2010 IEEE Conference on automation science and engineering (CASE). Piscataway, NJ

  83. Rong Y, Zhang G, Chang Y, Huang Y (2016) Integrated optimization model of laser brazing by extreme learning machine and genetic algorithm. Int J Adv Manuf Technol 87(9):2943–2950

    Google Scholar 

  84. Rong-Ji W, Xin-hua L, Qing-ding W, Lingling W (2009) Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm. Int J Adv Manuf Technol 42(11-12):1035–1042

    Google Scholar 

  85. Sagiroglu S, Sinanc D (2013) Big data: a review. In: 2013 International conference on collaboration technologies and systems (CTS). IEEE, pp 42–47

  86. Saravanan N, Ramachandran KI (2010) Incipient gear box fault diagnosis using discrete wavelet transform (dwt) for feature extraction and classification using artificial neural network (ann). Expert Syst Appl 37(6):4168–4181

    Google Scholar 

  87. Scattolini R (2009) Architectures for distributed and hierarchical model predictive control – a review. J Process Control 19(5):723–731

    Google Scholar 

  88. Scholz-Reiter B, Weimer D, Thamer H (2012) Automated surface inspection of cold-formed micro-parts. CIRP Ann 61(1):531–534

    Google Scholar 

  89. Senn M, Link N (2012) A universal model for hidden state observation in adaptive process controls. Int J Adv Intell Syst 4(3-4):245–255

    Google Scholar 

  90. Senn M, Link N, Gumbsch P (2013) Optimal process control through feature-based state tracking along process chains. In: Proceedings of the 2nd World Congress on Integrated Computational Materials Engineering (ICME), pp 69–74

    Google Scholar 

  91. Shahrabi J, Adibi MA, Mahootchi M (2017) A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Comput Ind Eng 110:75–82

    Google Scholar 

  92. Sharp M, Ak R, Hedberg T (2018) A survey of the advancing use and development of machine learning in smart manufacturing. J Manuf Syst 48:170–179

    Google Scholar 

  93. Shewhart WA (1925) The application of statistics as an aid in maintaining quality of a manufactured product. J Am Stat Assoc 20(152):546

    Google Scholar 

  94. Shi H, Gao Y, Wang X (2010) Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. Int J Adv Manuf Technol 48(9):955–962

    Google Scholar 

  95. Shi H, Xie S, Wang X (2013) A warpage optimization method for injection molding using artificial neural network with parametric sampling evaluation strategy. Int J Adv Manuf Technol 65(1):343–353

    Google Scholar 

  96. Shin HJ, Eom DH, Kim SS (2005) One-class support vector machines—an application in machine fault detection and classification. Comput Ind Eng 48(2):395–408

    Google Scholar 

  97. Silva JA, Abellán-Nebot JV, Siller HR, Guedea-Elizalde F (2014) Adaptive control optimisation system for minimising production cost in hard milling operations. Int J Comput Integr Manuf 27(4):348–360

    Google Scholar 

  98. Sivanaga Malleswara Rao S, Venkata Rao K, Hemachandra Reddy K, Parameswara Rao CVS (2017) Prediction and optimization of process parameters in wire cut electric discharge machining for high-speed steel (hss). Int J Comput Appl 39(3):140–147

    Google Scholar 

  99. Sorensen LC, Andersen RS, Schou C, Kraft D (2018) Automatic parameter learning for easy instruction of industrial collaborative robots. In: 2018 IEEE International conference on industrial technology (ICIT), Piscataway, pp 87–92

  100. Srinivasu DS, Babu NR (2008) An adaptive control strategy for the abrasive waterjet cutting process with the integration of vision-based monitoring and a neuro-genetic control strategy. Int J Adv Manuf Technol 38(5-6):514–523

    Google Scholar 

  101. Stefatos G, Ben hamza A (2010) Dynamic independent component analysis approach for fault detection and diagnosis. Expert Syst Appl 37(12):8606–8617

    Google Scholar 

  102. Sterling D, Sterling T, Zhang Y, Chen H (2015) Welding parameter optimization based on gaussian process regression bayesian optimization algorithm. In: 2015 IEEE International conference on automation science and engineering (CASE), Piscataway, pp 1490–1496

  103. Stoll A, Pierschel N, Wenzel K, Langer T (2019) Process control in a press hardening production line with numerous process variables and quality criteria. In: Machine learning for cyber physical systems. Springer, pp 77–86

  104. Sun A, Jin X, Chang Y (2017) Research on the process optimization model of micro-clearance electrolysis-assisted laser machining based on bp neural network and ant colony. Int J Adv Manuf Technol 88 (9-12):3485–3498

    Google Scholar 

  105. Tsai DM, Lai SC (2008) Defect detection in periodically patterned surfaces using independent component analysis. Pattern Recogn 41(9):2812–2832

    MATH  Google Scholar 

  106. Valavanis I, Kosmopoulos D (2010) Multiclass defect detection and classification in weld radiographic images using geometric and texture features. Expert Syst Appl 37(12):7606–7614

