Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system

This paper outlines a comparative study to compare between using the power of the spindle and the infrared images of the cutting tool to design a condition monitoring system. This paper compares the two technologies for the development of a tool condition monitoring for milling processes. Wavelet analysis is used to process the power signal. Image gradient and Wavelet analyses are used to process the infrared images. The results show that the image gradient and wavelet analysis are powerful image processing techniques in detecting tool wear. The power of the motor of the spindle has shown less sensitivity to tool conditions in this case when compared to infrared thermography

IoT Based Smart Manufacturing system-Case Studies

Manufacturing now a days growing and becoming more complex, automated and computerized. Smart manufacturing is an emerging form of production manufacturing asset of today and in the future with involvement of smart sensors, actuators, communication technology, smart consumer devices like smart phones and tablets and data-intensive modeling. This paper will highlight a review of IoT application in smart manufacturing. Case studies on advanced techniques used in manufacturing industries for different operation such as Monitoring and controlling of smart equipment, IoT based Smart factory connectivity for industries, Hazardous Gas Detection, Electromyogram (EMG) monitoring system, and Tool wears characterization, Defect predictive in a manufacturing system, Machinery Health monitoring are presented

The Application of ANN and ANFIS Prediction Models for Thermal Error Compensation on CNC Machine Tools

Thermal errors can have significant effects on Computer Numerical Control (CNC) machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This thesis first reviews different methods of designing thermal error models, before concentrating on employing Artificial Intelligence (AI) methods to design different thermal prediction models. In this research work the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the backbone for thermal error modelling. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this thesis, temperature measurement was supplemented by direct distortion measurement at accessible locations. The location of temperature measurement must also provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this thesis, a new intelligent system for reducing thermal errors of machine tools using data obtained from thermography data is introduced. Different groups of key temperature points on a machine can be identified from thermal images using a novel schema based on a Grey system theory and Fuzzy C-Means (FCM) clustering method. This novel method simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. An Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means clustering (ANFIS-FCM) is then employed to design the thermal prediction model. In order to optimise the approach, a parametric study is carried out by changing the number of inputs and number of Membership Functions (MFs) to the ANFIS-FCM model, and comparing the relative robustness of the designs. The proposed approach has been validated on three different machine tools under different operation conditions. Thus the proposed system has been shown to be robust to different internal heat sources, ambient changes and is easily extensible to other CNC machine tools. Finally, the proposed method is shown to compare favourably against alternative approaches such as an Artificial Neural Network (ANN) model and different Grey models

Cutting tool condition monitoring of the turning process using artificial intelligence

This thesis relates to the application of Artificial Intelligence to tool wear monitoring. The main objective is to develop an intelligent condition monitoring system able to detect when a cutting tool is worn out. To accomplish this objective it is proposed to use a combined Expert System and Neural Network able to process data coming from external sensors and combine this with information from the knowledge base and thereafter estimate the wear state of the tool. The novelty of this work is mainly associatedw ith the configurationo f the proposeds ystem.W ith the combination of sensor-baseidn formation and inferencer ules, the result is an on-line system that can learn from experience and can update the knowledge base pertaining to information associated with different cutting conditions. Two neural networks resolve the problem of interpreting the complex sensor inputs while the Expert System, keeping track of previous successe, stimatesw hich of the two neuraln etworks is more reliable. Also, mis-classificationsa re filtered out through the use of a rough but approximate estimator, the Taylor's tool life equation. In this study an on-line tool wear monitoring system for turning processesh as been developed which can reliably estimate the tool wear under common workshop conditions. The system's modular structurem akesi t easyt o updatea s requiredb y different machinesa nd/or processesT. he use of Taylor's tool life equation, although weak as a tool life estimator, proved to be crucial in achieving higher performance levels. The application of the Self Organizing Map to tool wear monitoring is, in itself, new and proved to be slightly more reliable then the Adaptive Resonance Theory neural network

Reducing the uncertainty of thermal model calibration using on-machine probing and data fusion

