CNC Machining Process Research Articles

Freeform surfaces manufactured with a combination of ultra-fine grinding and plasma jet polishing

In order to meet the increasing requirements of freeform production, a new process chain is presented that combines grinding with thermally induced plasma jet polishing. This is demonstrated by an example of a self-designed Alvarez lens geometry made of fused silica glass. A three-stage grinding process is applied, comprising coarse pre-shaping, fine freeform shape generation, and ultra-fine first surface smoothing. Grinding is performed using a 5-axis CNC machining process with spherical diamond grinding tools. After grinding, the surface quality is sufficient for subsequent plasma jet polishing, in terms of shape accuracy, mid-spatial frequency errors, and roughness. The surface shape is retained during polishing while simultaneously achieving a high surface quality and meeting the roughness requirements for optical applications. The requisite improvements to the grinding processes, as well as a parameter optimization for plasma jet polishing are presented. Moreover, the application of a convolution function, which describes plasma jet polishing theoretically, helps to reduce the number of samples necessary to optimize the process chain with respect to favorable transfer points, thereby perfectly linking the processes. The novel process chain offers a versatile and efficient approach for the fabrication of optical freeform surfaces with a final roughness of less than 0.5 nm.

A Direct Contour Control Method for Free-Form Surface Machining Trajectories Based on Coordinate Transformation

The requirement for high-speed and high-precision contouring with free-form surfaces in the CNC machining process is increasing. The contour error is an essential criterion for the quality of CNC machining. For the problem of contour error control, the present research is mainly based on position tracking, and the control method directly aiming at contour control has not been well studied. This paper proposed a new type of coordinate transformation-based direct contour control method (CT-DCC) for free-form surface machining trajectories. By real-time monitoring of the contour error and the foot point of the free-form parametric trajectory, the contouring task is decomposed into the contour error control task in the normal direction and the velocity control problem in the feed direction. Using coordinate transformation, the output commands of the two tasks are converted to the axial motion command. CT-DCC completely eliminates position tracking in the traditional control to achieve direct control of the contour error, which provides a new solution for the contour control problem of free-form surface machining. Both the axial velocity control and the axial torque control-based CT-DCC scheme were studied, and the two-axis and three-axis direct contour controllers were tested. The simulation and experiment results show the feasibility of the proposed method.

Sustainable Manufacturing Practices in the Hydropower Industry: A review

Abstract Hydropower is a crucial source of renewable energy in the ever-changing world, contributing significantly to global electricity generation. As the world focuses more on sustainability, the manufacturing processes within the hydropower industry have undergone considerable transformations to align with eco-friendly practices. This review explores the landscape of sustainable manufacturing practices implemented in the hydropower sector, examining the various strategies, technologies, and initiatives adopted to minimize environmental impact while enhancing efficiency of the project. The review encompasses the significance of sustainable materials and manufacturing techniques within the context of hydropower, highlighting the industry’s commitment to reducing its carbon footprint. It consists of the utilization of advanced materials and technologies aimed at optimizing the manufacturing processes, enhancing the durability and efficiency of hydropower components, and reducing resource consumption. Also, the review highlights the adoption of life cycle assessment (LCA) methodologies in the development and manufacturing phases of hydropower components. These practices facilitate the evaluation of environmental impacts across the entire life cycle of hydropower projects. Furthermore, the review discusses about the implementation of innovative approaches such as Additive manufacturing and CNC machining process as well as casting methods, streamlining production while minimizing waste generation and energy consumption. It also includes the case study of best practices that are prevalent in the context of sustainable manufacturing Additionally, the review examines the role of regulatory frameworks, standards, and certifications in driving sustainable manufacturing practices within the hydropower industry scenario in the world. It also outlines potential future directions to further enhance sustainability in hydropower manufacturing.

Enhancing Furniture Manufacturing with 3D Scanning

Product design and manufacturing leverage 3D scanning for various applications. This study aims to investigate the effectiveness of 3D scanning in furniture production by surveying the literature and showcasing four real-world case studies. The literature review reveals that 3D data acquired from real-world objects have applications in research, rapid prototyping, restoration, and preservation of antique furniture, optimizing CNC machining processes, and measuring furniture components for quality control. The case study descriptions demonstrated the circumstances, rationale, and methodology for 3D scanning. All the case studies analyzed stem from the collaboration between the Laboratory for Product Development and Design at the Faculty of Mechanical Engineering at the University of Sarajevo and various furniture production enterprises from Bosnia and Herzegovina. The conclusions highlight that 3D scanning in the furniture sector is advantageous for developing computer-aided design models from early-stage design prototypes, validating the dimensional accuracy of manufactured components by comparing with CAD models, safeguarding and reconstructing vintage furniture, and remanufacturing formerly produced goods that lack complete technical records (reverse engineering).

