Effect of Countersample Coatings on the Friction Behaviour of DC01 Steel Sheets in Bending-under-Tension Friction Tests.

The aim of this article is to provide an analysis of the influence of the type of hard anti-wear coatings on the friction behaviour of DC01 deep-drawing steel sheets. DC01 steel sheets exhibit high formability, and they are widely used in sheet metal forming operations. The tribological properties of the tool surface, especially the coating used, determine the friction conditions in sheet metal forming. In order to carry out the research, this study developed and manufactured a special bending-under-tension (BUT) friction tribometer that models the friction phenomenon on the rounded edges of tools in the deep-drawing process. The rationale for building the tribotester was that there are no commercial tribotesters available that can be used to model the phenomenon of friction on the rounded edges of tools in sheet forming processes. The influence of the type of coating and sheet deformation on the coefficient of friction (CoF) and the change in the topography of the sheet surface were analysed. Countersamples with surfaces prepared using titanium + nitrogen ion implantation, nitrogen ion implantation and electron beam remelting were tested. The tests were carried out in conditions of dry friction and lubrication with oils with different kinematic viscosities. Under dry friction conditions, a clear increase in the CoF value, with the elongation of the samples for all analysed types of countersamples, was observed. Under lubricated conditions, the uncoated countersample showed the most favourable friction conditions. Furthermore, oil with a lower viscosity provided more favourable conditions for reducing the coefficient of friction. Within the entire range of sample elongation, the most favourable conditions for reducing the CoF were provided by uncoated samples and lubrication with S100+ oil. During the friction process, the average roughness decreased as a result of flattening the phenomenon. Under dry friction conditions, the value of the Sa parameter during the BUT test decreased by 20.3-30.2%, depending on the type of countersample. As a result of the friction process, the kurtosis and skewness increased and decreased, respectively, compared to as-received sheet metal.

Thermo-Mechanical Numerical Simulation of Friction Stir Rotation-Assisted Single Point Incremental Forming of Commercially Pure Titanium Sheets.

Single point incremental forming (SPIF) is becoming more and more widely used in the metal industry due to its high production flexibility and the possibility of obtaining larger material deformations than during conventional sheet metal forming processes. This paper presents the results of the numerical modeling of friction stir rotation-assisted SPIF of commercially pure 0.4 mm-thick titanium sheets. The aim of this research was to build a reliable finite element-based thermo-mechanical model of the warm forming process of titanium sheets. Finite element-based simulations were conducted in Abaqus/Explicit software (version 2019). The formability of sheet metal when forming conical cones with a slope angle of 45° was analyzed. The numerical model assumes complex thermal interactions between the forming tool, the sheet metal and the surroundings. The heat generation capability was used to heat generation caused by frictional sliding. Mesh sensitivity analysis showed that a 1 mm mesh provides the best agreement with the experimental results of total forming force (prediction error 3%). It was observed that the higher the size of finite elements (2 mm and 4 mm), the greater the fluctuation of the total forming force. The maximum temperature recorded in the contact zone using the FLIR T400 infrared camera was 157 °C, while the FE-based model predicted this value with an error of 1.3%. The thinning detected by measuring the drawpiece with the ARGUS non-contact strain measuring system and predicted by the FEM model showed a uniform thickness in the drawpiece wall zone. The FE-based model overestimated the minimum and maximum wall thicknesses by 3.7 and 5.9%, respectively.

Analysis of the Microstructure and Mechanical Performance of Resistance Spot-Welding of Ti6Al4V to DP600 Steel Using Copper/Gold Cold-Sprayed Interlayers.

In this article, an attempt was made to join DP600 steel and Ti6Al4V titanium alloy sheets by resistance spot-welding (RSW) using an interlayer in the form of Cu and Au layers fabricated through the cold-spraying process. The welded joints obtained by RSW without an interlayer were also considered. The influence of Cu and Au as an interlayer on the resulting microstructure as well as mechanical properties (shear force and microhardness) of the joints were determined. A typical type of failure of Ti6Al4V/DP600 joints produced without the use of an interlayer is brittle fracture. The microstructure of the resulting joint consisted mainly of the intermetallic phases FeTi and Fe2Ti. The microstructure of the Ti6Al4V/Au/DP600 joint contained the intermetallic phases Ti3Au, TiAu, and TiAu4. The intermetallic phases TiCu and FeCu were found in the microstructure of the Ti6Al4V/Cu/DP600 joint. The maximum tensile/shear stress was 109.46 MPa, which is more than three times higher than for a welded joint fabricated without the use of Cu or Au interlayers. It has been observed that some alloying elements, such as Fe, can lower the martensitic transformation temperature, and some, such as Au, can increase the martensitic transformation temperature.

