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Journal articles on the topic 'Smart CNC milling'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Ceclan, Vasile Adrian, Ioan Alexandru Popan, Sorin Dumitru Grozav, Cristina Ștefana Miron-Borzan, and Ivan Kuric. "The Analyses of Working Parameters for a 3D Complex Part Manufacturing by CNC Machine." Applied Mechanics and Materials 808 (November 2015): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.808.286.

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In this paper I want to presents the process for manufacturing one complex parts made by aluminum alloy. For manufacturing this complex part I used CAD/CAM software, CNC milling machine and same special tools. Starting from the 3D model made in SolidWorks was manufactured this complex part, using new strategies for CNC milling. To be made this chain of pieces it is necessary to use smart software for this process.

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Myrsini, Ntemi, Paraschos Spyridon, Karakostas Anastasios, Gialampoukidis Ilias, Vrochidis Stefanos, and Kompatsiaris Ioannis. "Infrastructure monitoring and quality diagnosis in CNC machining: A review." CIRP Journal of Manufacturing Science and Technology Volume 38 (August 1, 2022): 631–49. https://doi.org/10.1016/j.cirpj.2022.06.001.

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Infrastructure monitoring and rapid quality diagnosis comprise the key solutions to achieve zero-defect smart manufacturing. The most fundamental systems in manufacturing industries are computer numerical controlled (CNC) tools. Automating and optimizing their functionality is a highly challenging task because complex dynamics and non-linear relationships govern the overall machining operations. Recent scientific advances in machining processes, incorporate intelligence in CNC tools to improve both the reliability and the productivity of the real-time cutting operations, while reducing waste and cost. This study extensively reviews these advances focusing on three fundamental aspects: Surface roughness prediction, tool wear prediction, and chatter detection in CNC cutting processes.

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Sudianto, Agus, Zamberi Jamaludin, Azrul Azwan Abdul Rahman, Sentot Novianto, and Fajar Muharrom. "Automatic Temperature Measurement and Monitoring System for Milling Process of AA6041 Aluminum Aloy using MLX90614 Infrared Thermometer Sensor with Arduino." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 82, no. 2 (2021): 1–14. http://dx.doi.org/10.37934/arfmts.82.2.114.

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Manufacturing process of metal part requires real-time temperature monitoring capability to ensure high surface integrity is upheld throughout the machining process. A smart temperature measurement and monitoring system for manufacturing process of metal parts is necessary to meet quality and productivity requirements. A smart temperature measurement can be applied in machining processes of conventional, non-conventional and computer numerical control (CNC) machines. Currently, an infrared fusion based thermometer Fluke Ti400 was employed for temperature measurement in a machining process. However, measured temperature in the form of data list with adjustable time range setting is not automatically linked to the computer for continuous monitoring and data analysis purposes. For this reason, a smart temperature measurement system was developed for a CNC milling operation on aluminum alloy (AA6041) using a MLX90614 infrared thermometer sensor operated by Arduino. The system enables data linkages with the computer because MLX90614 is compatible and linked to Microsoft Exel via the Arduino. This paper presents a work-study on the performance of this Arduino based temperature measurement system for dry milling process application. Here, the Arduino based temperature measurement system captured the workpiece temperature during machining of Aluminum Alloy (AA6041) and data were compared with the Fluke Ti400 infrared thermometer. Measurement results from both devices showed similar accuracy level with a deviation of ± 2 oC. Hence, a smart temperature measurement system was succeesfully developed expanding the scopes of current system setup.

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Park, Hong-seok, Bowen Qi, Duck-Viet Dang, and Dae Yu Park. "Development of smart machining system for optimizing feedrates to minimize machining time." Journal of Computational Design and Engineering 5, no. 3 (2017): 299–304. http://dx.doi.org/10.1016/j.jcde.2017.12.004.

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Abstract Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. Highlights The smart machining system was implemented in the CNC machine. Optimal feed rates enhance machine tool efficiency. The smart machining system is reliable to reduce machine time.

