Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution

The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost

Inspection by exception: a new machine learning-based approach for multistage manufacturing

Manufacturing processes usually consist of multiple different stages, each of which is influenced by a multitude of factors. Therefore, variations in product quality at a certain stage are contributed to by the errors generated at the current, as well as preceding, stages. The high cost of each production stage in the manufacture of high-quality products has stimulated a drive towards decreasing the volume of non-added value processes such as inspection. This paper presents a new method for what the authors have referred to as ‘inspection by exception’ – the principle of actively detecting and then inspecting only the parts that cannot be categorized as healthy or unhealthy with a high degree of certainty. The key idea is that by inspecting only those parts that are in the corridor of uncertainty, the volume of inspections are considerably reduced. This possibility is explored using multistage manufacturing data and both unsupervised and supervised learning algorithms. A case study is presented whereby material conditions and time domain features for force, vibration and tempering temperature are used as input data. Fuzzy C-Means (FCM) clustering is implemented to achieve inspection by exception in an unsupervised manner based on the normalized Euclidean distances between the principal components and cluster centres. Also, deviation vectors for product health are obtained using a comparator system to train neural networks for supervised learning-based inspection by exception. It is shown that the volume of inspections can be reduced by as much as 82% and 93% using the unsupervised and supervised learning approaches, respectively

IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN γ-TiAl AEROSPACE ALLOY MACHINING

The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (γ-TiAl), capable of improved product and process performance. However, while γ-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency

Modeling and Simulation in Engineering

The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes

[EN] Digitization of large parts with tight geometric tolerances is a time-consuming process that requires a detailed scan of the outer surface and the acquisition and processing of massive data. In this work, we propose a methodology for fast digitization using a partial scan in which large regions remain unmeasured. Our approach capitalizes on a database of fully scanned parts from which we extract a low-dimensional description of the shape variability using Statistical Shape Analysis. This lowdimensional description allows an accurate representation of any sample in the database with few independent parameters. Therefore, we propose a reconstruction algorithm that takes as input an incomplete measurement (faster than a complete digitization), identifies the statistical shape parameters and outputs a full scan reconstruction. We showcase an application to the digitization of large aeronautical fuselage panels. A statistical shape model is constructed from a database of 793 shapes that were completely digitized, with a point cloud of about 16 million points for each shape. Tests carried out at the manufacturing facility showed an overall reduction in the digitization time by 80% (using a partial digitization of 3 million points per shape) while keeping a high accuracy (reconstruction precision of 0.1mm) on the reconstructed surface.The authors want to thank Stelia Aerospace for providing the data and their helpful support through the project, and also the financial support of the regional research consortium RFI Atlanstic2020 in Pays de la Loire, France.Navarro-Jiménez, J.; Aguado, JV.; Bazin, G.; Albero Gabarda, V.; Borzacchiello, D. (2023). Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes. Journal of Intelligent Manufacturing. 34(5):2345-2358. https://doi.org/10.1007/s10845-022-01918-z23452358345Ballester, A., Piérola, A., Parrilla, E., Izquierdo, M., Uriel, J., Nácher, B., Ortiz, V., Gonzalez, J. C., Page, A., & Alemany, S. (2017). Fast, portable and low-cost 3d foot digitizers: Validity and reliability of measurements. Proceedings of 3DBODY. TECH (pp. 11–12).Barrault, M., Yvon, M., Nguyen, N. C., & Patera, A. T. (2004). An ‘empirical interpolation’ method: Application to efficient reduced-basis discretization of partial differential equations. Comptes Rendus Mathematique, 339(9), 667–672.Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), 509–522.Berger, M., Andrea, T., Lee, S., Pierre, A., Joshua, L., Andrei, S., & Claudio, S. (2014). State of the art in surface reconstruction from point clouds. 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Hogg, & R. Boyle (Eds.), BMVC92 (pp. 9–18). Springer London.D’Apuzzo, N. (2006). Overview of 3d surface digitization technologies in Europe. In Three-dimensional image capture and applications VII (Vol. 6056, pp. 605605). International Society for Optics and Photonics.Davies, R. H. (2002). Learning shape: Optimal models for analysing natural variability. University of Manchester Manchester.Drouot, A., Zhao, R., Irving, L., Sanderson, D., & Ratchev, S. (2018). Measurement assisted assembly for high accuracy aerospace manufacturing. 16th IFAC symposium on information control problems in manufacturing INCOM 2018. IFAC-PapersOnLine (Vol. 51, No. 11, pp. 393–398).Duchon, J. (1977). Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In W. Schempp, & K. Zeller (Eds.), Constructive theory of functions of several variables (pp. 85–100). Springer.Feng, J.C.-X., & Wang, X. (2002). Digitizing uncertainty modeling for reverse engineering applications: Regression versus neural networks. Journal of Intelligent Manufacturing, 13(3), 189–199.Gao, J., Gindy, N., & Chen, X. (2006). An automated GD&T inspection system based on non-contact 3d digitization. International Journal of Production Research, 44(1), 117–134.Guo, G., Jiang, T. T., Wang, Y. Z., & Gao, W. (2013). 2-D shape completion with shape priors. Chinese Science Bulletin, 58(27), 3430–3436.Hao, R., Lu, B., Cheng, Y., Li, X., & Huang, B. (2021). A steel surface defect inspection approach towards smart industrial monitoring. Journal of Intelligent Manufacturing, 32(7), 1833–1843.Heimann, T., & Meinzer, H.-P. (2009). Statistical shape models for 3d medical image segmentation: A review. Medical Image Analysis, 13(4), 543–563.Jolliffe, I. T. (2002). Principal components in regression analysis. Principal Component Analysis, 167–198.Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.Khalfaoui, S., Seulin, R., Fougerolle, Y., & Fofi, D. (2013). An efficient method for fully automatic 3d digitization of unknown objects. Computers in Industry, 64(9), 1152–1160 (Special Issue: 3D Imaging in Industry).Kurfess, T. R., Saldana, C., Saleeby, K., & Dezfouli, M. P. (2020). A review of modern communication technologies for digital manufacturing processes in industry 4 0. Journal of Manufacturing Science and Engineering, 142(11), 110815.Lauzeral, N., Borzacchiello, D., Kugler, M., George, D., Rémond, Y., Hostettler, A., & Chinesta, F. (2019). Shape parametrization of bio-mechanical finite element models based on medical images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 7(5–6), 480–489.Lee J., et al. (2020). Industrial AI. Applications with Sustainable Performance.Marton, Z. C., Rusu, R. B., & Beetz, M. (2009). On fast surface reconstruction methods for large and noisy point clouds. In 2009 IEEE international conference on robotics and automation (pp. 3218–3223).Meinguet, J. (1979). Multivariate interpolation at arbitrary points made simple. Zeitschrift für angewandte Mathematik und Physik ZAMP, 30(2), 292–304.Mian, S. H., & Al-Ahmari, A. (2019). Comparative analysis of different digitization systems and selection of best alternative. Journal of Intelligent Manufacturing, 30(5), 2039–2067.Panczuk, R., Foissac, P.-Y. (2008). Method and device for machining panels, E.U. Patent EP1689558B1.Pauly, M., Mitra, N. J., Giesen, J., Gross, M. H., & Guibas, L. J. (2005). Example-based 3d scan completion. In Symposium on geometry processing, number CONF (pp. 23–32).Pimenov, D. Y., Andrés, B., & Tadeusz, M. (2018). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing, 29(5), 1045–1061.Poulhaon, F., Leygue, A., Rauch, M., Hascoet, J.-Y., & Chinesta, F. (2014). Simulation-based adaptative toolpath generation in milling processes. International Journal of Machining and Machinability of Materials, 15(3–4), 263–284.Qian, J., Feng, S., Mingzhu, X., Tao, T., Shang, Y., Chen, Q., & Zuo, C. (2021). High-resolution real-time 360$$^{\circ }$$ 3d surface defect inspection with fringe projection profilometry. Optics and Lasers in Engineering, 137, 106382.Stockman, G., & Shapiro, L. G. (2001). Computer vision. Prentice Hall PTR.Shen, C.-H., Hongbo, F., Chen, K., & Shi-Min, H. (2012). Structure recovery by part assembly. ACM Transactions on Graphics (TOG), 31(6), 1–11.Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2020). Segmentation-based deep-learning approach for surface-defect detection. 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The digitization of quality control operations with cloud platform computing technologies (pp. 305–334). Springer