Health monitoring of CNC machining processes using machine learning and wavelet packet transform

Monitoring the health of a machining process plays a crucial role in advancing fully automated manufacturing. Faulty machining processes (abnormal process) leads to part rejection and can damage the CNC machines. In this context, data-driven machine learning (ML) models with wavelet packet transform (WPT)-based feature extraction method have gained significant importance in health monitoring. The study of different ML models, mother wavelets, and decomposition levels on real production data is important for effective process health monitoring but are not explored well in the literature. This study analyzes the performance of fifty mother wavelets, two decomposition levels (L2 and L3), and five ML models using actual production facility CNC machine dataset to achieve accurate process health monitoring. Various performance indicators obtained from the confusion matrix have been used to evaluate the ML models. The findings indicate that the Random Forest (RF) and Light-Gradient boosting machine (GBM), using the coif8 and db14 mother wavelet, at decomposition level (L3), deliver the best classification performance. Furthermore, the RF model consistently achieves good precision, sensitivity, accuracy, and F1 scores across most classes, making it a reliable and efficient option for the classification of normal and abnormal machining processes. The SVC, MLP, and CNN models, however, also exhibit competitive performance.

Analisis Pengaruh Kecepatan Pemotongan dan Kedalaman Pemotongan Terhadap Kekasaran Permukaan Baja SKD11 Dengan Metode RSM

In the current development of the manufacturing industry, the quality of each product must be met. One machining process, namely CNC lathe, can meet these demands. Surface roughness is one of the values that must be considered again. The minimum surface roughness indicates a very good CNC machining process, because the smaller the surface roughness value of a product, the better the resulting product value. To achieve that all need to du the turning in the right process variables. The process variables that are varied are cutting speed (m/mnt) and depth of cut (mm). Each variable has three levels, mnamely cutting speed 240, 250, and 260. For depth 0,1, 0,15, and 0,2. The research method used to obtain surface roughness values is the response surface methodology (RSM) with a central composite design. The material used in this research is SKD 11 steel. The minimum roughnees is at acutting speed of 240 m/min and a depth of cut of 0,325 mm, the roughness value abtained is an average of 1,627 (µm). for maximum roughness at a cutting speed of 230 m/min and depth of cut of 0,225 mm, the roughness value obtained of 1,975 (µm).

The Right CAD/CAM Software

Aktiv Solutions has optimised it CNC machining processes with continuous investment in its CAD/CAM software investment which has boosted production and brought some major benefits

Research on CNC programming and machining process based on CAD/CAM technology

Abstract Focusing on CNC process data, this paper provides an in-depth analysis, characterization and mining of macro machining processes by associating CAD and CAM models. The article introduces a Bayesian inference method to construct an association relationship between the manufacturing features of the query part and the macroscopic process of the manufactured part, which avoids the need for a direct similarity comparison between the geometry of the query part and the part to be manufactured. In addition, the study calculated the overall similarity between the query part and the CNC programming process data instances through the guidance of the process skeleton, thus realizing the precise evaluation from the CNC programming perspective. The algorithm for generating tool trajectories in multi-axis CNC programming is analyzed. By selecting representative surfaces and performing tool processing calculations, this study explores the process planning and tool parameter selection of usual parts of surfaces in multi-axis CNC programming machining. It optimizes the nonlinear error in tool trajectory processing by combining CAD/CAM technology. The results show that the optimized process proposed in this paper significantly improves the machining efficiency by 36.94%. In the cutting at tool corners, the maximum cutting force generated by the optimized machining process is only 190.3N, which is only 67.2% of the leading cutting force of the Cimatron process. In addition, when the engagement frequency is 780 Hz, the optimized process proposed in this paper has the smallest vibration amplitude, which is only 21.4% of the Cimatron strategy. Therefore, this study significantly improves the machining efficiency while ensuring the machining quality, which has important practical application value.

Comparison of Primary Energy Consumption Between Additive Manufacturing Processes and CNC Machining Applied to Components in the Oil and Gas Sector

This work elucidates the environmental impact on the production of components in the oil and gas sector by analyzing two different production routes. To this end, a life cycle assessment of the CNC machining process and additive manufacturing (AM) was carried out using Multi Jet Fusion (MJF) technology. Life cycle inventories were prepared for both processes, using the Open LCA software and information from the Ecoinvent database to develop the life cycle, with Cumulative Energy Demand (CED) being considered as the impact method. From the analysis, it was found that the machining process presented an energy demand of 7177 MJ. In contrast, the MJF presented 3214 MJ, less than half of the primary energy required to produce one unit of the studied component, thus indicating that manufacturing by MJF presents greater environmental sustainability than the Oil and Gasconventional machining process.