Various sources of error hinder the possibility of achieving tight accuracy requirements for high-value manufacturing processes. These are often classified as: pseudo-static geometric errors; non-rigid body errors; thermal errors; and dynamic errors. It is comparatively complicated to obtain an accurate error map for the thermal errors because they are influenced by various factors with different materials, time constants, asymmetric heating sources and machining process, environmental effects, etc. Their transient nature and complex interaction mean that they are relatively difficult to compensate using pre-calibration methods. For error correction, the magnitude and sign of the error must first be measured or estimated. Pre-calibrated thermal compensation has been shown to be an effective means of improving accuracy. However, the time required to acquire the calibration data is prohibitive, reducing the uptake of this technology in industrial applications. Furthermore, changing conditions of the machine or factory environment are not adequately accommodated by pre-calibrated compensation, leading to degradation in performance. The supplementary use of on-machine probing, which is often installed for process control, can help to achieve better results. During the probing operation, the probe is carried by the machine tool axes. Therefore, the measurement data that it takes inevitably includes both the probing errors and those originating from the inaccuracies of a machine tool as well as any deviation in the part or artefact being measured. Each of these error sources must be understood and evaluated to be able to establish a measurement with a stated uncertainty. This is a vital preliminary step to ensure that the calibration parameters of the thermal model are not contaminated by other effects. This thesis investigates the various sources of measurement uncertainties for probing on a CNC machine tool and quantify their effects in the particular case where the on-machine probing is used to calibrate the thermal error model. Thermal errors constitute the largest uncertainty source for on-machine probing. The maximum observed thermal displacement error was approximately 220 μm for both X and Z-axis heating test at 100 % speed. To reduce the influence of this uncertainty source, sensor data fusion model using artificial neural network and principal component analysis was developed. The output of this model showed better than 90 % correlation to the measured thermal displacement. This data fusion model was developed for the temperature and FBG sensors. To facilitate the integration of the sensor and to ease the communication with machine tool controller, a modular machine tool structural monitoring system using LabVIEW environment was developed. Finally, to improve the performance of the data fusion model in order to reduce the thermal uncertainty, a novel photo-microsensor based sensing head for displacement measurement is presented and analysed in detail. This prototype sensor has measurement range of 20 μm and resolution of 21 nm

Sustainability-Based Expert System for Additive Manufacturing and CNC Machining

The development of technologies which enable resource efficient production is of paramount importance for the continued advancement of the manufacturing industry. In order to ensure a sustainable and clean energy future, manufacturers should be able to contrast and validate existing manufacturing technologies on a sustainability basis. In the post COVID-19 era of enterprise management, the use of artificial intelligence to simulate human expert decision making abilities will open new doors to achieving heightened levels of productivity and efficiency. The introduction of innovative technologies such as CNC machining and 3D printing to production systems has redefined the manufacturing landscape in a way that has compelled users to investigate into their sustainability. For the purposes of this study, cost effectiveness, energy and auxiliary material usage efficiency have been considered to be key indicators of manufacturing process sustainability. The objective of this research study is to develop a set of expert systems which can aid metal manufacturing facilities in selecting Binder Jetting, Direct Metal Laser Sintering or CNC Machining based on viable product, process, system parameters and inherent sustainability aspects. The expert systems have been developed using the knowledge automation software, Exsys CorvidÒ. Comprehensive knowledge bases pertaining to the objectives of each expert system have been created using literature reviews and communications with manufacturing experts. An interactive environment which mimics the expertise of a human expert has been developed by the application of suitable logical rules and backward chaining. The programs have been verified by analyzing and comparing the sustainability impacts of Binder Jetting and CNC Machining during fabrication of a stainless steel 316L component. According to the results of the study, Binder Jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC Machining, for fabrication of components feasible for each technology

