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This paper presents a new innovative approach for designing and manufacturing a fixture for locating and clamping porous blocks of biocompatible material, which is required for their machining on CNC machines. Manufacturing porous blocks for their application in medical and/or dental fields is gaining traction. However, limited solutions are available today. In order to address this issue, a new design has been proposed for locating and clamping porous blocks. Finite element analysis was used as a verification tool for the designed fixture with the workpiece, which showed a low concentration of stresses. After the manufacturing, dimensional verification in the form of CAD analysis showed small deviations on the manufactured object with deviations peaking around +0.015 mm, thus validating the adequate locating and clamping of the workpiece. [ABSTRACT FROM AUTHOR]

Turning is an often observed technique in the field of metal cutting. During CNC machining, a work material is rotated along its axis while cutting tools at the centre of its axis are used to remove excess material and create the desired component. The ultimate level of quality and accuracy of the end result depends on several factors. In this context, three crucial parameters stand out as key contributors: cutting speed, feed rate, and depth of cut. The specific input parameter chosen for the working material is EN 8, and the cutting tool used is a carbide insert. Observation of tool wear can be made by examining the effects of velocity, feed rate, and cutting depth under different conditions. EN8 steel is a type of steel that has a significant amount of carbon and has a stiffness of around 600 MPa. The displayed graphs obtained from the conducted CNC machining process research provide clear insights into the patterns that demonstrate how the progression of tool flank wear ultimately results in failure. Through the implementation of several experiments, we successfully examined the influence of input variables on both the Material Removal Rate and Tool Wear. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has become a key topic in the manufacturing industry, challenging conventional techniques. However, AM has its limitations, and understanding its convenience despite established processes remains sometimes difficult, especially in preliminary design phases. This investigation provides a hybrid multi-criteria decision-making method (MCDM) for comparing AM and conventional processes. The MCDM method consists of the Best Worst Method (BWM) for the definition of criteria weights and the Proximity Index Value (PIV) method for the generation of the final ranking. The BWM reduces the number of pairwise comparisons required for the definition of criteria weights, whereas the PIV method minimizes the probability of rank reversal, thereby enhancing the robustness of the results. The methodology was validated through a case study, an aerospace bracket. The candidate processes for the bracket production were CNC machining, high-pressure die casting, and PBF-LB/M. The production of the bracket by AM was found to be the optimal choice for small to medium production batches. Additionally, the study emphasized the significance of material selection, process design guidelines, and production batch in the context of informed process selection, thereby enabling technical professionals without a strong AM background in pursuing conscious decisions. [ABSTRACT FROM AUTHOR]

Linear commands need to be smoothed by inserting high-order continuous splines at corners to eliminate the tangential discontinuities of connection points. Although several local smoothing methods have been developed, it is still a challenge to improve the machining efficiency further, when following the transition spline with the predefined fixed proportion constraints. In fact, the curvature distribution of the transition spline has a direct impact on the transition velocity and machining efficiency. This article presents a real-time local smoothing method with curvature-optimized transition splines to smooth linear commands. The designed transition spline, composed of a biairthoid spline and a circular segment, is analytically determined by maximizing the allowable velocity and staying within the predefined geometric constraints. As a result, the curvature increment induced by only biairthoid splines is significantly prevented, and thus, faster transition motion is gracefully attained. With the analytical expression of the arc length and curvature extreme, it is friendlier to the CNC system for real-time executing smooth motion. Simulations and experiments demonstrate that the proposed method can shorten the machining time by more than 8% when compared to the traditional only biairthoid splines. [ABSTRACT FROM AUTHOR]

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified. [ABSTRACT FROM AUTHOR]

Integrating Industry 4.0 and Quality 4.0 optimises manufacturing through IoT and ML, improving processes and product quality. The primary challenge involves identifying patterns in computer numerical control (CNC) machining time‐series data to boost manufacturing quality control. The proposed solution involves an experimental study comparing one‐class and binary classification algorithms. This study aims to classify time‐series data from CNC turning machines, offering insight into monitoring and adjusting tool wear to maintain product quality. The methodology entails extracting spectral features from time‐series data to train both one‐class and binary classification algorithms, assessing their effectiveness and computational efficiency. Although certain models consistently outperform others, determining the best performing is not possible, as a trade‐off between classification and computational performance is observed, with gradient boosting standing out for effectively balancing both aspects. Thus, the choice between one‐class and binary classification ultimately relies on dataset's features and task objectives. [ABSTRACT FROM AUTHOR]