A life cycle analysis approach to evaluate sustainable strategies in the furniture manufacturing industry

Life Cycle Assessment (LCA) plays a crucial role in sustainability evaluations and impact assessments, especially in the realm of eco-design and environmentally friendly manufacturing in the furniture sector. However, the existing research tends to focus on specific stages of the furniture life cycle and leaves a substantial portion of the environmental impact unaddressed, including resource consumption, waste generation, and emissions throughout the entire value chain. This research presents a comprehensive LCA of the production and distribution process of a Pinewood Table, aiming to evaluate and improve its environmental sustainability. Six different production and distribution models were analyzed, including a base model strategy and five alternative strategies incorporating various sustainable policies. The study assessed the environmental impacts at each stage, considering resource consumption, emissions, and waste generation. Additionally, a cost analysis was conducted to identify the most economically viable sustainable option among the alternative strategies. Among the six production and distribution models studied, Alternative Strategy 5 demonstrates the lowest Climate Change CO2 equivalent emissions, with Alternative Strategy 2 following closely. This outcome underscores the importance of reducing forklift usage and transitioning to photovoltaic cells, resulting in a 50 % reduction in carbon emissions during CNC machining processes. Results also indicated that the collaborated strategy exhibited the highest reduction in Climate Change CO2 eq which is 65.4 %, highlighting the significance of reducing forklift usage and transitioning to photovoltaic cells for electricity generation. Finally, a sustainable option where all the strategies were collaborated is recommended for practitioners, emphasizing the significance of implementing eco-friendly practices to enhance the overall sustainability of pinewood table production and distribution. This research contributes to literature by providing essential information and guidance for industry stakeholders and policymakers to make informed decision-making and promote sustainable practices within the furniture manufacturing sector.

Reducing Cycle Time in the Manufacturing Process of Circular Pattern Interpolar Products (CPIP) Using Special Fixtures on a 3-Axis CNC Machine

This research focuses on the design of fixture aids to support the manufacturing process of Circular Pattern Interpolation Products (CPIP), such as disk brake components, flanges, turbine disk runners, and other plate components with circular feeding patterns in the 3-Axis CNC machining process. The objective of this study is to achieve more efficient CPIP production times on 3-Axis CNC machines. The research methodology employed a design and build approach, involving the design of fixture aids, fabrication, and testing during the production of turbine disk runners on a 3-Axis CNC machine. This was done by comparing the initial workpiece setup time and the workpiece clamping transfer time using CPIP production aids versus not using CPIP production aids. The results showed that the designed CPIP Fixture significantly reduced the CPIP manufacturing Cycle Time, with a reduction of 63.11% in the initial workpiece setup time and 83.55% during the clamping transfer process.

Physics-informed Gaussian process for tool wear prediction

The tool wear monitoring (TWM) system plays an increasingly important role to ensure high quality finishing and system safety in advanced CNC machining process. The pure data-based TWM approaches generally needs to develop complex machine learning models and require massive sensory data to learn the models to reach high monitoring accuracy, while the physics-based tool wear models are simple but hard to adapt to varied working conditions. In order to incorporate the benefits of both methods, a novel physics-informed Gaussian process model is developed to predict the tool wear. Different from the traditional approaches, three tool wear physical models are introduced to develop the physics-informed Gaussian process regression (PB-GPR) model. The wear model is applied to constrain the mean function of the Gaussian process, so that the PB-GPR is more in line with the actual tool wear. At the same time, the model can initiate small data training to meet limited tool wear labels in practice, and then update the model with new measurements. Multi-sensor signals are collected and multi-domain features are extracted for the model learning. The proposed approach is validated from high speed milling experiments. The results show a significant performance improvement including tool wear prediction accuracy and robustness in extrapolation compared to the conventional machine learning methods.

A comprehensive review of machine learning techniques in computer numerical controlled machines

Machine learning (ML) is significant advancements in computer science and data processing systems that may be utilized to improve almost all technology-enabled services, goods, and industrial applications. Machine learning is a branch of computer science and artificial intelligence. It emphasizes the use of data and algorithms to mimic the learning process of machines and improve system accuracy. To extend the life of the cutting tools used in machining processes, machine learning algorithms may be used to forecast cutting forces and cutting tool wear. In order to improve productivity during the component production processes, optimized machining parameters for CNC machining operations may be obtained by applying cutting-edge machine learning algorithms. Furthermore, the appearance of Advanced machine learning algorithms can forecast and enhance machinable components to raise the caliber of machinable parts. Machine learning is applied to prediction approaches of energy consumption of CNC machine tools in order to analyses and minimize power usage during CNC machining processes. The use of machine learning and artificial intelligence systems in CNC machine tools is examined in this paper, and future research projects are also suggested in order to offer an overview of the most recent studies on these topics. As a result, the research filed can be moved forward by reviewing and analyzing recent achievements in published papers to offer innovative concepts and approaches in applications of machine learning in CNC machine tools.

