Search

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

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. more...

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. more...

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] more...

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. more...

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. more...

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. more...

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

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] more...

In CNC machining, G01 codes are widely used to represent the tool path. Directly interpolating these G01 codes is time-consuming and may cause discontinuities. In this paper, we propose a time-spline curve fitting method that combines tool path fitting and feedrate scheduling into a single step for five-axis CNC machining. The input for this method consists of a three-dimensional linear path of the tool tip in the workpiece coordinate system and two-dimensional tool orientations in the machine coordinate system (MCS). The output is a fitted tool path in the MCS represented by a five-dimensional smooth time-parametric B-spline curve, simply referred to as the time-spline curve. The time-spline curve provides not only position information but also kinematic information, including velocity, acceleration, and jerk for each axis, directly derived from the first, second, and third derivatives of the curve. To meet fitting error constraints and axial kinematic constraints, our objective is to find the time-spline curve that is time-optimal. We formulate the optimization problem as a nonlinear optimization model and design a recursive algorithm to solve it. The resulting time-spline curve demonstrates high accuracy and fully utilizes the machine's kinematic capabilities. Along the tool path defined by the time-spline curve, exact interpolation points can be straightforwardly obtained according to the interpolation period. Simulations and experimental results indicate that the proposed method yields a time-optimal time-spline curve with the desired precision and kinematic constraints. [ABSTRACT FROM AUTHOR] more...

The efficiency of machining operation is partly a function of the cutting parameters, such as the cutting speed, depth of cut and cutting feed rate. With an effective cutting performance of any cutting tool, manufacturing industries will remain competitive and hence strive to meet the dynamic service and functional requirements expected of the material. Hence, the purpose of this research is to experimentally perform a comparative analysis of the cutting performances of SiAlON, a ceramic alloy consisting of the elements: silicon, aluminium, oxygen and nitrogen; cubic boron nitride (CBN) and carbide cutting tools, during a titanium machining process. A face milling operation was performed on a computer numerical control (CNC) milling machine from these three different types of cutting tools, for the determination of the cutting forces, cutting temperatures, cutting vibrations produced and the surface roughness, achieved during the machining of titanium (Ti-6Aℓ-4 V) alloy. The response surface methodology was adopted for the possible combination of the cutting parameters for the determination of the practical experimental results of the cutting responses. [ABSTRACT FROM AUTHOR] more...

Efficient and accurate prediction of tool Remaining Useful Life (RUL) is the key to improve product accuracy, improve work efficiency and reduce machining costs. Aiming at the problems of weak tool wear state features, difficult extraction, and low prediction precision and accuracy, this research proposes a CNN-LSTM-PSO tool remaining life prediction method based on multi-channel feature fusion. Firstly, based on computer vision, feature extraction, information fusion technology, the multi-source sensor signals collected during the tool life cycle are effectively processed and analyzed, and a sample data set of spatio-temporal correlation of traffic flow is constructed. Secondly, the sample data set was input into the CNN-LSTM-PSO model, the CNN network obtained the sequence feature vector by extracting the spatial characteristics of traffic flow data, and the feature vector was input into the multi-layer LSTM network to extract the time-dependent features, and the PSO algorithm optimized the hyperparameters in the CNN-LSTM model. The accuracy of tool RUL prediction model and the efficiency of model fitting are further improved. The results show that the CNN-LSTM-PSO model can effectively predict tool wear, with the mean absolute error (MAE) value of 1.0892, the root mean square error (RMSE) value of 1.3520, and the determination coefficient $R^{2}$ value of 0.9961; Through the comparative analysis of ablation experiments, it is found that the method proposed in the research has the highest efficiency in fitting the tool RUL prediction model, the lowest values of MAE value and root mean square error RMSE, and the value of determination coefficient $R^{2}$ is closest to 1, which has certain advantages.The proposed method has reference value and engineering practical significance for the related research of tool wear residual life prediction. more...

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology. [ABSTRACT FROM AUTHOR] more...

Reducing servo response error and further making reduction on contour error is crucial for high-precision computer numerical control (CNC) machine tools. For a permanent magnet synchronous motor (PMSM) servo system, there is always a response lag in feedrate tracking, which would introduce response error into the machining trajectory. Therefore, it is necessary to improve the performance of feedrate planning and interpolation for trajectory path. In this paper, a novel contour error compensation strategy is proposed. Compared with the mainstream methods, the proposed method offers a simplified alternative to existing contour error estimation techniques. Through a three-closed-loop control structure of a PMSM servo system, a response error model is founded. Afterwards, an improved S-model feedrate planning method is introduced according to the servo response error compensation. This predicted error is subsequently compensated in each interpolation cycle, resulting in a reduction of contour error. Finally, simulations and experiments are performed to demonstrate that the contour error can be reduced in both the '∞'-shaped Non-Uniform Rational B-Spline (NURBS) curve path and the butterfly-shaped NURBS curve path using the proposed method. [ABSTRACT FROM AUTHOR] more...