Improved Synthesis of Cu2O NPs and Ascorbic Acid-Modified Derivatives for Adsorption of Brilliant Cresyl Blue: Surface and Reusability Studies.

This study addresses the critical need for efficient and recyclable photocatalysts for water treatment applications by presenting a novel approach for the synthesis and characterization of copper (I) oxide (Cu2O) nanoparticles modified with ascorbic acid (Cu2O/AA). The motivation for this research stems from the increasing concern about environmental pollution caused by organic pollutants, such as Brilliant Cresyl Blue (BCB), and the necessity for sustainable solutions to mitigate this issue. Through comprehensive characterization techniques including Ultraviolet-Visible spectroscopy (UV-Vis), Fourier Transform Infrared spectroscopy (FTIR), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), zeta potential measurements, and Brunauer-Emmett-Teller (BET) analysis, we demonstrate a significant modification to the electronic structure, enhancing the photocatalytic activity of Cu2O/AA. BET analysis revealed a mesoporous structure with a specific surface area of 2.7247 m2/g for Cu2O/AA, further emphasizing its potential for enhanced catalytic performance. The photocatalytic degradation studies showcased remarkable efficiency improvements, with degradation coefficients of 30.8% and 73.12% for Cu2O NPs and Cu2O/AA NC, respectively, within a 120 min timeframe. Additionally, recyclability experiments indicated sustained efficiency over five consecutive cycles, with both catalysts retaining crystalline integrity. These findings underscore the promising potential of Cu2O/AA nanoparticles as highly efficient and recyclable photocatalysts for the degradation of organic pollutants, offering superior performance compared to pure Cu2O NPs and addressing the pressing need for sustainable water treatment solutions.

Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0.

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool.

Artificial Neural Networks and Experimental Analysis of the Resistance Spot Welding Parameters Effect on the Welded Joint Quality of AISI 304.

The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R2). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1-1 mm, 3.298 kN and 4.1 mm for the case of 0.5-0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5-1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1-1 mm case was the welding current; for the 0.5-0.5 mm case, it was pulse welding; and for the 0.5-1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg-Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures.

Current Trends in Metallic Materials for Body Panels and Structural Members Used in the Automotive Industry.

The development of lightweight and durable materials for car body panels and load-bearing elements in the automotive industry results from the constant desire to reduce fuel consumption without reducing vehicle performance. The investigations mainly concern the use of these alloys in the automotive industry, which is characterised by mass production series. Increasing the share of lightweight metals in the entire structure is part of the effort to reduce fuel consumption and carbon dioxide emissions into the atmosphere. Taking into account environmental sustainability aspects, metal sheets are easier to recycle than composite materials. At the same time, the last decade has seen an increase in work related to the plastic forming of sheets made of non-ferrous metal alloys. This article provides an up-to-date systematic overview of the basic applications of metallic materials in the automotive industry. The article focuses on the four largest groups of metallic materials: steels, aluminium alloys, titanium alloys, and magnesium alloys. The work draws attention to the limitations in the development of individual material groups and potential development trends of materials used for car body panels and other structural components.

Analytical Approach for Forecasting the Load Capacity of the EN AW-7075-T6 Aluminum Alloy Joints Created Using RFSSW Technology.

To ensure the high reliability of aircraft structures, the Refill Friction Stir Spot Welding (RFSSW) process must be characterized by a high load capacity of the welds and a small standard deviation of the load capacity spread. This allows us to obtain uniform functional properties in the connections, ensuring the high quality of the process. This work aims to select the most favorable technological parameters for the welding process of EN AW-7075-T6 Alclad aluminum alloy sheets, which are used for the production of aircraft structures. The best networks were calculated using the Statistica 13.3 program. The obtained results were compared with the results of previous investigations. It has been shown that a model using neural networks allows for the determination of connection parameters with much greater accuracy than the classical model. The maximum error in estimating the load capacity of the connection for the mathematical model was 6.13%, and the standard deviation was 14.51%. In the case of neural networks, the maximum error value did not exceed 1.55%, and the standard deviation was 3.74%. It was shown that, based on the neural model, it is possible to determine the process parameters that ensure the required quality capacity of the process, ensuring a probability of obtaining the required load capacity of the connections amounting to P = 0.999935 with a defect rate of 0.0065%. This possibility is not provided by the classical model due to its large error in estimating the process spread and the high sensitivity of the process input parameters to the output parameters.