Optimizing cutting fluid usage in cutting processes on CNC machines: A conceptual digital twin model for ecological sustainability

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.

CNC Turning of an Additively Manufactured Complex Profile Ti6Al4V Component Considering the Effect of Layer Orientations

Electron beam melting (EBM) is one example of a 3D printing technology that has shown great promise and advantages in the fabrication of medical devices such as dental and orthopedic implants. However, these products require high surface quality control to meet the specifications; thus, post-processing, such as with machining processes, is required to improve surface quality. This paper investigates the influence of two-part orientations of Ti6Al4V EBM parts on the CNC machining (turning) process. The two possible EBM part orientations used in this work are across EBM layers (AL) and parallel to the EBM layer (PL). The effect of the EBM Ti6Al4V part orientations is examined on surface roughness, power consumption, chip morphology, tool flank wear, and surface morphology during the dry turning, while using uncoated carbide tools at different feed rates and cutting speeds. The results showed that the AL orientation had better surface quality control and integrity after machining than PL orientation. Using the same turning parameters, the difference between the roughness (Ra) value for AL (0.36 μm) and PL (0.79 μm) orientations is about 54%. Similarly, the power consumption in AL orientation differs by 19% from the power consumption in PL orientation. The chip thickness ratio has a difference of 23% between AL and PL orientations, and the flank wear shows a 40% difference between AL and PL orientations. It is found that, when EBM components are manufactured along across-layer (AL) orientations, the impact of part orientation during turning is minimized and machined surface integrity is improved.

A data-driven digital twin framework for key performance indicators in CNC machining processes

ABSTRACT This paper presents a data-driven digital twin (DT) framework that predicts key performance indicators (KPIs) in a CNC machining environment. The decision-makers can use these predicted KPIs in the CNC machining process flow to better choose cutting parameters to accomplish the required KPIs. Those beneficiaries would be the process planner in the process planning stage and the machine operator in the machining stage. The cutting parameters affect major performance KPIs such as machining time, quality, and energy consumption. So, correctly selected cutting parameters can improve KPIs in CNC machining operations. In this paper, the two KPIs considered for building predictive models, and their application in the proposed DT with experimental data are energy and surface roughness. The data for building the predictive models for a CNC milling process are obtained through experiments. This work also illustrates the choice of predictive modelling methods in both the stages of CNC machining and its outcomes.

Analisis Kekasaran Permukaan Pada Pengaruh Kecepatan Spindel Dan Feeding Terhadap Material Baja AISI 1045 Pada Mesin Bubut CNC

In this era of increasingly advanced industrial technology development, of course, everyone wants the best results in making a product. For example in the machining process, one of which is CNC lathe machining, the level of roughness of a product really needs to be reviewed. The minimum surface roughness shows that the quality of a CNC machining process is very good, because the smaller the surface roughness value, the greater the quality value of a product, therefore in order to achieve this goal it is necessary to set the right parameters. Parameters varied were spindle speed (Rpm) and feeding. In this study the method used is the experimental method, with a combination of parameters to obtain surface roughness values. The material used is AISI 1045 steel, with the results of the data being analyzed using the Taguchi Smaller the Better method in Minitab 21.3 software. From the results of the calculation of the mean factor, the percent contribution to the spindle speed was obtained at 56.28%, while at feeding it was obtained at 38.14%. The optimal roughness value is obtained from the combination of parameters in the CNC lathe process on AISI 1045 steel which can make the optimal response value obtained at a spindle speed of 1645 rpm and a feeding of 0.18 mm/rev.

A Feasibility Study on Mixed Reality-Based Visualization and Interaction Tool for Performance Improvement of Metal Cutting Processes

Modern CNC machining industries rely on the application of high-technology virtual simulations such as Finite Element Analysis (FEA) to become economically competitive, improve productivity, and ensure sustainability. However, the traditional way of using FEA in CNC machining industries is to perform the virtual studies at a completely offline location, that often leads to erroneous results, along with massive wastage of resources, time, and money. Real-time FEA is an emerging technique that generates real-time solutions in response to actual load variations. This research aims to integrate real-time FEA results with the corresponding real CNC machining process using Mixed Reality (MR) technologies to facilitate the machining operations to be economically competitive with higher efficiencies and improved sustainability. The proposed MR-based system enhances the real-time decision-making capability of the CNC machine operator. The preliminary results show that the use of real-time FEA could significantly improve the CNC machining results.

Industry-Oriented System Architecture for Feature-Based Data Management in CNC Machining Processes

Industry-Oriented System Architecture for Feature-Based Data Management in CNC Machining Processes

Adaptive CNC machining process optimization of near-net-shaped blade based on machining error data flow control

Adaptive CNC machining process optimization of near-net-shaped blade based on machining error data flow control