Process knowledge reuse technology can improve the computer numerical control (CNC) programming efficiency and shorten the production cycle for similar parts. However, current process knowledge reuse methods focus on retrieving similar machining cases based on the machining features or topological structures of a part, which failed to deeply consider the close relationship between the 3D part geometry and CNC machining process, can result in incomplete matches between the reused knowledge and CNC process. It is more obvious for the similar complex surface parts that the most existing reuse methods are hardly to integrate 3D part geometry with CNC machining process. To overcome the problem, a reuse method for similar ruled surface blades is proposed through dual mapping mechanism in this paper, which is based on the 3D solid mapping model and spatial transformation relationship. First of all, a dual mapping mechanism is constructed to transfer process knowledge from the parameter domain to physical domain for similar ruled surface blades. Secondly, the acquired process knowledge of the ruled surface blade is characterized to the process knowledge template by the multi-vector function, followed by its inheritability is analyzed for the process knowledge reuse. Finally, two similar ruled surface blades are machined in an experiment, the machined surfaces of both blades are smooth without obvious pits in no violent vibration machining, furthermore, the surface roughness value is 4.314 µm and 4.933 µm of the two similar blades, which can meet the requirement of semi-fishing, this result indicates the correctness and validity of the process knowledge reuse method presented in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm for the automatic generation of toolpaths for machining complex geometric positions, such as molds used in orthosis production. The production of individualized orthoses often requires the use of multi-axis machining systems, such as five-axis machines or industrial robots. Typically, complex and expensive CAD/CAM systems are used to generate toolpaths for these machines, requiring the definition of a machining strategy for each surface. While this approach can achieve a reliable and high-quality machining process, it is very time-consuming and makes it challenging to meet the criteria for rapid production of orthopedic aids. Given that their production is a custom-made process using individual shapes as inputs, the toolpath generation process becomes even more demanding. To address these challenges, this paper proposes an algorithm suitable for the automatic generation of toolpaths for such complex positions. The proposed algorithm has been tested and has proven to be robust and applicable. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) technologies have demonstrated a promising material efficiency potential in comparison to traditional material removal processes. A new directed energy deposition (DED) category AM process called wire arc additive manufacturing (WAAM) is evolving due to its benefits which include faster build rates, capacity to build large volumes, and inexpensive feedstock materials and machine tools compared to more technologically mature powder-based AM technologies. However, WAAM products present challenges like poor surface finish and lower dimensional accuracy compared to powder-based processes or machined parts, prevalence of thermal distortions, residual stresses, and defects like porosity, cracks, and humping, often requiring post-processing operations like finish machining and heat treatment. These post-processing operations add to the production cost and environmental footprint of WAAM-built parts. Therefore, considering the opportunities and challenges presented by WAAM, this paper analyses the environmental impact, production costs, and mechanical properties of WAAM parts and compares them with those achieved by laser powder bed fusion (LPBF) and traditional computer numerical control (CNC) milling. A high-strength low-alloy steel (ER70S) mechanical part with medium complexity was fabricated using WAAM. Based on the data collected during this experiment, environmental impact and cost models were built using life cycle assessment and life cycle costing methodologies. WAAM was observed to be the most environmentally friendly option due to its superior material efficacy than CNC milling and has a better energy efficiency than LPBF. Also, WAAM was the most cost-friendly option when adopted in batch production for batch sizes above 3. The environmental and cost potential of WAAM is amplified when used for manufacturing large products, resulting in significant material, emission, and cost savings. The fabricated WAAM part demonstrated good mechanical properties comparable to that of cast/forged material. The methodology and experimental data presented in this study can be used to calculate environmental impacts and costs for other products and can be helpful to manufacturers in selecting the most ecofriendly and cost-efficient manufacturing process. [ABSTRACT FROM AUTHOR]