This paper presents for the first time a compact wideband bandpass filter in groove gap waveguide (GGW) technology. The structure is obtained by including metallic pins along the central part of the GGW bottom plate according to an n-order Chebyshev stepped impedance synthesis method. The bandpass response is achieved by combining the high-pass characteristic of the GGW and the low-pass behavior of the metallic pins, which act as impedance inverters. This simple structure together with the rigorous design technique allows for a reduction in the manufacturing complexity for the realization of high-performance filters. These capabilities are verified by designing a fifth-order GGW Chebyshev bandpass filter with a bandwidth BW = 3.7 GHz and return loss RL = 20 dB in the frequency range of the WR-75 standard, and by implementing it using computer numerical control (CNC) machining and three-dimensional (3D) printing techniques. Three prototypes have been manufactured: one using a computer numerical control (CNC) milling machine and two others by means of a stereolithography-based 3D printer and a photopolymer resin. One of the two resin-based prototypes has been metallized from a silver vacuum thermal evaporation deposition technique, while for the other a spray coating system has been used. The three prototypes have shown a good agreement between the measured and simulated S-parameters, with insertion losses better than IL = 1.2 dB. Reduced size and high-performance frequency responses with respect to other GGW bandpass filters were obtained. These wideband GGW filter prototypes could have a great potential for future emerging satellite communications systems. [ABSTRACT FROM AUTHOR] more...

Introduction. The optimization placement problem refereed to virtual localization is studied. This problem is motivated by the need to optimize the production of parts from near-net shape blanks using CNC machines. The known algorithms for solving the virtual localization problem come down to determining the location parameters of the part CAD model inside the point cloud obtained by scanning the workpiece surface. The main disadvantage of such algorithms is the use of criteria that are insensitive to the intersection of the surfaces of the part and the workpiece. In order to prevent such errors in production conditions, it is necessary to involve a human operator in conducting operations based on virtual localization. In this way, the virtual localization problem of complex shape objects is of paramount importance. The purpose of the paper is to propose a new approach for solving the virtual localization problem. Results. A new mathematical model of the virtual localization problem based on the phi-function technique is proposed. We developed a solution strategy that combines algorithm of generating feasible starting points with non-linear optimization procedure. The testing of the proposed approach was carried out for a two-dimensional case. The computational results illustrated with graphical illustrations are provided that show the efficiency of the proposed algorithm. Conclusions. The obtained results show that the use of the phi-functions technique prevents the occurrence of erroneous solutions with the intersection of the workpiece surfaces. An algorithm for solving the problem of virtual localization in a two-dimensional formulation for the case when the part and the workpiece are convex polygons has been developed. For the considered test problems, the solution time did not exceed 2.5 sec, which fully meets the requirements of industrial use. In the future, it is planned to extend the proposed method to the cases when the CAD model of the part has an arbitrary shape and is formed by Boolean operations on geometric primitives. more...

This paper process a self-adaptive hybrid remanufacturing process of TC4 blade based on the additive manufacturing and CNC machining. The requirement of mechanical properties of the blade remanufacturing process is firstly introduced. The process flow of self-adaptive hybrid manufacturing process of TC4 blade based on the selective laser melting and CNC machining is then processed. The mechanical properties of the remanufacturing area, the transition area, and the matrix area of blade are thirdly analyzed by the FEA and the mechanical experiment. The feasibility of the proposed process method is finally verified by a case blade remanufacturing experiment. The results show that the remanufacturing area, the transition area and the matrix area are of good metallurgical bonding effect. The tensile properties of the remanufacturing area and the transition area after the hybrid remanufacturing process have reached the tensile properties standard of TC4. The impact toughness value of the transition area of TC4 blade is higher than that of the remanufacturing area, and the transition area after the hybrid remanufacturing process has better impact resistance. The blade remanufacturing experiment verified that the self-adaptive hybrid remanufacturing process is feasibility to obtain the sufficient mechanical property of the remanufacturing area and the transition area. more...