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LIANG, Yuming. "Curriculum reform and innovation for CNC lathe and milling processing certificate under the "1+X" certification system." Region - Educational Research and Reviews 5, no. 5 (2023): 206. http://dx.doi.org/10.32629/rerr.v5i5.1475.

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With the development of smart manufacturing, CNC lathe and milling processing technology play an increasingly vital role in modern manufacturing. The "1+X" certification system offers a new pathway for vocational education to cultivate highly skilled professionals. This paper focuses on the curriculum system of the CNC lathe and milling processing certificate and explores strategies for curriculum reform and innovation under the guidance of the "1+X" certification system. By examining the existing curriculum system and analyzing the discrepancies between industry demands and the current teaching situation, specific reform measures are proposed. Additionally, in line with the concept of industry-education integration, innovative approaches to curriculum content and teaching methods are explored, as well as ways to leverage information technology for improving teaching efficiency and quality. The research shows that by optimizing curriculum structure, updating teaching content, improving teaching methods, and enhancing practical components, it is possible to significantly enhance students' skills and innovation capabilities, better meeting the needs of enterprises in the context of Industry 4.0.

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Huang, Yi-Cheng, and Ching-Chen Hou. "Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback–Leibler Divergence with a Gaussian Mixture Model." Sensors 23, no. 13 (2023): 6174. http://dx.doi.org/10.3390/s23136174.

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Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose during operation, improving the quality of finished products. In this study, feature engineering and principal component analysis were combined with the online and real-time Gaussian mixture model (GMM) based on the Kullback–Leibler divergence’s measure to achieve the real-time monitoring of changes in manufacturing parameters. Based on the attached accelerometer device’s vibration signals and current sensing of the spindle, the developed GMM unsupervised learning was successfully used to diagnose the spindle speed changes of a CNC machine tool during milling. The F1-scores with improved experimental results for X, Y, and Z axes were 0.95, 0.88, and 0.93, respectively. The established FE-PCA-GMM/KLD method can be applied to issue warnings when it predicts a change in the manufacturing process parameter. A smart sensing device for diagnosing the machining status can be fabricated for implementation. The effectiveness of the developed method for determining the manufacturing parameter changes was successfully verified by experiments.

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Atishey, Mittal*, Kumar Atul, and Daniel Freedon. "OPTIMIZATION OF CUTTING PARAMETER FOR EFFICIENT ENERGY IN CNC MILLING MACHINE- A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 384–89. https://doi.org/10.5281/zenodo.47506.

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Energy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a workshop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parameters to reduce the overall machining time while at the same time minimizing the peak energy consumption. In the current trends of optimizing machining process parameters, various evolutionary or meta- heuristic techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining from 2007 to 2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances.  

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Han, Zhenyu, Hongyu Jin, Dedong Han, and Hongya Fu. "ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system." International Journal of Advanced Manufacturing Technology 89, no. 9-12 (2016): 2731–46. http://dx.doi.org/10.1007/s00170-016-9863-y.

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Tnani, Mohamed-Ali, Michael Feil, and Klaus Diepold. "Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring." Procedia CIRP 107 (2022): 131–36. http://dx.doi.org/10.1016/j.procir.2022.04.022.

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Kim, Hyungjung, Woo-Kyun Jung, In-Gyu Choi, and Sung-Hoon Ahn. "A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)." Sensors 19, no. 20 (2019): 4506. http://dx.doi.org/10.3390/s19204506.

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In the new era of manufacturing with the Fourth Industrial Revolution, the smart factory is getting much attention as a solution for the factory of the future. Despite challenges in small and medium-sized enterprises (SMEs), such as short-term strategies and labor-intensive with limited resources, they have to improve productivity and stay competitive by adopting smart factory technologies. This study presents a novel monitoring approach for SMEs, KEM (keep an eye on your machine), and using a low-cost vision, such as a webcam and open-source technologies. Mainly, this idea focuses on collecting and processing operational data using cheaper and easy-to-use components. A prototype was tested with the typical 3-axis computer numerical control (CNC) milling machine. From the evaluation, availability of using a low-cost webcam and open-source technologies for monitoring of machine tools was confirmed. The results revealed that the proposed system is easy to integrate and can be conveniently applied to legacy machine tools on the shop floor without a significant change of equipment and cost barrier, which is less than $500 USD. These benefits could lead to a change of monitoring operations to reduce time in operation, energy consumption, and environmental impact for the sustainable production of SMEs.