The research of hybrid composites' machinability is an intricate modern trend. Industries then evaluate the surface finish of the machined surfaces to ensure they are up to snuff with the needs of the applications. Therefore, the purpose of this research is to examine the machinability performance of AA8014 workpieces (welded by friction stir processing (FSP) with hybrid reinforcement of Titanium diboride and Zirconium dioxide at CNC vertical milling centre, including material removal rate and surface finish (surface roughness). By changing the reinforcement percentage from 4 to 10%, the tool rotational speed from 1000 to 1600 rpm, the traverse speed of the tool head from 4 mm/min setting to 5 mm/min setting, and the axial force from 3 to 4 kN at four levels, we were able to weld the samples with 16 different combinations (L16 orthogonal array) of FSP parameters settings. Taguchi analysis and planning were used to optimize the pace of material removal and smooth out the surface. After milling, the weldment's maximum material removal rate was measured at 2.48 mm3/min, and its minimum surface roughness was measured at 1.84 m. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Computer-aided process planning, as the link between designing and machining, is expected to move towards being of more automation and intelligence with the current trend of intelligent manufacturing. Hence, a solution with STEP-NC and feature-based technology for intelligent process planning is proposed based on the analysis of the mainstream enabling technologies in process planning, making full use of the data advantage of the STEP-NC standard and integrating process planning unit module from the content. More automated operations will be implemented through taking machining features as planning units and developing integrated built-in models as well as algorithms to aid decision-making. The solution is implemented by developing a process planning prototype system, wherein key implementation techniques and model-based planning algorithms are introduced. To verify the feasibility of the solution, two test parts of the industrial application level are applied to the prototype system. The application results show that the solution has promising application prospects and reference value for improving the automation and intelligence of the whole planning process. [ABSTRACT FROM AUTHOR]

CNC machining is widely used in the manufacturing industry and can produce high-quality products in large quantities. However, defective products occur due to uncertain factors during machining, and monitoring the process to detect and control them early is essential. Furthermore, quality is an important monitoring indicator that can identify factors such as the condition of the process and the timing of tool replacement. Therefore, monitoring studies of CNC machining should consider quality as the monitoring indicator. In this study, we built a product manufacturing process using a CNC turning and acquired process signals and quality data through designed machining experiments. Based on the experimental data, a machine learning algorithm builds various models to identify quality-varying process signals. In addition, we develop a monitoring indicator that represents product quality using the identified signals. This research extracts the features that affect the quality in the frequency domain and validates the features. Also, it can contribute to monitoring technology in the manufacturing field based on CNC machines. [ABSTRACT FROM AUTHOR]

