Hostname: page-component-6766d58669-nqrmd Total loading time: 0 Render date: 2026-05-21T23:04:47.642Z Has data issue: false hasContentIssue false
  • English
  • Français

Artificial intelligence methods for improving the inventive design process, application in lattice structure case study

Published online by Cambridge University Press:  18 July 2022

Masih Hanifi Open the ORCID record for Masih Hanifi [Opens in a new window] ,
Hicham Chibane Open the ORCID record for Hicham Chibane [Opens in a new window] ,
Remy Houssin ,
Denis Cavallucci  and
Naser Ghannad
Masih Hanifi*
Affiliation:
Strasbourg University, 4 Rue Blaise Pascal, 67081 Strasbourg, France INSA of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France
Hicham Chibane
Affiliation:
INSA of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France
Remy Houssin
Affiliation:
Strasbourg University, 4 Rue Blaise Pascal, 67081 Strasbourg, France
Denis Cavallucci
Affiliation:
INSA of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France
Naser Ghannad
Affiliation:
INSA of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France
*
Author for correspondence: Masih Hanifi, E-mail: masih.hanifi@insa-strasbourg.fr
Get access Rights & Permissions [Opens in a new window]

Abstract

Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.

Keywords

Artificial intelligencedocument embeddinginventive designlattice structuresimilarity computationtext classification