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Laddada, S., T. Benkedjouh, M. O. Si- Chaib, and R. DRAI. "Remaining useful life prediction of cutting tools using wavelet packet transform and extreme learning machine." Algerian Journal of Signals and Systems 3, no. 4 (2018): 156–65. http://dx.doi.org/10.51485/ajss.v3i4.72.

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Online tool wear prediction is a determining factor to the success of smart manufacturing operations. The implementation of sensors based Prognostic and Health Management (PHM) system plays an important role in estimating Remaining Useful Life (RUL) of cutting tools and optimizing the usage of Computer Numerically Controlled (CNC) machines. The present paper deals with health assessment and RUL estimation of the cutting tool machines based on Wavelet Packet Transform (WPT) and Extreme Learning Machine (ELM). This approach is done in two phases: a learning (offline) phase and a testing (online) phase. During the first phase, the WPT is used to extract the relevant features of raw data computed in the form of nodes energy. The extracted features are then fed to the learning algorithm ELM in order to build an offline model. In the online phase, the constructed model is exploited for assessing and predicting the RUL of cutting tool. The main idea is that ELM involves nonlinear regression in a high dimensional feature space for mapping the input data via a nonlinear function to build a prognostics model. The method was applied to real world data gathered during several cuts of a milling CNC tool. The performance of the proposed method is evaluated through the accuracy metric. Results showed the significance performances achieved by the WPT and ELM for early detection and accurate prediction of the monitored cutting tools.

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Esheiba, Laila, Iman M. A. Helal, Amal Elgammal, and Mohamed E. El-Sharkawi. "A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems." Sensors 22, no. 6 (2022): 2118. http://dx.doi.org/10.3390/s22062118.

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Nowadays, manufacturers are shifting from a traditional product-centric business paradigm to a service-centric one by offering products that are accompanied by services, which is known as Product-Service Systems (PSSs). PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers. This is combined with service customization, in which configured products are expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage and facilitate the transition to smart connected products. The concept of PSS customization is gaining significant interest; however, there are still numerous challenges that must be addressed when designing and offering customized PSSs, such as choosing the optimum types of sensors to install on products and their adequate locations during the service customization process. In this paper, we propose a data warehouse-based recommender system that collects and analyzes large volumes of product usage data from similar products to the product that the customer needs to customize by adding IoT smart devices. The analysis of these data helps in identifying the most critical parts with the highest number of incidents and the causes of those incidents. As a result, sensor types are determined and recommended to the customer based on the causes of these incidents. The utility and applicability of the proposed RS have been demonstrated through its application in a case study that considers the rotary spindle units of a CNC milling machine.

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Moran, Tess, Rod MacDonald, and Hao Zhang. "A Dynamic Simulation Model for Understanding Sustainability of Machining Operation." Sustainability 15, no. 1 (2022): 152. http://dx.doi.org/10.3390/su15010152.

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The environmental impact of machining operations such as milling, drilling, and turning, is often treated as a conflicting interest when compared to other machining factors such as cost, quality, time, and process settings. It is more beneficial in the long-term for the manufacturer to adjust their practices to be more environmentally conscious. Currently, there are limited existing research showing the linkages between environmental impact of machining and other machining factors. The objective of this study is to create a systems model to examine the linkages of environmental impact with cutting conditions, cost, quality, and efficiency. The model aims to replicate the machining behaviors at the unit process level and generate the long-term implications of their techniques and impacts for engineering decision making. A case study was conducted on a CNC machining operation to create injection molds for climbing holds. The model simulates tool wear and replacement, cutting, energy, cost, and surface quality. The result of this study contributes to the manufacturing knowledge by creating a systems model to quantify and better understand the linkages and trade-offs between environmental impact and decisions surrounding machining operation parameters and technologies. The self-governing behavior of the dynamic model can also be used as a decision-making tool for smart machining control.