Development of hybrid micro machining approaches and test-bed

High precision miniature and micro products which possess 3D complex structures or free-form surfaces are now being widely used in industry. These micro products require to be fabricated by several machining processes and the integration of these various machining processes onto one machine becomes necessary since this will help reduce realignment errors and also increase the machining efficiency. This thesis describes the development and testing of several hybrid machining approaches for machines which are typically used to produce micro products such as micro fluidic moulds, solar concentrator moulds, micro grooves in brittle materials and micro structured milling cutters. These are: (a) micro milling and laser deburring; (b) micro grinding involving laser pre-heating; (c) micro milling and laser polishing. The hybrid micro milling/ laser deburring process was tested during the fabrication of a micro fluidic injection mould. Micro burrs on the channel of micro fluidic mould generated during micro milling were completely removed by developed laser deburring process. This approach can achieve a good surface finish on a micro fluidic mould. The hybrid laser assisted micro grinding process was investigated by fabricating a set of micro grooves on brittle materials, including Al2O3 and Si3N4. The workpiece was pre-heated by laser to increase its temperature above that of the brittle to ductile transition phase interface. It was found that lower cutting forces were apparent in the grinding process when used to machine brittle materials. It was also found that laser assisted grinding helped achieve a very good surface finish and reduced subsurface damage. The final hybrid machining approach tested involved micro milling and laser polishing to fabricate solar concentrator moulds. Such a mould requires a good surface finish in order to accurately guide light focusing on a target. The laser polishing process was successfully used to remove any unwanted cutting marks generated by a previous micro milling process. Abstract iii As a novel extension to this hybrid machine world, a focussed ion beam (FIB) fabrication approach was researched regarding the generation of microstructures on the rake faces of milling cutters with the aim of reducing cutter cutting forces and increasing tool life. The tool wear resistance performance of these microstructured tools was evaluated through three sets of slot milling trials on a NAK80 specimen with the results indicating that micro structured micro milling cutters of this kind can effectively improve the tool wear resistance performance. A microstructure in a direction perpendicular to the cutting edge was found to be the best structure for deferring tool wear and obtaining prolonged tool life. This approach can potentially be further integrated into a hybrid precision machine such that micro structure cutters can be fabricated in-situ using a laser machining process. The conceptual design of a 5-axis hybrid machine which incorporates micro milling, grinding and laser machining has been proposed as a test-bed for the above hybrid micro machining approach. Through finite element analysis, the best configuration was found to be a closed-loop vertical machine which has one rotary stage on the worktable and another on machining head. In this thesis, the effectiveness of these novel hybrid machining approaches have been fully demonstrated through machining several microproducts. Recommendations for future work are suggested to focus on further scientific understanding of hybrid machining processes, the development of a laser repairing approach and the integration of a controller for the proposed hybrid machine