Information

Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e20
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Adwan, O, Faris, H, Jaradat, K, Harfoushi, O and Ghatasheh, N (2014) Predicting customer churn in telecom industry using multilayer preceptron neural networks. Life Science Journal 11, 75–81.Google Scholar
Altshuller, G, Alʹtov, G and Altov, H (1996) And Suddenly the Inventor Appeared: TRIZ, the Theory of Inventive Problem Solving. Worcester, MA: Technical Innovation Center, Inc.Google Scholar
Aman, H, Amasaki, S, Yokogawa, T and Kawahara, M (2018) A Doc2vec-based assessment of comments and its application to change-prone method analysis. In 2018 25th Asia-Pacific Software Engineering Conference (APSEC), Nara, Japan, pp. 643–647. doi:10.1109/APSEC.2018.00082.CrossRefGoogle Scholar
Bala, A, Sehgal, VK and Saini, B (2014) Effect of fly ash and waste rubber on properties of concrete composite. Concrete Research Letters 5, 842–857.Google Scholar
Berduygina, D and Cavallucci, D (2020) Improvement of automatic extraction of inventive information with patent claims structure recognition. In Arai, K Kapoor, S and Bhatia, R (eds), Intelligent Computing, Vol. 1229. Cham: Springer International Publishing, pp. 625–637. doi:10.1007/978-3-030-52246-9_46.CrossRefGoogle Scholar
Cavallucci, D and Strasbourg, I (2009) From TRIZ to inventive design method (IDM): towards a formalization of inventive practices in R&D departments. Innovation 18, 2.Google Scholar
Cavallucci, D, Rousselot, F and Zanni, C (2009) Assisting R&D activities definition through problem mapping. CIRP Journal of Manufacturing Science and Technology 1, 131–136. doi:10.1016/j.cirpj.2008.09.014.CrossRefGoogle Scholar
Chai, K-H, Zhang, J and Tan, K-C (2005) A TRIZ-based method for new service design. Journal of Service Research 8, 48–66. doi:10.1177/1094670505276683.CrossRefGoogle Scholar
Chang, W, Xu, Z, Zhou, S and Cao, W (2018) Research on detection methods based on Doc2vec abnormal comments. Future Generation Computer Systems 86, 656–662. doi:10.1016/j.future.2018.04.059.CrossRefGoogle Scholar
Chen, L, Wang, P, Dong, H, Shi, F, Han, J, Guo, Y, Childs, PR, Xiao, J, Wu, C (2019) An artificial intelligence based data-driven approach for design ideation. Journal of Visual Communication and Image Representation 61, 10–22. doi:10.1016/j.jvcir.2019.02.009.CrossRefGoogle Scholar
Chibane, H, Dubois, S and De Guio, R (2021) Innovation beyond optimization: application to cutting tool design. Computers & Industrial Engineering 154, 107139. doi:10.1016/j.cie.2021.107139.CrossRefGoogle Scholar
Cohen, MA, Eliasberg, J and Ho, T-H (1996) New product development: the performance and time-to-market tradeoff. Management Science 42, 173–186. doi:10.1287/mnsc.42.2.173.CrossRefGoogle Scholar
Dalal, MK and Zaveri, MA (2011) Automatic text classification: a technical review. International Journal of Computer Applications 28, 37–40. doi:10.5120/3358-4633.CrossRefGoogle Scholar
Devi, G, Al Balushi, DALR, Ahmed, SJ and Almawali, NS (2016) Synthesis and application of nano and micro-silica particles to enhance the mechanical properties of cement concrete. Concrete Research Letters 7, 113–122.Google Scholar
Ding, R, Yao, J, Du, B, Zhao, L and Guo, Y (2020) Mechanical properties and energy absorption capability of ARCH lattice structures manufactured by selective laser melting. Advanced Engineering Materials 22, 1901534. doi:10.1002/adem.201901534.CrossRefGoogle Scholar
Ding, R, Yao, J, Du, B, Li, K, Pan, M, Zhao, L and Guo, Y (2021) Flexural properties of ARCH lattice structures manufactured by selective laser melting. Advanced Engineering Materials 5, 2001440. doi:10.1002/adem.202001440.CrossRefGoogle Scholar
“Duocel® Foam Energy Absorbers”. Available at http://ergaerospace.com/applications/duocel-foam-energy-absorbers/.Google Scholar
Edouard, R, Chibane, H and Cavallucci, D (2021) New characterizing method of a 3D parametric lattice structure. FME Transactions 49, 894–895. doi:10.5937/fme2104894E.CrossRefGoogle Scholar
Fazilati, J and Alisadeghi, M (2016) Multiobjective crashworthiness optimization of multi-layer honeycomb energy absorber panels under axial impact. Thin-Walled Structures 107, 197–206. doi:10.1016/j.tws.2016.06.008.CrossRefGoogle Scholar
Feng, J, Xu, H, Mannor, S and Yan, S (2014) Robust logistic regression and classification. Advances in Neural Information Processing Systems 27, 253–261.Google Scholar
Han, J, Shi, F, Chen, L and Childs, PRN (2018a) The combinator – a computer-based tool for creative idea generation based on a simulation approach. Design Science 4, e11. doi:10.1017/dsj.2018.7.CrossRefGoogle Scholar
Han, J, Shi, F, Chen, L and Childs, PRN (2018b) A computational tool for creative idea generation based on analogical reasoning and ontology. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 462–477.CrossRefGoogle Scholar
Hanifi, M, Chibane, H, Houssin, R and Cavallucci, D (2019) Improving inventive design methodology's agility. In New Opportunities for Innovation Breakthroughs for Developing Countries and Emerging Economies, Vol. 572. Cham: Springer International Publishing, pp. 216–227. doi:10.1007/978-3-030-32497-1_18.CrossRefGoogle Scholar