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Fattahi, Saman, Takuya Okamoto, and Sharifu Ura. "Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing." Big Data and Cognitive Computing 5, no. 4 (2021): 58. http://dx.doi.org/10.3390/bdcc5040058.

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In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.

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Bien, Jonas, Julius Weihe, Jakob Wagner, Thomas Lagemann, Elke Hergenröther, and Marina Dervisopoulos. "Computer-Vision-gestütztes Einmessen von Rohteilen/Computer-Vision-aided measuring of parts – Development of a robust camera-based measuring system in a 3-axis-machiningcenter." wt Werkstattstechnik online 111, no. 09 (2021): 654–58. http://dx.doi.org/10.37544/1436-4980-2021-09-80.

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Bei CNC-gesteuerten Bearbeitungszentren ist das Erfassen der Rohteilposition und -abmessungen im Arbeitsraum der Werkzeugmaschine ein wichtiger Arbeitsschritt beim Rüstprozess vor dem Fräsvorgang. Meist wird zum Einmessen des Rohteils ein Messtaster verwendet, der das Rohteil in seinen X- und Y- Ausmaßen, in der Z-Höhe und der Rotation vermisst und diese Maße an die Steuerung der Werkzeugmaschine übergibt. Im durch die Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz („Loewe“) des Landes Hessen geförderten Forschungsvorhaben „Smarte Aufspannkontrolle“ wurde ein Computer-Vision-System entwickelt, dass das Erfassen von Rohteilposition und -abmessungen mit einer Kamera erlaubt und damit den Rüstprozess vereinfacht, beschleunigt und weniger anfällig gegenüber Bedienungsfehler macht. &nbsp; In CNC-Mills, recording the position and dimension of raw parts is a critical part of the process. In modern milling-centres, 3D-Touchprobes are used to detect part dimensions and locations. In the research project „Smarte Aufspannkontrolle“ funded by “Loewe” (Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz), a computer vision system (CVS) was developed. The CVS is able to detect part dimensions and locations. Its purpose is to make the part-setup quicker and to lower the risk of human mistakes. &nbsp;

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Barać, Milica, Nikola Vitković, Dragan Marinković, Predrag Janković, and Dragan Mišić. "Optimizing cutting fluid usage in cutting processes on CNC machines: A conceptual digital twin model for ecological sustainability." Acta Technica Jaurinensis, June 14, 2023. http://dx.doi.org/10.14513/actatechjaur.00697.

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The increasing demand for environmentally friendly manufacturing processes has led to the need for optimizing the use of cutting fluids in turning and milling processes. Cutting fluids are commonly used in cutting processes to reduce tool wear and improve cutting performance. However, cutting fluids have a negative impact on environment and human health. This paper proposes a conceptual model of аn information system based on digital twin of the production process. This system will enable monitoring of the manufacturing process and provide a decision support system for helping industrial engineers manage its parameters. The model is represented by using SADT (Structured Analysis and Design Technique), and it is presented by using one of the most common problems of optimizing cutting fluid usage in cutting processes on CNC machines from an ecological perspective. The proposed model considers various cutting process parameters (cutting speed, feed rate, depth of cut) and cutting environment factors (cutting process temperature) to determine the optimal cutting fluid flow rate. To optimize the usage of cutting fluid, the smart information system acquires, processes, and stores data from cutting process temperature and cutting fluid flow sensors to establish the correlation between process parameters and sensor data, which is then used to develop a model. The proposed model can be integrated with existing CNC machines to reduce environmental impact while maintaining high productivity. This paper provides a promising approach for optimizing cutting fluid usage in CNC machining processes while promoting ecological sustainability.

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