B-spline curve approximation is widely used for fitting linear toolpaths to improve machining quality and efficiency in CNC machining. For a high-quality smoothing method, the control of both approximation error and curve curvature needs to be taken into account. To reduce the maximum curve curvature while meeting the precision requirements, a B-spline approximation scheme with tool tip position adjustment is proposed in this paper. Toolpaths are first divided into several subdivisions according to the discrete curvature of each tool tip point. For subdivisions that need adjustment, tool tip positions are adjusted to minimize the maximum discrete curvature. An existing approximation method named energy-term-incorporated progressive and iterative approximation for least square fitting (ELSPIA) is selected and improved to fit the adjusted toolpaths and lower the chord errors. For improving the numerical stability of the ELSPIA method, the way of searching appropriate foot point parameters is discussed in this paper. Deviation expansion factors of tooltip points are also defined to make the ELSPIA method suitable for fitting the adjusted toolpaths. Both simulations and experimental studies are conducted to prove that the proposed method can significantly decrease the maximum curvature of fitted B-spline curves and improve the machining efficiency without exceeding the tolerance. For example, experimental results show that for the tested butterfly and rabbit toolpaths, the proposed method can improve the machining efficiency by 2.61% and 2.37%, compared with the ELSPIA method without tool tip position adjustment. [ABSTRACT FROM AUTHOR] more...

Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome. more...

Industry 4.0 has changed the ways in which Small to Medium (SME’s) and Large Enterprise (LE’s) manufacturers and businesses operate. Artificial Intelligence (AI), Bigdata, Edge Computing, Cloud Computing, Internet of Everything (IoE), Fifth Generation (5G) and Information Communication Technology (ICT) allow processes to be optimized, controlled, and monitored in close real-time. These enabling technologies allow manufacturing facilities to collect enormous amounts of data from process lines, such as real-time measurement data, machinery state of health, and cycle time, to accurately plan and report the state of both processes, machinery, and final products. In this work, the research programme focuses on the collected data from process input variables that can be monitored to ensure process outputs and final components conform to design specifications. Current methods of analyzing data, especially in aerospace manufacturing environments, require engineers or process operators with a precise and high skill set to be able to map the results into the appropriate chart and interpret these results. Furthermore, the quantity of data analysis required to monitor process inputs in real-time renders conventional analysis techniques unfeasible. Current development using AI has shown the potential and the capability to detect trends in large data sets using machine and deep learning. It also enables a more precise and automated analysis without human intervention. This paper focuses on determining the feasibility of using a deep neural network (i.e., deep neural networks -DNN) to predict process outputs based on process inputs. A simplified example is presented using data collected from firing a ‘statapult’ with varied configurations, training a deep neural network and predicting future results based on process inputs. Initial results are presented, and the results show promising estimating and prediction capabilities. more...

In machining a fixed depth of cut at a constant feedrate to generate a desired curvilinear shape, substantial variations in chip load can occur whenever the smallest concave radius of curvature is comparable to the tool radius. These chip load variations may result in a poor quality of the machined surface or premature tool wear. Conversely, attempting to suppress chip load variations by modulating the feedrate may incur high rates of feed acceleration, that may tax the machine drive systems or induce large contour errors. To address these conflicting influences, the feasibility of minimizing variations in chip load through real-time spindle speed modulation is examined herein. A second-order model is employed to determine the tool angular speeds and accelerations that are required to achieve a specified constant chip load for a given depth of cut along a desired part shape defined by a parametric curve, using a constant feedrate for a tool with a given radius and number of cutting edges. For a spindle driven by a DC motor, the motor voltage variation required to generate the modulated spindle speed is also determined. These analyses facilitate an a priori assessment of the part geometries and process parameters for which spindle speed modulation is a viable approach to the suppression of chip load variations. more...

Purpose: This article focuses on a comparative analysis of the technology of additive shaping and multi-axis CNC machining. The authors examine the impact of 3D printing assumptions and CNC machining conditions on the strength of the selected PET material used to produce machine elements on the example of a shaft-type element. The purpose of the study is to identify a better production method. Design/methodology/approach: The analysis was carried out by producing six samples of different diameters and lengths from the same thermoplastic material (ethylene terephthalate) by both 3D printing (FDM) and CNC machining. The resulting samples were subjected to a static compression test, for which a universal testing machine by Zwick & Roell 100 kN was used. The following factors during the production of elements were compared: the difficulty of preparing the project, the time of execution, the cost of execution, the accuracy of the execution and the properties of the elements made. Findings: Elements made by CNC machining have higher compressive strength and yield strength, as well as lower relative expansion and relative shortening. Those produced by CNC machining are created as a monolith (semi-finished product), and the printed elements are incrementally shaped layer by layer. During the strength test, the spaces between the layers decrease, which in turn causes an increase in relative shortening and a decrease in strength properties. Research limitations/implications: Further research is planned on the analysis of manufacturing technology using incremental shaping technology (e.g. change of filling density, change of filling type, change of material) compared to CNC machining. Practical implications: In the conducted tests, a universal method was used, which can be translated into a comparative study of elements made of other materials. Originality/value: The research carried out allowed for the initial assessment of the use of PET material for the production of machine elements through 3D printing and CNC machining. [ABSTRACT FROM AUTHOR] more...