Hanifi, M, Chibane, H, Houssin, R and Cavallucci, D (2020 a) A method to formulate problem in initial analysis of inventive design. In Nyffenegger, F, Ríos, J, Rivest, L and Bouras, A (eds), Product Lifecycle Management Enabling Smart X, Vol. 594. Cham: Springer International Publishing, pp. 311–323. doi:10.1007/978-3-030-62807-9_25.CrossRefGoogle Scholar
Hanifi, M, Chibane, H, Houssin, R and Cavallucci, D (2020 b) Contribution to TRIZ in combining lean and inventive design method. In Cavallucci, D, Brad, S and Livotov, P (eds), Systematic Complex Problem Solving in the Age of Digitalization and Open Innovation, Vol. 597. Cham: Springer International Publishing, pp. 280–291. doi:10.1007/978-3-030-61295-5_23.CrossRefGoogle Scholar
Hanifi, M, Chibane, H, Houssin, R and Cavallucci, D (2021) IPG as a new method to improve the agility of the initial analysis of the inventive design. FME Transactions 49, 549–562. doi:10.5937/fme2103549H.CrossRefGoogle Scholar
Hanifi, M, Chibane, H, Houssin, R and Cavallucci, D (2022) Problem formulation in inventive design using Doc2vec and cosine similarity as artificial intelligence methods and scientific papers. Engineering Applications of Artificial Intelligence 109, 104661. doi:10.1016/j.engappai.2022.104661.CrossRefGoogle Scholar
Hu, S, Tang, H and Han, S (2021) Energy absorption characteristics of PVC coarse aggregate concrete under impact load. International Journal of Concrete Structures and Materials 15, 1–16. doi:10.1186/s40069-021-00465-w.CrossRefGoogle Scholar
Huang, A (2008) Similarity measures for text document clustering. Proc. Sixth N. Z. Comput. Sci. Res. Stud. Conf. NZCSRSC2008, Vol. 4. Christch. N. Z., pp. 9–56.Google Scholar
Imandoust, SB and Bolandraftar, M (2013) Application of K-nearest neighbor (KNN) approach for predicting economic events: theoretical background. International Journal of Engineering Research and Applications 3, 605–610.Google Scholar
Khan, A, Baharudin, B, Lee, LH and Khan, K (2010) A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology 1, 4–20. doi:10.4304/jait.1.1.1-1.Google Scholar
Kim, HK, Kim, H and Cho, S (2017) Bag-of-concepts: comprehending document representation through clustering words in distributed representation. Neurocomputing 266, 336–352. doi:10.1016/j.neucom.2017.05.046.CrossRefGoogle Scholar
Kowsari, K, Jafari Meimandi, K, Heidarysafa, M, Mendu, S, Barnes, L and Brown, D (2019) Text classification algorithms: a survey. Information 10, 150. doi:10.3390/info10040150.CrossRefGoogle Scholar
Kumar, G and Bhatia, PK (2012) Impact of agile methodology on software development process. International Journal of Computer and Electronics Engineering 2, 46–50.Google Scholar
Le, Q and Mikolov, T (2014) Distributed representations of sentences and documents. International Conference on Machine Learning 32, 1188–1196.Google Scholar
Li, M, Ming, X, He, L, Zheng, M and Xu, Z (2015) A TRIZ-based trimming method for patent design around. Computer-Aided Design 62, 20–30. doi:10.1016/j.cad.2014.10.005.CrossRefGoogle Scholar
Li, T, Chen, Y, Hu, X, Li, Y and Wang, L (2018) Exploiting negative Poisson's ratio to design 3D-printed composites with enhanced mechanical properties. Materials & Design 142, 247–258. doi:10.1016/j.matdes.2018.01.034.CrossRefGoogle Scholar
Li, D, Liao, W, Dai, N and Xie, YM (2019) Comparison of mechanical properties and energy absorption of sheet-based and strut-based gyroid cellular structures with graded densities. Materials 12, 2183. doi:10.3390/ma12132183.CrossRefGoogle ScholarPubMed
Lin, Y, Zhu, X, Zheng, Z, Dou, Z and Zhou, R (2019) The individual identification method of wireless device based on dimensionality reduction and machine learning. The Journal of Supercomputing 75, 3010–3027. doi:10.1007/s11227-017-2216-2.CrossRefGoogle Scholar
Mikolov, T, Sutskever, I, Chen, K, Corrado, GS and Dean, J (2013) Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 26, 3111–3119.Google Scholar
Mimura, M (2019) An attempt to read network traffic with Doc2vec. Journal of Information Processing 27, 711–719. doi:10.2197/ipsjjip.27.711.CrossRefGoogle Scholar
Mimura, M and Tanaka, H (2018) Leaving all proxy server logs to paragraph vector. Journal of Information Processing 26, 804–812. doi:10.2197/ipsjjip.26.804.CrossRefGoogle Scholar
Mulak, P and Talhar, N (2015) Analysis of distance measures using K-nearest neighbor algorithm on KDD dataset. International Journal of Science and Research 4, 2101–2104.Google Scholar
Muley, P, Varpe, S and Ralwani, R (2015) Chopped carbon fibers innovative material for enhancement of concrete performances. International Journal of Applied Science and Engineering 1, 164–169.Google Scholar
Nasar, Z, Jaffry, SW and Malik, MK (2018) Information extraction from scientific articles: a survey. Scientometrics 117, 1931–1990. doi:10.1007/s11192-018-2921-5.CrossRefGoogle Scholar
Nédey, O, Souili, A and Cavallucci, D (2018) Automatic extraction of IDM-related information in scientific articles. International TRIZ Future Conference 541, 213–224. doi:10.1007/978-3-030-02456-7.Google Scholar
Panchal, G, Ganatra, A, Kosta, YP and Panchal, D (2011) Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering 3, 332–337. doi:10.7763/IJCTE.2011.V3.328.CrossRefGoogle Scholar