Step error is the machining error between adjacent cutter location points in the feeding direction. In order to improve the computational efficiency, a hybrid particle swarm optimization method (HPSO) combining genetic algorithm (GA) and simulated annealing (SA) algorithm is proposed. The mapping relationship between local cutter contact (CC) curve in step error calculation and the particle search range and the fitness calculation model are established. The maximum fitness value is taken as step error. The chaotic initialization population is carried out by the Tent mapping. Two nonlinear control methods based on the Sigmoid function and the numbers of iterations are proposed for inertia weight and learning factors, respectively. Combined with the above optimizations, an improved particle swarm optimization algorithm (IPSO) is proposed algorithm is formed. Based on IPSO, the crossover and mutation strategies of GA are used to increase particle diversity, and then, Metropolis criterion from SA is applied to the particle selection; the improved crossover and mutation particle swarm optimization algorithm (ICMPSO) is formed. IPSO is used for Elite particles with higher fitness values to enhance the convergence speed. The other Ordinary particles employ ICMPSO to improve global search capability. The combination of IPSO and ICMPSO forms a whole hybrid particle swarm optimization (HPSO) method. All the proposed algorithms are implemented, and two typical free-form surfaces are taken as examples to calculate step errors. The calculation results show that the tool path generation time of the proposed method is lower than that of the geometric iterative algorithm and the standard particle swarm optimization algorithm, which verifies the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and experimental validation of a novel canned cycle for CNC milling machines, enabling the precise and efficient cutting of threads with fixed or variable pitch and radius. Conventional canned cycles are limited to fixed pitch threads, restricting the versatility of CNC milling machines in thread machining applications. The development process involves integrating a sophisticated control algorithm into the CNC milling machine's software, giving the operator remarkable control over the thread cutting process. This algorithm allows the operator to choose between external or internal threads, set both initial and final radii, determine initial and final pitches, specify the number of turns, and select the left or right-hand thread type. Such flexibility enables the creation of threads with diverse geometries. Furthermore, the proposed canned cycle provides the capability to switch between roughing and finishing passes by adjusting the step motion along the prescribed helical curve. Simulation tests conducted under various threading cases clearly demonstrate the efficiency of the proposed canned cycle. These results showcase its capability to address a wide range of machining scenarios, offering practical solutions applicable across a spectrum of applications. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In current five-axis computer numerical control (CNC) machining, the use of minute linear path segments as an approximation for the ideal cutter contacting (CC) point trajectory is still prevalent. However, introducing rotation axes leads to a deviation of the actual CC point trajectory from the ideal, resulting in nonlinear errors. An integrated method is proposed in this paper for compensating and correcting both the contour error, associated with the approximation of the part surface by the ideal CC point trajectory and the nonlinear error of the CC point trajectory based on the information in the CC point data. By analyzing the spatial relationship between the tool posture and the CC point path during the five-axis linear interpolation process, two adjacent machining tool positions containing CC point data information are selected as the starting and ending points of the five-axis linear interpolation machining. The ideal tool center point and the actual CC point are calculated during the interpolation process, as well as the distance and the unit vector in the perpendicular direction between the actual CC point and the ideal CC point trajectory segment. In the comprehensive error compensation and correction phase, the obtained unit vectors are used as direction vectors for error compensation, and the tool center point during interpolation is first compensated and corrected. This ensures the actual CC point and the contour curve are on the same plane. The compensation direction for contour error is calculated using the start/end tool axis vectors and the ideal CC point trajectory vectors. The size of the contour error approximating the contour curve is calculated through the chord error. A second compensation and correction are applied to the tool center point for interpolation, ultimately achieving comprehensive compensation and correction of nonlinear errors. The data calculations were conducted in the MATLAB environment using actual machining data. After compensation and correction, the contour error was reduced by 76%, the nonlinear error of the CC point trajectory decreased to below 0.88 μm, and the comprehensive nonlinear error of the CC point trajectory was reduced from 19 to 1.5 μm, a reduction of 93%. This demonstrates significant practical value in enhancing the accuracy of five-axis CNC machining. Through actual machining verification, after using the method described in this paper, the average surface roughness decreased from 1.133 to 0.220 μm, and the maximum surface roughness decreased from 6.667 to 1.240 μm. This significantly demonstrates that the compensation and correction method proposed in this paper can significantly improve the surface quality of machined parts.

This paper presents a new innovative approach for designing and manufacturing a fixture for locating and clamping porous blocks of biocompatible material, which is required for their machining on CNC machines. Manufacturing porous blocks for their application in medical and/or dental fields is gaining traction. However, limited solutions are available today. In order to address this issue, a new design has been proposed for locating and clamping porous blocks. Finite element analysis was used as a verification tool for the designed fixture with the workpiece, which showed a low concentration of stresses. After the manufacturing, dimensional verification in the form of CAD analysis showed small deviations on the manufactured object with deviations peaking around +0.015 mm, thus validating the adequate locating and clamping of the workpiece.

Optimization of industrial processes such as manufacturing or processing of specific materials constitutes a point of interest for many researchers, and its application can lead not only to speeding up the processes in question, but also to reducing the energy cost incurred during them. This article presents a novel approach to optimizing the spindle motion of a computer numeric control (CNC) machine. The proposed solution is to use deep learning with reinforcement to map the performance of the reference points realization optimization (RPRO) algorithm used in the industry. A detailed study was conducted to see how well the proposed method performs the targeted task. In addition, the influence of a number of different factors and hyperparameters of the learning process on the performance of the trained agent was investigated. The proposed solution achieved very good results, not only satisfactorily replicating the performance of the benchmark algorithm, but also speeding up the machining process and providing significantly higher accuracy. [ABSTRACT FROM AUTHOR]