Powder-based laser hybrid additive manufacturing of metals:a review

Recent advances in additive manufacturing (AM) have attracted significant industrial interest. Initially, AM was mainly associated with the fabrication of prototypes, but the AM advances together with the broadening range of available materials, especially for producing metallic parts, have broaden the application areas and now the technology can be used for manufacturing functional parts, too. Especially, the AM technologies enable the creation of complex and topologically optimised geometries with internal cavities that were impossible to produce with traditional manufacturing processes. However, the tight geometrical tolerances along with the strict surface integrity requirements in aerospace, biomedical and automotive industries are not achievable in most cases with standalone AM technologies. Therefore, AM parts need extensive post-processing to ensure that their surface and dimensional requirements together with their respective mechanical properties are met. In this context, it is not surprising that the integration of AM with post-processing technologies into single and multi set-up processing solutions, commonly referred to as hybrid AM, has emerged as a very attractive proposition for industry while attracting a significant R&D interest. This paper reviews the current research and technology advances associated with the hybrid AM solutions. The special focus is on hybrid AM solutions that combine the capabilities of laser-based AM for processing powders with the necessary post-process technologies for producing metal parts with required accuracy, surface integrity and material properties. Commercially available hybrid AM systems that integrate laser-based AM with post-processing technologies are also reviewed together with their key application areas. Finally, the main challenges and open issues in broadening the industrial use of hybrid AM solutions are discussed. 2021, The Author(s).The authors would acknowledge the support received from the ESIF/ERDF Smart Factory Hub (SmartFub) programme in West Midlands. The authors also acknowledge the support received from Yamazaki Mazak, DMG MORI, LASEA and Systems 3R for establishing the hybrid AM facilities at the University of Birmingham.Scopu

Application of Audible Signals in Tool Condition Monitoring using Machine Learning Techniques

Machining is always accompanied by many difficulties like tool wear, tool breakage, improper machining conditions, non-uniform workpiece properties and some other irregularities, which are some of major barriers to highly-automated operations. Effective tool condition monitoring (TCM) system provides a best solution to monitor those irregular machining processes and suggest operators to take appropriate actions. Even though a wide variety of monitoring techniques have been developed for the online detection of tool condition, it remains an unsolved problem to look for a reliable, simple and cheap solution. This research work mainly focuses on developing a real-time tool condition monitoring model to detect the tool condition, part quality in machining process by using machine learning techniques through sound monitoring. The present study shows the development of a process model capable of on-line process monitoring utilizing machine learning techniques to analyze the sound signals collected during machining and train the proposed system to predict the cutting phenomenon during machining. A decision-making system based on the machine learning technique involving Support Vector Machine approach is developed. The developed system is trained with pre-processed data and tested, and the system showed a significant prediction accuracy in different applications which proves to be an effective model in applying to machining process as an on-line process monitoring system. In addition, this system also proves to be effective, cheap, compact and sensory position invariant. The successful development of the proposed TCM system can provide a practical tool to reduce downtime for tool changes and minimize the amount of scrap in metal cutting industry

Vibration assisted machining: Modelling, simulation, optimization, control and applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 30/11/2010.Increasing demand for precision components made of hard and brittle materials such as glasses, steel alloys and advanced ceramics, is such that conventional grinding and polishing techniques can no longer meet the requirements of today's precision manufacturing engineering. Particularly, in order to undertake micro-milling of optical glasses or other hard-machining materials, vibration assisted machining techniques have been adopted. However, it is essential and much needed to undertake such processes based on a scientific approach, i.e. the process to be quantitatively controlled and optimized rather than carried out with a trial-and-error manner. In this research, theoretical modelling and instrumental implementation issues for vibration assisted micro-milling are presented and explored in depth. The modelling is focused on establishing the scientific relationship between the process variables such as vibration frequency, vibration amplitude, feedrate and spindle speed while taking into account machine dynamics effect and the outcomes such as surface roughness generated, tool wear and material removal rate in the process. The machine dynamics has been investigated including a static analysis, machine tool-loop stiffness, modal analysis, frequency response function, etc, carried out for both the machine structure and the piezo-actuator device. The instrumentation implementation mainly includes the design of the desktop vibration assisted machining system and its control system. The machining system consists of a piezo-driven XY stage, air bearing spindle, jig, workpiece holder, PI slideway, manual slideway and solid metal table to improve the system stability. The control system is developed using LabVIEW 7.1 programming. The control algorithms are developed based on theoretical models developed by the author. The process optimisation of vibration assisted micro-milling has been studied by using design and analysis of experiment (DOE) approach. Regression analysis, analysis of variance (ANOVA), Taguchi method and Response Surface Methodology (RSM) have been chosen to perform this study. The effects of cutting parameters are evaluated and the optimal cutting conditions are determined. The interaction of cutting parameters is established to illustrate the intrinsic relationship between cutting parameters and surface roughness, tool wear and material removal rate. The predicted results are confirmed by validation experimental cutting trials. This research project has led to the following contribution to knowledge: (1) Development of a prototype desktop vibration assisted micro-milling machine. (2) Development of theoretical models that can predict the surface finish, tool wear and material removal rate quantitatively. (3) Establishing in depth knowledge on the use of vibration assisted machining principles. (4) Optimisation of cutting process parameters and conditions through simulations and machining trials for through investigation of vibration assisted machining.Financial support was obtained from Brunel University