Pandey, N, Sanyal, DK, Hudait, A and Sen, A (2017) Automated classification of software issue reports using machine learning techniques: an empirical study. Innovations in Systems and Software Engineering 13, 279–297. doi:10.1007/s11334-017-0294-1.CrossRefGoogle Scholar
Park, H-A (2013) An Introduction to logistic regression: from basic concepts to interpretation with particular attention to nursing domain. Journal of Korean Academy of Nursing 43, 154–164. doi:10.4040/jkan.2013.43.2.154.CrossRefGoogle ScholarPubMed
Park, EL, Cho, S and Kang, P (2019) Supervised paragraph vector: distributed representations of words, documents and class labels. IEEE Access 7, 29051–29064. doi:10.1109/ACCESS.2019.2901933.CrossRefGoogle Scholar
Pawar, PY and Gawande, SH (2012) A comparative study on different types of approaches to text categorization. International Journal of Machine Learning and Computing 2, 423–426. doi:10.7763/IJMLC.2012.V2.158.CrossRefGoogle Scholar
Ramchoun, H, Amine, M, Idrissi, J, Ghanou, Y and Ettaouil, M (2016) Multilayer perceptron: architecture optimization and training. The International Journal of Interactive Multimedia and Artificial Intelligence 4, 26–30. doi:10.9781/ijimai.2016.415.CrossRefGoogle Scholar
Rane, A and Kumar, A (2018) Sentiment classification system of Twitter data for US Airline Service Analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Vol. 1. Tokyo, Japan, pp. 769–773. doi:10.1109/COMPSAC.2018.00114.CrossRefGoogle Scholar
Sarica, S, Luo, J and Wood, KL (2020) Technet: technology semantic network based on patent data. Expert Systems with Applications 142, 112995. doi:10.1016/j.eswa.2019.112995.CrossRefGoogle Scholar
Sarkar, D (2019) Text Analytics with Python: a Practitioner's Guide to Natural Language Processing. Berkeley, CA: Apress. doi:10.1007/978-1-4842-4354-1.CrossRefGoogle Scholar
Sekiguchi, Y (2017) Effects of mixed micro and nano silica particles on the dynamic compressive performances of epoxy adhesive. Applied Adhesion Science 5, 1–12. doi:10.1186/s40563-017-0083-y.Google Scholar
Shi, F, Chen, L, Han, J and Childs, P (2017) A data-driven text mining and semantic network analysis for design information retrieval. Journal of Mechanical Design 139, 111402. doi:10.1115/1.4037649.CrossRefGoogle Scholar
Siddharth, L and Chakrabarti, A (2018) Evaluating the impact of idea-inspire 4.0 on analogical transfer of concepts. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 431–448. doi:10.1017/S0890060418000136.CrossRefGoogle Scholar
Silva, LOLA, Koga, ML, Cugnasca, CE and Costa, AHR (2013) Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedlings. Computers and Electronics in Agriculture 97, 47–55. doi:10.1016/j.compag.2013.07.001.CrossRefGoogle Scholar
Singh, A, Halgamuge, MN and Lakshmiganthan, R (2017) Impact of different data types on classifier performance of random forest, naïve Bayes, and K-nearest neighbors algorithms. International Journal of Advanced Computer Science and Applications 8, doi:10.14569/IJACSA.2017.081201.CrossRefGoogle Scholar
Song, B, Luo, J and Wood, K (2019) Data-driven platform design: patent data and function network analysis. Journal of Mechanical Design 141, 021101. doi:10.1115/1.4042083.CrossRefGoogle Scholar
Soofi, A and Awan, A (2017) Classification techniques in machine learning: applications and issues. Journal of Basic & Applied Sciences 13, 459–465. doi:10.6000/1927-5129.2017.13.76.CrossRefGoogle Scholar
Souili, A and Cavallucci, D (2017) Automated extraction of knowledge useful to populate inventive design ontology from patents. TRIZ – The Theory of Inventive Problem Solving. Cham: Springer International Publishing, pp. 43–62. doi:10.1007/978-3-319-56593-4_2.CrossRefGoogle Scholar
Souili, A, Cavallucci, D, Rousselot, F and Zanni, C (2015) Starting from patents to find inputs to the problem graph model of IDM-TRIZ. Procedia Engineering 131, 150–161. doi:10.1016/j.proeng.2015.12.365.CrossRefGoogle Scholar
Valverde, UY, Nadeau, J-P and Scaravetti, D (2017) A new method for extracting knowledge from patents to inspire designers during the problem-solving phase. Journal of Engineering Design 28, 369–407. doi:10.1080/09544828.2017.1316361.CrossRefGoogle Scholar
Vapnik, VN (1995) The Nature of Statistical Learning Theory. New York, NY: Springer New York. doi:10.1007/978-1-4757-2440-0.CrossRefGoogle Scholar
Zanni-Merk, C, Cavallucci, D and Rousselot, F (2011) Use of formal ontologies as a foundation for inventive design studies. Computers in Industry 62, 323–336. doi:10.1016/j.compind.2010.09.007.CrossRefGoogle Scholar
Zhang, H and Zhou, L (2019) Similarity judgment of civil aviation regulations based on Doc2Vec deep learning algorithm. In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China, pp. 1–8. doi:10.1109/CISP-BMEI48845.2019.8965709.CrossRefGoogle Scholar
Zhang, W, Yoshida, T and Tang, X (2008) Text classification based on multi-word with support vector machine. Knowledge-Based Systems 21, 879–886. doi:10.1016/j.knosys.2008.03.044.CrossRefGoogle Scholar
Zhao, R and Mao, K (2018) Fuzzy bag-of-words model for document representation. IEEE Transactions on Fuzzy Systems 26, 794–804. doi:10.1109/TFUZZ.2017.2690222.CrossRefGoogle Scholar