An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

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.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

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.

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining

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.

A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining

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.

Compact Wideband Groove Gap Waveguide Bandpass Filters Manufactured with 3D Printing and CNC Milling Techniques

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.

Scalable production of large components by industrial robots and machine tools through segmentation

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.

Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

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.

Experimental Method of Machining Gears with an Involute Profile Using CNC Lathe with Driven Tools

There are many ways of machining gears; the world’s manufacturers of machine tools have patented many methods that allow for the production of gears in an accurate and efficient way. In general, the patented methods require the use of kinematically complex and expensive CNC machine tools. These tools, moreover, the production of the technology itself, including the machining code, require the use of dedicated software. Therefore, it seems justified to strive for the application of kinematically simple and relatively cheap machines in the machining processes so as to increase the universality and availability of new machining methods. This paper presents a method of machining a spur gear with straight teeth with an involute profile using a basic CNC lathe DMG MORI CLX350V4 equipped with driven tools. On the basis of the presented mathematical model, an algorithm was developed to generate a code that controls the machining of cylindrical gears with an involute profile of straight teeth, with the possibility of modifying the transition profile and the tooth root. The machining was experimental, and the gear was made of aluminium AlSi1MgMn using a solid carbide cylindrical cutter. In conclusion, the presented method was found to be very competitive with commercial methods and is able to provide very high quality gears. The accuracy of the machined profile form deviation in the entire processing did not exceed an average value of 10 μm; while assessing the tooth line, the basis average error was 5 μm. Finally, the gear was assessed as manufactured in accuracy class 6. This machining method guarantees very competitive machining cycle times, and thanks to the use of an uncomplicated CNC lathe and universal tools, it provides great flexibility, at the same time giving the possibility of machining gears with arbitrary profiles.

An Efficient Approach to the Five-Axis Flank Milling of Non-Ferrous Spiral Bevel Gears

Five-axis flank milling has been applied in industry as a relatively new method to cut spiral bevel gears (SBGs) for its flexibility, especially for the applications of small batches and repairs. However, it still has critical inferior aspects compared to the traditional manufacturing ways of SBGs: the efficiency is low, and the machining accuracy may not ensure the qualified meshing performances. To improve the efficiency, especially for cutting non-ferrous metals, this work proposes an approach to simultaneously cut the tooth surface and tooth bottom by a filleted cutter with only one pass. Meanwhile, the machining accuracy of the contact area is considered beforehand for the tool path optimization to ensure the meshing performances, which is further confirmed by FEM (finite element method). For the convenience of the FEM, the tooth surface points are calculated with an even distribution, and the calculation process is efficiently implemented with a closed-form solution. Based on the proposed method, the number (or total length) of the tool path is reduced, and the contact area is qualified. Both the simulation and cutting experiment are implemented to validate the proposed method.

Study of Machining of Gears with Regular and Modified Outline Using CNC Machine Tools

Classic methods of machining cylindrical gears, such as hobbing or circumferential chiseling, require the use of expensive special machine tools and dedicated tools, which makes production unprofitable, especially in small and medium series. Today, special attention is paid to the technology of making gears using universal CNC (computer numerical control) machine tools with standard cheap tools. On the basis of the presented mathematical model, a software was developed to generate a code that controls a machine tool for machining cylindrical gears with straight and modified tooth line using the multipass method. Made of steel 16MnCr5, gear wheels with a straight tooth line and with a longitudinally modified convex-convex tooth line were machined on a five-axis CNC milling machine DMG MORI CMX50U, using solid carbide milling cutters (cylindrical and ball end) for processing. The manufactured gears were inspected on a ZEISS coordinate measuring machine, using the software Gear Pro Involute. The conformity of the outline, the tooth line, and the gear pitch were assessed. The side surfaces of the teeth after machining according to the planned strategy were also assessed; the tests were carried out using the optical microscope Alicona Infinite Focus G5 and the contact profilographometer Taylor Hobson, Talysurf 120. The presented method is able to provide a very good quality of machined gears in relation to competing methods. The great advantage of this method is the use of a tool that is not geometrically related to the shape of the machined gear profile, which allows the production of cylindrical gears with a tooth and profile line other than the standard.

