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.

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.

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.

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.

Geometric Measurements on a CNC Machining Device as an Element of Closed Door Technology.

The article discusses the quality testing of a measuring system consisting of a CNC machine with measuring probes. The research was conducted in a broader context regarding the implementation of the closed door technology, i.e., production without human intervention, in an aviation plant manufacturing aircraft gearbox systems. This technology may involve automated measuring operations performed in machining centers, and not in measuring laboratories, provided that the quality of the measurements is appropriate. The aim of the study was to investigate whether the CNC machining device can be used to measure the geometric features of aircraft gearbox housing. For this purpose, measurement experiments were carried out with the use of three different probes. Measurements were carried out using four sequences of increasing complexity, so that, after error analysis, it was possible to find the causes of possible irregularities. A reference ring with known dimensions and position in the working space of the machine was used for the measurements performed as part of the assessment of the measurement system. The quality of the measurements was evaluated with the use of repeatability and reproducibility testing and statistical process control. The analysis results showed that the tested measurement system ensures adequate accuracy and repeatability, and the measurement process is characterized with adequate efficiency in relation to the manufacturing tolerance of the components produced using the machine. Thus, it was proven that the measurement process can be carried out on a machining device, which enables its integration into the closed door technology.

A personalised prosthetic liner with embedded sensor technology: a case study.

BACKGROUND: Numerous sensing techniques have been investigated in an effort to monitor the main parameters influencing the residual limb/prosthesis interface, fundamental to the optimum design of prosthetic socket solutions. Sensing integration within sockets is notoriously complex and can cause user discomfort. A personalised prosthetic liner with embedded sensors could offer a solution. However, to allow for a functional and comfortable instrumented liner, highly customised designs are needed. The aim of this paper is to presents a novel approach to manufacture fully personalised liners using scanned three-dimensional image data of the patient's residual limb, combined with designs that allow for sensor integration. To demonstrate the feasibility of the proposed approach, a personalised liner with embedded temperature and humidity sensors was realised and tested on a transtibial amputee, presented here as a case study. METHODS: The residual limb of a below knee amputee was first scanned and a three-dimensional digital image created. The output was used to produce a personalised prosthesis. The liner was manufactured using a cryogenic Computer Numeric Control (CNC) machining approach. This method enables fast, direct and precise manufacture of soft elastomer products. Twelve Hygrochron Data Loggers, able to measure both temperature and humidity, were embedded in specific liner locations, ensuring direct sensor-skin contact. The sensor locations were machined directly into the liner, during the manufacturing process. The sensors outputs were assessed on the below amputee who took part in the study, during resting (50 min) and walking activities (30 min). To better describe the relative thermal properties of new liner, the same tests were repeated with the amputee wearing his existing liner. Quantitative comparisons of the thermal properties of the new liner solution with that currently used in clinical practice are, therefore, reported. RESULTS: The liner machining process took approximately 4 h. Fifteen minutes after donning the prosthesis, the skin temperature reached a plateau. Physical activity rapidly increased residuum skin temperatures, while cessation of activity caused a moderate decrease. Humidity increased throughout the observation period. In addition, the new liner showed better thermal properties with respect to the current liner solution (4% reduction in skin temperature). CONCLUSIONS: This work describes a personalised liner solution, with embedded temperature and humidity sensors, developed through an innovative approach. This new method allows for a range of sensors to be smoothly embedded into a liner, which is capable of measuring changes in intra-socket microclimate conditions, resulting in the design of advanced socket solutions personalised specifically for individual requirements. In future, this method will not only provide a personalised liner but will also enable dynamic assessment of how a residual limb behaves within the socket during daily activities.

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.

Empirical analysis of dust and noise pollutants produced in the wood-CNC machining process.