In a novel application of 1D Convolutional Neural Networks (1D-CNN), this study pioneers a tri-class classification framework for accurately forecasting the Remaining Useful Life (RUL) of milling tools. By harnessing the 1D-CNN’s innate capability to analyze raw time-series data, we eliminate the traditional bottleneck of extensive feature engineering. Our model undergoes rigorous validation using a leave-one-out cross-validation method, catering to the constraints of a limited dataset. When optimized, the model delivers compelling performance metrics: average accuracy, precision, and recall scores stand at 0.90 ± 0.02, 0.85 ± 0.12, and 0.87 ± 0.08, respectively. What sets this work apart is its dual utility: not only does it excel in tool health assessment, but its output also serves as a diagnostic tool for experimental setups. For instance, anomalies detected in the model’s predictions can act as early warnings for potential sensor malfunctions. Additionally, the model’s performance metrics offer invaluable guidance in optimizing experimental parameters, such as choosing the most efficient sampling rate. In summary, this study not only establishes the robustness of 1D-CNNs in assessing milling tool health but also unveils their untapped potential as diagnostic aids for fine-tuning experimental setups.

Additive manufacturing (AM) has become a key topic in the manufacturing industry, challenging conventional techniques. However, AM has its limitations, and understanding its convenience despite established processes remains sometimes difficult, especially in preliminary design phases. This investigation provides a hybrid multi-criteria decision-making method (MCDM) for comparing AM and conventional processes. The MCDM method consists of the Best Worst Method (BWM) for the definition of criteria weights and the Proximity Index Value (PIV) method for the generation of the final ranking. The BWM reduces the number of pairwise comparisons required for the definition of criteria weights, whereas the PIV method minimizes the probability of rank reversal, thereby enhancing the robustness of the results. The methodology was validated through a case study, an aerospace bracket. The candidate processes for the bracket production were CNC machining, high-pressure die casting, and PBF-LB/M. The production of the bracket by AM was found to be the optimal choice for small to medium production batches. Additionally, the study emphasized the significance of material selection, process design guidelines, and production batch in the context of informed process selection, thereby enabling technical professionals without a strong AM background in pursuing conscious decisions.

This paper proposes a new algorithm for the automatic generation of toolpaths for machining complex geometric positions, such as molds used in orthosis production. The production of individualized orthoses often requires the use of multi-axis machining systems, such as five-axis machines or industrial robots. Typically, complex and expensive CAD/CAM systems are used to generate toolpaths for these machines, requiring the definition of a machining strategy for each surface. While this approach can achieve a reliable and high-quality machining process, it is very time-consuming and makes it challenging to meet the criteria for rapid production of orthopedic aids. Given that their production is a custom-made process using individual shapes as inputs, the toolpath generation process becomes even more demanding. To address these challenges, this paper proposes an algorithm suitable for the automatic generation of toolpaths for such complex positions. The proposed algorithm has been tested and has proven to be robust and applicable.

This paper presents a new method to generate iso-scallop tool paths for triangular mesh surfaces. With the popularity of 3D scanning techniques, scanning-derived mesh surfaces have seen a significant increase in their application to machining. Quite often, such mesh surfaces exhibit defects such as noises, which differentiate them from the good-quality mesh surfaces previous research work focuses on. To generate tool paths for such low-quality mesh surfaces, the primary challenge lies in robustness against the defects. In this work, a robust tool path generation method is proposed for low-quality mesh surfaces. In addition to robustness, the method is quite efficient, providing the benefit of faster iterations and improved integration between scanning and machining. The fundamental principle of the method is to convert the tool path generation problem to the heat diffusion problem that has robust and efficient algorithms available. The effectiveness of the method will be demonstrated by a series of case studies and comparisons. [ABSTRACT FROM AUTHOR]