Effect of Metal Lathe Waste Addition on the Mechanical and Thermal Properties of Concrete

The amount of steel chips generated by lathes and CNC machines is 1200 million tons per year, and they are difficult to recycle. The effect of adding steel chips without pre-cleaning (covered with production lubricants and cooling oils) on the properties of concrete was investigated. Steel waste was added as a replacement for fine aggregate in the amounts of 5%, 10% and 15% of the cement weight, which correspond with 1.1%, 2.2% and 3.3% mass of all ingredients and 0.33%, 0.66% and 0.99% volume of concrete mix, respectively. The slump cone, air content, pH value, density, compressive strength, tensile strength, tensile splitting strength, elastic modulus, Poisson’s ratio and thermal parameters were tested. It was observed that with the addition of lathe waste, the density decreased, but mechanical properties increased. With the addition of 5%, 10% and 15% metal chips, compressive strength increased by 13.9%, 20.8% and 36.3% respectively compared to plain concrete; flexural strength by 7.1%, 12.7% and 18.2%; and tensile splitting strength by 4.2%, 33.2% and 38.4%. Moreover, it was determined that with addition of steel chips, thermal diffusivity was reduced and specific heat capacity increased. With the addition of 15% metal chips, thermal diffusivity was 25.2% lower than in the reference sample, while specific heat was 23.0% higher. No effect was observed on thermal conductivity.

Analysis of the Impact of Changes in Thermomechanical Properties of Polymer Materials on the Machining Process of Gears

This paper presents an analysis of the impact of modification of thermomechanical properties of polymer materials on the process of gear wheel machining on a CNC machine tool. Polymer materials Tecaflon (PVDA) and polyethylene (PE) were used for processing. The materials underwent thermal modification i.e., annealing. Prepared samples (gear wheel dimensions Ø76.5 × 20 mm) were machined under the same conditions, only changing the feed rate parameter. A CNC milling machine of its own construction was used for machining with a horizontal numerical dividing attachment. The obtained gear wheels were tested using ZEISS GEAR PRO gear analyzes software. Deviations of the involute outline and the tooth line allowed classification of wheels in the 9th grade of accuracy. Machined teeth surfaces were examined for changes in the properties of surface layer, taking into account the influence of polymer material thermal modification on the surface condition. The samples were tested for mechanical properties (tensile strength) and thermomechanical properties (DSC and DMTA). The tests showed positive changes in material strength and significant improvements in PVDA Tecaflon after heat treatment.

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces developed during the process. There are various techniques to determine these cutting forces, such as cutting force prediction or measurement, using dynamometers and other sensor systems. This technique has often been used by numerous researchers in this area. This paper aims to give a review of the different techniques and devices for measuring the forces developed for machining processes, allowing a quick perception of the advantages and limitations of each technique, through the literature research carried out, using recently published works.

Manufacturing data analytics using a virtual factory representation

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

Design and milling manufacture of polyurethane custom contoured cushions for wheelchair users

BACKGROUND

The design of custom contoured cushions manufactured in flexible polyurethane foams is an option to improve positioning and comfort for people with disabilities that spend most of the day seated in the same position. These surfaces increase the contact area between the seat and the user. This fact contributes to minimise the local pressures that can generate problems like decubitus ulcers. The present research aims at establishing development routes for custom cushion production to wheelchair users. This study also contributes to the investigation of Computer Numerical Control (CNC) machining of flexible polyurethane foams.

METHOD

The proposed route to obtain the customised seat began with acquiring the user's contour in adequate posture through plaster cast. To collect the surface geometry, the cast was three-dimensionally scanned and manipulated in CAD/CAM software. CNC milling parameters such as tools, spindle speeds and feed rates to machine flexible polyurethane foams were tested. These parameters were analysed regarding the surface quality. The best parameters were then tested in a customised seat. The possible dimensional changes generated during foam cutting were analysed through 3D scanning. Also, the customised seat pressure and temperature distribution was tested.

RESULTS

The best parameters found for foams with a density of 50kg/cm(3) were high spindle speeds (24000 rpm) and feed rates between 2400-4000mm/min. Those parameters did not generate significant deformities in the machined cushions. The custom contoured cushion satisfactorily increased the contact area between wheelchair and user, as it distributed pressure and heat evenly.

CONCLUSION

Through this study it was possible to define routes for the development and manufacturing of customised seats using direct CNC milling in flexible polyurethane foams. It also showed that custom contoured cushions efficiently distribute pressure and temperature, which is believed to minimise tissue lesions such as pressure ulcers.