In a CNC machining operation, key performance indicators (KPIs) of process, such as machining time, quality, and energy consumption, vary with cutting parameters. This paper explains a methodology for building physics-guided data-driven models for predicting these process KPIs in CNC machining operations from the planning, machining, and quality data. These physics-guided data-driven models are developed by combining data-driven and physics-based models of machining operations. Using hybrid physics-ML method, predictive modelling of energy consumption and surface roughness in CNC milling operation is also explained by conducting experiments. Finally, accuracies obtained by these models are compared with respective physics-based and data-driven models. [ABSTRACT FROM AUTHOR] more...

Industry 4.0 promotes data-driven optimization of computerized numerical controlled (CNC) machining processes in industrial environments. In this paper, an industry-oriented data management architecture is presented, which allows automatic relation of in-process machine and sensor feedback to CAD/CAM process meta information along the product development cycle. The proposed system utilizes state-of-the-art feature-based machining technologies and open communication protocols such as OPC UA. Thereby, it ensures seamless integration into existing industrial CNC machining networks. The proposed system is demonstrated using a milling use-case with a CNC machining center and a coordinate measurement machine. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

As recent advances in additive manufacturing (AM) technology has grown rapidly over the past decades, a wide range of applications in the various field has been proposed. Especially, large-scale 3D printing technology has emerged as one of the most innovative alternatives to the traditional manufacturing process due to its simple, fast, and cost-efficient features. In this article, we proposed a large-scale hybrid manufacturing equipment of the three-dimensional (3D) printer based on fused deposition modeling (FDM) incorporated with CNC machining. Our manufacturing system is designed to produce a product having dimensions up to 3,000 mm × 4,000 mm × 1,200 mm with an extruder having an extrusion rate of 30 mm·s−1 and nozzle area of 15 mm2 × 15 mm2. We also optimized the operating conditions of our equipment including the shape of the nozzle, the temperature of the heater, and the RPM of the machining tool. The performance of the equipment was confirmed by pilot production via sand-casting. We expect that our hybrid manufacturing system can be widely used to produce various shapes of large-scale mold as a cost-effective alternative to conventional methods in the manufacturing process. more...

With the in-depth integration of intelligent manufacturing and energy-efficient manufacturing, energy consumption should be taken as an important indicator to meet the development philosophy of low-carbon manufacturing while continuously pursuing manufacturing efficiency. The intelligent estimation of energy consumption for machined parts is the basis for establishing an energy-efficient intelligent manufacturing system. STEP-NC is one of the practical schemas to implement an intelligent manufacturing mode in the CNC machining field. Hence, this paper proposed an estimation methodology of energy consumption based on the STEP-NC program to realize the estimation of the staged and overall energy consumption for parts. Firstly, the influencing factors of energy consumption are analyzed in detail and the data model of energy consumption is extended to the STEP-NC standard accordingly. Secondly, the energy consumption estimation methodology based on the machining feature was constructed, and the mapping relationship between STEP-NC program and the estimation method was established. Finally, the energy consumption estimation framework with the STEP-NC program as input is developed while the validity of the methodology is verified by practical machining experiments. By comprehensive analysis, the methodology shows promising results in efficiency and application prospect, which lays a foundation for further intelligent energy-efficient research. [ABSTRACT FROM AUTHOR] more...

A modern, environmentally friendly urban lifestyle requires paying attention to landscaping and green areas. The scarcity of free land in cities and the high price of land require the combination of greenery with buildings—both vertically and horizontally. The developed green technology for construction brings together computer numerical control (CNC) processing of supporting structures and prefabricated solid planting blocks made of concrete composite. The timber structures are fixed together using traditional carpentry joints. The details, which will be manufactured in the factory using CNC processing at a controlled temperature and humidity corresponding to indoor conditions, can be easily assembled on the construction site. The high bending strength but good elasticity and connections of carpentry joints endow the structure with good properties in a non-controllable environment. By combining CNC-processed wooden structures with concrete technology as substrate composites, labor-intensive manual work in landscaping and gardening will be reduced in the future. The novel material-hardening substrate composite material uses only the residues as the raw materials. [ABSTRACT FROM AUTHOR] more...