    Google Scholar 

  107. Vallejo AJ, Morales-Menendez R (2010) Cost-effective supervisory control system in peripheral milling using hsm. Annu Rev Control 34(1):155–162

    Google Scholar 

  108. Venkata Rao K, Murthy PBGSN (2018) Modeling and optimization of tool vibration and surface roughness in boring of steel using rsm, ann and svm. J Intell Manuf 29(7):1533–1543

    Google Scholar 

  109. Vijayaraghavan A, Dornfeld D (2010) Automated energy monitoring of machine tools. CIRP Ann 59 (1):21–24

    Google Scholar 

  110. Wang CH (2008) Recognition of semiconductor defect patterns using spatial filtering and spectral clustering. Expert Syst Appl 34(3):1914–1923

    Google Scholar 

  111. Wang GG, Shan S (2007) Review of metamodeling techniques in support of engineering design optimization. J Mech Des 129(4):370

    Google Scholar 

  112. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: Methods and applications. J Manuf Syst 48:144–156

    Google Scholar 

  113. Weimer D, Scholz-Reiter B, Shpitalni M (2016) Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann 65(1):417–420

    Google Scholar 

  114. Weiss SM, Baseman RJ, Tipu F, Collins CN, Davies WA, Singh R, Hopkins JW (2010) Rule-based data mining for yield improvement in semiconductor manufacturing. Appl Intell 33(3):318–329

    Google Scholar 

  115. Weiss SM, Dhurandhar A, Baseman RJ (2013) Improving quality control by early prediction of manufacturing outcomes. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1258–1266

  116. Weiss SM, Dhurandhar A, Baseman RJ, White BF, Logan R, Winslow JK, Poindexter D (2016) Continuous prediction of manufacturing performance throughout the production lifecycle. J Intell Manuf 27(4):751–763

    Google Scholar 

  117. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01(01):1–41

    Google Scholar 

  118. Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4(1):23–45

    Google Scholar 

  119. Xu G, Yang Z (2015) Multiobjective optimization of process parameters for plastic injection molding via soft computing and grey correlation analysis. Int J Adv Manuf Technol 78(1-4):525–536

    Google Scholar 

  120. Yin S, Ding SX, Xie X, Luo H (2014) A review on basic data-driven approaches for industrial process monitoring. IEEE Trans Ind Electron 61(11):6418–6428

    Google Scholar 

  121. Yun JP, Choi DC, Jeon YJ, Park C, Kim SW (2014) Defect inspection system for steel wire rods produced by hot rolling process. Int J Adv Manuf Technol 70(9-12):1625–1634

    Google Scholar 

  122. Yusup N, Zain AM, Hashim SZM (2012) Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011). Expert Syst Appl 39(10):9909–9927

    Google Scholar 

  123. Zain AM, Haron H, Sharif S (2008) An overview of ga technique for surface roughness optimization in milling process. 2008 Int Sympos Inf Technol 4:1–6

    Google Scholar 

  124. Zain AM, Haron H, Sharif S (2011) Optimization of process parameters in the abrasive waterjet machining using integrated sa–ga. Appl Soft Comput 11(8):5350–5359

    Google Scholar 

  125. Zain AM, Haron H, Sharif S (2012) Integrated ann–ga for estimating the minimum value for machining performance. Int J Prod Res 50(1):191–213

    MATH  Google Scholar 

  126. Zhang L, Jia Z, Wang F, Liu W (2010) A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-edm. Int J Adv Manuf Technol 51(5-8):575–586

    Google Scholar 

  127. Zhang W, Jia MP, Zhu L, Yan XA (2017) Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis. Chin J Mech Eng 30(4):782–795

    Google Scholar 

  128. Zhao T, Shi Y, Lin X, Duan J, Sun P, Zhang J (2014) Surface roughness prediction and parameters optimization in grinding and polishing process for ibr of aero-engine. Int J Adv Manuf Technol 74(5-8):653–663

    Google Scholar 

Download references

Acknowledgments

This work was supported by Fraunhofer Cluster of Excellence “Cognitive Internet Technologies.”

Funding

This work is part of the Fraunhofer Lighthouse Project ML4P (Machine Learning for Production).

Author information

Authors and Affiliations

  1. Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, St. Augustin, Germany

    Dorina Weichert, Stefan Rüping & Stefan Wrobel

  2. Fraunhofer IWU, Institute for Machine Tools and Forming Technology, Chemnitz/Dresden, Germany

    Patrick Link, Anke Stoll & Steffen Ihlenfeldt

Authors
  1. Dorina Weichert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anke Stoll
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Rüping
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steffen Ihlenfeldt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefan Wrobel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Dorina Weichert or Patrick Link.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally to this work.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Weichert, D., Link, P., Stoll, A. et al. A review of machine learning for the optimization of production processes. Int J Adv Manuf Technol 104, 1889–1902 (2019). https://doi.org/10.1007/s00170-019-03988-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-03988-5

Keywords

  • Machine learning
  • Optimization
  • Manufacturing
  • Production