The contour following task of a multi-axis servo system is one of the most important applications of modern computer numerical control (CNC) machining. Reducing the contour error is an important content in the multi-axis contour following task. A common method to solve this problem is the cross-coupling control (CCC). Since the traditional CCC method cannot meet the requirements of tracking accuracy and contour control accuracy at large curvature positions in free-form curve machining, the main contribution of this paper is to propose a novel integrated control strategy based on arc length parameters for contour error compensation, which consists of a double-circle weighted approximation contour error estimation model based on arc length parameters module, an improved cross-coupling position command shaping controller (CPCSC) module, and an improved position error compensator (PEC) module. To improve the accuracy of contour error estimation for large free-form curvature trajectories, a double-circle weighted approximation contour error estimation model based on arc length parameters is proposed. The method first finds the nearest interpolation point by backtracking method and calculates the backward reference points by using the method based on arc length parameters. Then, the obtained backward reference points are used as the expected instruction points by the double-circle weighted approximation method, and the estimated value of contour error is calculated. Moreover, an improved structure of CPCSC is proposed. Compared with the traditional biaxial CCC structure, the advantage of this new structure is that the compensator design and stability analysis in its CCC are relatively simple, and it can be easily implemented on most current systems by reprogramming the reference position command subroutine. In addition, an improved PEC method is further proposed to reduce contour error. The main advantage of this module is that it can simultaneously improve tracking and contouring performances by compensating position errors in advance. The feasibility of the proposed integrated control strategy is verified by serval non-uniform rational B-spline (NURBS) parametric curve contour following experiments. Moreover, the results of comparative experiments indicate that the proposed integrated control strategy can significantly improve the tracking and contour control accuracy of biaxial contour following tasks compared with none-CCC method and CCC method, and has better contour control performance in large curvature positions. [ABSTRACT FROM AUTHOR]

In the field of mechanical manufacturing, CNC machining technology plays an important role in improving the precision and efficiency of part processing. However, how to further improve the effect of NC machining by optimizing milling parameters is still a key problem. The aim of this study is to optimize CNC milling parameters through systematic research and experiments to improve the machining efficiency and quality of mechanical parts. By adjusting key parameters such as tool speed, feed speed, and removal rate cutting depth, the influence of these parameters on the milling process was systematically studied using advanced CNC machining equipment. Through the collection and analysis of experimental data, the mathematical model is established, and the optimization algorithm is applied to find the best combination of milling parameters. The experimental results show that under the optimal combination of parameters, the surface quality of parts can be significantly improved, the machining time can be reduced, and the tool wear can be reduced. This research successfully realizes the optimization of milling parameters of mechanical parts by CNC machining technology and provides an effective solution for improving machining efficiency and reducing costs. This not only has guiding significance for the application of CNC machining technology but also has important promotion value in actual production. [ABSTRACT FROM AUTHOR]

The combination of Atomic Diffusion Additive Manufacturing (ADAM) and traditional CNC machining allows manufacturers to leverage the advantages of both technologies in the production of functional metal parts. This study presents the methodological development of hybrid manufacturing for solid copper parts, initially produced using ADAM technology and subsequently machined using a 5-axis CNC system. The ADAM technology was dimensionally characterized by adapting and manufacturing the seven types of test artifacts standardized by ISO/ASTM 52902:2019. The results showed that slender geometries suffered warpage and detachment during sintering despite complying with the design guidelines. ADAM technology undersizes cylinders and oversizes circular holes and linear lengths. In terms of roughness, the lowest results were obtained for horizontal flat surfaces, while 15° inclined surfaces exhibited the highest roughness due to the stair-stepping effect. The dimensional deviation results for each type of geometry were used to determine the specific and global oversize factors necessary to compensate for major dimensional defects. This also involved generating appropriate over-thicknesses for subsequent CNC machining. The experimental validation of this process, conducted on a validation part, demonstrated final deviations lower than 0.5% with respect to the desired final part, affirming the feasibility of achieving copper parts with a high degree of dimensional accuracy through the hybridization of ADAM and CNC machining technologies. [ABSTRACT FROM AUTHOR]