Additive manufacturing is defined as a process based on the superposition of layers of materials in order to obtain 3D parts; however, the process does not allow achieve the adequate and necessary surface finishing. In addition, with the development of new materials with superior properties, some of them acquire high hardness and strength, consequently decreasing their ability to be machined. To overcome this shortcoming, a new technology assembling additive and subtractive processes, was developed and implemented. In this process, the additive methods are integrated into a single machine with subtractive processes, often called hybrid manufacturing. The additive manufacturing process is used to produce the part with high efficiency and flexibility, whilst machining is then triggered to give a good surface finishing and dimensional accuracy. With this, and without the need to transport the part from one machine to another, the manufacturing time of the part is reduced, as well as the production costs, since the waste of material is minimized, with the additive–subtractive integration. This work aimed to carry out an extensive literature review regarding additive manufacturing methods, such as binder blasting, directed energy deposition, material extrusion, material jetting, powder bed fusion, sheet laminating and vat polymerization, as well as machining processes, studying the additive-subtractive integration, in order to analyze recent developments in this area, the techniques used, and the results obtained. To perform this review, ScienceDirect, Web of Knowledge and Google Scholar were used as the main source of information because they are powerful search engines in science information. Specialized books have been also used, as well as several websites. The main keywords used in searching information were: "CNC machining", "hybrid machining", "hybrid manufacturing", "additive manufacturing", "high-speed machining" and "post-processing". The conjunction of these keywords was crucial to filter the huge information currently available about additive manufacturing. The search was mainly focused on publications of the current century. The work intends to provide structured information on the research carried out about each one of the two considered processes (additive manufacturing and machining), and on how these developments can be taken into consideration in studies about hybrid machining, helping researchers to increase their knowledge in this field in a faster way. An outlook about the integration of these processes is also performed. Additionally, a SWOT analysis is also provided for additive manufacturing, machining and hybrid manufacturing processes, observing the aspects inherent to these technologies. [ABSTRACT FROM AUTHOR] more...

In this paper, we give a method for positioning a toroidal tool on a tensor product surface with two points of contact. By firing vertical rays and circular rays from the tensor product surface to the toroidal cutting surface, we can find these two points of contact by solving only quadratic equations, whereas previous methods typically solve transcendental equations using iterative methods. The new method is faster than the methods presented in literature and is independent of the method used to define the part surface. In addition to general applicability, test results on three sample surfaces are presented. [ABSTRACT FROM AUTHOR] more...

The production of large components currently requires cost-intensive special machine tools with large workspaces. The corresponding process chains are usually sequential and hard to scale. Furthermore, large components are usually manufactured in small batches; consequently, the planning effort has a significant share in the manufacturing costs. This paper presents a novel approach for manufacturing large components by industrial robots and machine tools through segmented manufacturing. This leads to a decoupling of component size and necessary workspace and enables a new type of flexible and scalable manufacturing system. The presented solution is based on the automatic segmentation of the CAD model of the component into segments, which are provided with predefined connection elements. The proposed segmentation strategy divides the part into segments whose structural design is adapted to the capabilities (workspace, axis configuration, etc.) of the field components available on the shopfloor. The capabilities are provided by specific information models containing a self-description. The process planning step of each segment is automated by utilizing the similarity of the segments and the self-description of the corresponding field component. The result is a transformation of a batch size one production into an automated quasi-serial production of the segments. To generate the final component geometry, the individual segments are mounted and joined by robot-guided Direct Energy Deposition. The final surface finish is achieved by post-processing using a mobile machine tool coupled to the component. The entire approach is demonstrated along the process chain for manufacturing a forming tool. more...

In the last decades, data-driven methods have gained great popularity in the industry, supported by state-of-the-art advancements in machine learning. These methods require a large quantity of labeled data, which is difficult to obtain and mostly costly and challenging. To address these challenges, researchers have turned their attention to unsupervised and few-shot learning methods, which produced encouraging results, particularly in the areas of computer vision and natural language processing. With the lack of pretrained models, time series feature learning is still considered as an open area of research. This paper presents an efficient two-stage feature learning approach for anomaly detection in machine processes, based on a prototype few-shot learning technique that requires a limited number of labeled samples. The work is evaluated on a real-world scenario using the publicly available CNC Machining dataset. The proposed method outperforms the conventional prototypical network and the feature analysis shows a high generalization ability achieving an F1-score of 90.3%. The comparison with handcrafted features proves the robustness of the deep features and their invariance to data shifts across machines and time periods, which makes it a reliable method for sensory industrial applications. [ABSTRACT FROM AUTHOR] more...