In the manufacturing processes, consideration of sustainability is of particular importance. The current study is concerned with the influences of changing the process variables on the reduction of pollutions in the wood-CNC machining operation. Noise and dust are the studied pollutants in the present research work. Process variables include feed rate, spindle speed, step-over, and depth of cut, and the aim is to predict the behavior of aforementioned pollutants variations in the current process. The amounts of these harmful factors are measured based on existing standards. In order to analyze the findings, adaptive neuro-fuzzy inference system (ANFIS) and regression analysis methods have been employed, separately. The effects of process parameters on response variables have been comprehensively studied. The research findings demonstrated that for the present problem, ANFIS outcomes are more accurate. According to the mean absolute error (MAE) criterion, the prediction errors of ANFIS for noise and dust factors were computed to, in turn, 0.50 and 14.89. Meanwhile, the error values for prediction of noise and dust responses using regression analysis were calculated as 1.54 and 34.62, respectively.

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.

Dimensional Characterization and Hybrid Manufacturing of Copper Parts Obtained by Atomic Diffusion Additive Manufacturing, and CNC Machining.

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.

A Multi-Scale Tool Orientation Generation Method for Freeform Surface Machining with Bull-Nose Tool.

Free-form surface parts are widely used in industries, and they consist of intricate 3D surfaces such as molds, impellers, and turbine blades that possess complex geometrical contours and demand high precision. Proper tool orientation is crucial for ensuring the efficiency and accuracy of five-axis computer numerical control (CNC) machining. Multi-scale methods have received much attention and have been widely used in various fields. They have been proven to be instrumental and can obtain fruitful outcomes. Ongoing research on multi-scale tool orientation generation methods, which aim to acquire tool orientations that satisfy both macro- and micro-scale requirements, is significantly important for improving the machining quality of workpiece surfaces. This paper proposes a multi-scale tool orientation generation method that considers both the machining strip width and roughness scales. This method also ensures a smooth tool orientation and avoids interference in the machining process. First, the correlation between the tool orientation and rotational axis is analyzed, and feasible area calculation and tool orientation adjustment methods are introduced. Then, the paper introduces the calculation method for machining strip widths on the macro-scale and the roughness calculation method on the micro-scale. Besides, tool orientation adjustment methods for both scales are proposed. Next, a multi-scale tool orientation generation method is developed to generate tool orientations that meet the macro- and micro-scale requirements. Finally, to verify the effectiveness of the proposed multi-scale tool orientation generation method, it is applied to the machining of a free-form surface. Experimental verification results have shown that the tool orientation generated by the proposed method can obtain the expected machining strip width and roughness, meeting both macro- and micro-scale requirements. Therefore, this method has significant potential for engineering applications.

Ultrasonic-Assisted Surface Finishing of STAVAX Mold Steel Using Lab-Made Polishing Balls on a 5-Axis CNC Machining Center.

The inconvenience of conventional wool ball polishing is that the surface finishing process should be equipped with a slurry container. The main objective of this research is to develop an ultrasonic-assisted surface finishing process for STAVAX mold steel on a 5-axis CNC machining center, by using new lab-made rubber polishing balls containing the abrasive aluminum oxide instead of the traditional wool ball polishing. In total, five types (type A to type E) of new rubber-matrixed polishing balls with a composite of nitrile butadiene rubber (NBR), an abrasive of aluminum oxide, and an additive of silicon dioxide have been developed. The performance of the composites with different grain sizes (0.05 µm to 3 µm) and concentrations of the abrasive of aluminum oxide have been investigated. The effects of multiple polishing passes on the surface roughness improvement for the lab-made polishing balls have also been investigated in this study. A surface roughness of Ra 0.027 µm on average was achieved by using the multiple polishing process of E-C-B-A. The volumetric wear of the lab-made polishing balls, using ultrasonic vibration-assisted polishing, can be improved from about 12.64% (type A) to 65.48% (type E) compared with the non-vibration-assisted polishing. The suitable combination of the ultrasonic vibration-assisted polishing parameters were an amplitude of 10 µm, a frequency of 23 kHz, a spindle speed of 5000 rpm, a feed rate of 60 mm/min, a stepover of 20 µm, a penetration depth of 180 µm, and a polishing pass of E-C-B-A, based on the experimental results. The surface roughness improvement on a test carrier with a saddle surface has also been presented by using the ultrasonic vibration-assisted polishing with the lab-made polishing balls.

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.

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.

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.

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.

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.