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators—average roughness, peak-to-valley roughness, and diameter deviation—are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents the development of a new canned cycle for CNC milling machines capable of cutting threads with variable pitch, addressing a significant limitation in modern CNC systems. While existing CNC milling machines provide canned cycles for thread cutting, they lack the capability to program variable pitch, necessitating reliance on CAM systems. In response, the proposed canned cycle offers increased flexibility, quicker setup times, and reduced dependence on CAM systems. The cycle accommodates both internal and external threads, supports left-hand or right-hand threading, and incorporates a user-friendly control panel for easy programming. To validate its efficiency, the canned cycle is implemented using a G-code parametric algorithm, and a series of simulation tests are conducted. The results demonstrate the viability and advantages of the new CNC milling feature in cutting threads with variable pitch, opening up possibilities for various mechanical applications. [ABSTRACT FROM AUTHOR]

This study investigates the thermal generation dynamics in the spindle unit of a CNC milling machine. The main objective is to analyze the heat transfer from the bearings to the housing during operation, using simulations and experiments. An air-oil-cooled lubrication system is employed, which enables the airflow to dissipate some of the heat produced. Before conducting the experiment, heat generation and transfer are accurately predicted. The air-oil mixture, formed by pressurized air atomizing oil droplets, is effective in cooling high-temperature regions. A spindle ER16-80SK 24k is integrated with a CNC machine for realtime data acquisition, including parameters such as spindle speed and temperature. The results show significant temperature rises in all bearings, which reach a steady state after an hour of operation. There are noticeable differences in range, from 6.1% to 43.4%, between the experimental and simulated maximum temperatures, indicating possible real factors not considered in the simulation. The environmental impacts of oil particle emission are also discussed, which require proper ventilation management during operations. This research provides valuable insight into the thermal dynamics of CNC milling machine spindles and establishes a solid basis for further research and development in this important field of machining technology. [ABSTRACT FROM AUTHOR]

In the applications of multi-axis computer-numerical-control (CNC) precision machining, one of the important issues in the multi-axis contour following task is to reduce the contour error in the machining process. A popular method to solve this problem is the cross-coupling control (CCC). As the traditional CCC method cannot meet the requirements for tracking accuracy and contour control accuracy for large curvature positions, a novel integrated control strategy of cross-coupling contour error compensation, which consists of an improved real-time contour error estimation algorithm based on arc length parameters, an improved position error compensator (PEC) and a single neuron cross-coupling controller, is proposed. To improve the accuracy of contour error estimation for large curvature trajectories, an improved real-time estimation algorithm of contour error based on arc length parameters is proposed. The method first finds the nearest interpolation point by backtracking method and calculates the backward reference point by using the method based on arc length parameters. Then, the obtained backward reference point is used as the desired command point by arc approximation method to find the estimated value of contour error. Moreover, a single-neuron adaptive cross-coupling controller is designed, which continuously adjusts the weights through a single-neuron learning algorithm to reach the effect of improving the control accuracy. In addition, an improved PEC method is further presented, which improves the tracking accuracy by compensating the tracking error in advance. The feasibility of the proposed integrated control strategy is verified with several non-uniform rational B-spline (NURBS) parametric curve contour following experiments. Moreover, experimental results indicate that the proposed integrated control strategy can significantly improve the tracking and contour control accuracy of biaxial contour following tasks compared with None CCC method and CCC method and has better contour control performance in large curvature positions. [ABSTRACT FROM AUTHOR]

CNC machining is a common manufacturing process which requires significant energy consumption. The first step towards energy savings is to monitor the energy consumption of CNC machines, which are usually equipped with a three-phase electrical power supply. However, existing energy metres are costly for detailed measurements of a machine's energy consumption level. This study developed a low-cost energy measurement device based on the Arduino microcontroller-based platform to monitor energy consumption during CNC machining processes. The product development methodology includes the measurement procedure, calibration, pilot study prior to the commencement of the actual study, and results validation. A field experiment was conducted to validate the design's functionality. Preliminary energy measurements were performed on tool standby, tool changing, spindle rotation speeds, and feed rates. Results showed that the average standby power of the CNC machine is 5.15 kWh, with actual power consumption for the coolant pump motor, spindle motor, and feed motor being 0.43 kW, 0.77 kW, and 0.45 kW, respectively. Energy consumption is increased with spindle rotation speed and feed rate increments. The analysis results demonstrated the product's ability as a cost-effective solution for machining energy monitoring. [ABSTRACT FROM AUTHOR]