Conference on Thermal Issues in Machine Tools: Proceedings

Inhomogeneous and changing temperature distributions in machine tools lead to sometimes considerable quality problems in the manufacturing process. In addition, the switching on and off of aggregates, for example, leads to further fluctuations in the temperature field of machine tools. More than 100 specialists discussed these and other topics from the field of thermal research at the 1st Conference on Termal Issues in Machine Tools in Dresden from 22 to 23 March.:Efficient modelling and computation of structure-variable thermal behavior of machine tools S. Schroeder, A. Galant, B. Kauschinger, M. Beitelschmidt Parameter identification software for various thermal model types B. Hensel, S. Schroeder, K. Kabitzsch Minimising thermal error issues on turning centre M. Mareš, O. Horejš, J. Hornych The methods for controlled thermal deformations in machine tools A. P. Kuznetsov, H.-J. Koriath, A.O. Dorozhko Efficient FE-modelling of the thermo-elastic behaviour of a machine tool slide in lightweight design C. Peukert, J. Müller, M. Merx, A. Galant, A. Fickert, B. Zhou, S. Städtler, S. Ihlenfeldt, M. Beitelschmidt Development of a dynamic model for simulation of a thermoelectric self-cooling system for linear direct drives in machine tools E. Uhlmann, L. Prasol, S.Thom, S. Salein, R. Wiese System modelling and control concepts of different cooling system structures for machine tools J. Popken, L. Shabi, J. Weber, J. Weber The electric drive as a thermo-energetic black box S. Winkler, R. Werner Thermal error compensation on linear direct drive based on latent heat storage I. Voigt, S. Winkler, R. Werner, A. Bucht, W.-G. Drossel Industrial relevance and causes of thermal issues in machine tools M. Putz, C. Richter, J. Regel, M. Bräunig Clustering by optimal subsets to describe environment interdependencies J. Glänzel, R. Unger, S. Ihlenfeldt Using meta models for enclosures in machine tools F. Pavliček, D. P. Pamies, J. Mayr, S. Züst, P. Blaser, P. Hernández-Becerro, K. Wegener Model order reduction of thermal models of machine tools with varying boundary conditions P. Hernández-Becerro, J. Mayr, P. Blaser, F. Pavliček, K. Wegener Effectiveness of modelling the thermal behaviour of the ball screw unit with moving heat sources taken into account J. Jedrzejewski, Z. Kowal, W. Kwasny, Z. Winiarski Analyzing and optimizing the fluidic tempering of machine tool frames A. Hellmich, J. Glänzel, A. Pierer Thermo-mechanical interactions in hot stamping L. Penter, N. Pierschel Experimental analysis of the heat flux into the grinding tool in creep feed grinding with CBN abrasives C. Wrobel, D. Trauth, P. Mattfeld, F. Klocke Development of multidimensional characteristic diagrams for the real-time correction of thermally caused TCP-displacements in precise machining M. Putz, C. Oppermann, M. Bräunig Measurement of near cutting edge temperatures in the single point diamond turning process E. Uhlmann, D. Oberschmidt, S. Frenzel, J. Polte Investigation of heat flows during the milling processes through infrared thermography and inverse modelling T. Helmig, T. Augspurger, Y. Frekers, B. Döbbeler, F. Klocke, R. Kneer Thermally induced displacements of machine tool structure, tool and workpiece due to cutting processes O. Horejš, M. Mareš, J. Hornych A new calibration approach for a grey-box model for thermal error compensation of a C-Axis C. Brecher, R. Spierling, M. Fey Investigation of passive torque of oil-air lubricated angular contact ball bearing and its modelling J. Kekula, M. Sulitka, P. Kolář, P. Kohút, J. Shim, C. H. Park, J. Hwang Cooling strategy for motorized spindle based on energy and power criterion to reduce thermal errors S. Grama, A. N. Badhe, A. Mathur Cooling potential of heat pipes and heat exchangers within a machine tool spindleo B. Denkena, B. Bergman, H. Klemme, D. Dahlmann Structure model based correction of machine tools X. Thiem, B. Kauschinger, S. Ihlenfeldt Optimal temperature probe location for the compensation of transient thermal errors G. Aguirre, J. Cilla, J. Otaegi, H. Urreta Adaptive learning control for thermal error compensation on 5-axis machine tools with sudden boundary condition changes P. Blaser, J. Mayr, F. Pavliček, P. Hernández-Becerro, K. Wegener Hybrid correction of thermal errors using temperature and deformation sensors C. Naumann, C. Brecher, C. Baum, F. Tzanetos, S. Ihlenfeldt, M. Putz Optimal sensor placement based on model order reduction P. Benner, R. Herzog, N. Lang, I. Riedel, J. Saak Workpiece temperature measurement and stabilization prior to dimensional measurement N. S. Mian, S. Fletcher, A. P. Longstaff Measurement of test pieces for thermal induced displacements on milling machines H. Höfer, H. Wiemer Model reduction for thermally induced deformation compensation of metrology frames J. v. d. Boom Local heat transfer measurement A. Kuntze, S. Odenbach, W. Uffrecht Thermal error compensation of 5-axis machine tools using a staggered modelling approach J. Mayr, T. Tiberini. P. Blaser, K. Wegener Design of a Photogrammetric Measurement System for Displacement and Deformation on Machine Tools M. Riedel, J. Deutsch, J. Müller. S. Ihlenfeldt Thermography on Machine Tools M. Riedel, J. Deutsch, J. Müller, S. Ihlenfeldt Test piece for thermal investigations of 5-axis machine tolls by on-machine measurement M. Wiesener. P. Blaser, S. Böhl, J. Mayr, K. Wegene