The Influence of the Shielding-Gas Flow Rate on the Mechanical Properties of TIG-Welded Butt Joints of Commercially Pure Grade 1 Titanium.

This article proposes as a novelty the differentiation of shielding-gas flow rates from both sides of the tungsten inert gas (TIG)-welded butt joints of commercially pure (CP) grade 1 titanium tubes. Such an approach is aimed at economically reducing the amount of protective gas used in TIG closed butt welding. The effect of the shielding-gas flow rate on the properties of CP grade 1 titanium butt-welded joints made using the tungsten inert gas (TIG)-welding method. Butt-welded joints were made for different values of the shielding-gas flow from the side of the root of the weld. Argon 5.0 was used as the shielding gas in the welding process. As part of the research, the welded joints obtained were analysed using optical and scanning electron microscopy. The microstructural characteristics of the joints were examined using an optical microscope, and the mechanical properties were determined using hardness and tensile tests. It was observed that as the flow of the shielding gas decreases, the hardness of the weld material increases and its brittleness also increases. A similar trend related to the amount of gas flow was also noticeable for the tensile strength of the joints. The increase in the hardness of the weld and the heat-affected zone compared to the base metal is mainly related to the increase in the amount of acicular structure (α' phase). The optimal gas flow rates from the side of the root of weld were found at the values of 12 dm3/min.

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet.

Friction is one of the main phenomena accompanying sheet metal forming methods, affecting the surface quality of products and the formability of the sheet metal. The most basic and cheapest way to reduce friction is to use lubricants, which should ensure the highest lubrication efficiency and at the same time be environmentally friendly. Due to the trend towards sustainable production, vegetable oils have been used in research as an alternative to petroleum-based lubricants. The analysis of friction in sheet metal forming requires an appropriate tribotester simulating the friction conditions in a specific area of the sheet metal being formed. Research has used a special strip drawing tribometer, enabling the determination the value of the coefficient of friction in the blankholder zone in the deep drawing process. Quantitative analysis of the friction phenomenon is necessary at the stage of designing the technological process and selecting technological parameters, including blankholder pressure. This article presents the results of friction testing of 1.0338 (DC04) steel sheets using a strip drawing test. The experimental tests involved pulling a strip of sheet metal between two countersamples with a rounded surface. The tests were carried out on countersamples with different levels of roughness for the range of contact pressures occurring in the blankholder zone in the deep drawing process (1.7-5 MPa). The values of the coefficient of friction determined under dry friction conditions were compared with the results for edible (corn, sunflower and rapeseed) and non-edible (Moringa, Karanja) vegetable lubricants. The tested oils are the most commonly used vegetable-based biolubricants in metal forming operations. Multi-layer artificial neural networks were used to determine the relationship between the value of the contact pressure, the roughness of the countersamples, the oil viscosity and density, and the value of the coefficient of friction. Rapeseed oil provided the best lubrication efficiency during friction testing for all of the tested samples, with an average surface roughness of Sa 0.44-1.34 µm. At the same time, as the roughness of the countersamples increased, a decrease in lubrication efficiency was observed. The lowest root mean squared error value was observed for the MLP-4-8-1 network trained with the quasi-Newton algorithm. Most of the analysed networks with different architectures trained using the various algorithms showed that the kinematic viscosity of the oil was the most important aspect in assessing the friction of the sheets tested. The influence of kinematic viscosity on the value of the coefficient of friction is strongly dependent on the surface roughness of the countersamples.

Tribological Performance of Anti-Wear Coatings on Tools for Forming Aluminium Alloy Sheets Used for Producing Pull-Off Caps.

Ensuring adequate reliability of the production process of packaging closures has made it necessary to study the effect of annealing and varnishing variants on the strength and structural properties of the stock material. As a test material, EN AW-5052-H28 aluminium alloy sheets with a thickness of 0.21 mm were used. The surface treatment of the test material involved varnishing the sheet metal surface using various varnishes and soaking the sheet metal. The coefficient of friction and the abrasion resistance of the coatings were determined using the T-21 ball-and-disc tribotester. The tested sheets were subjected to tribological analysis by the T-05 roller-block tribotester using countersamples made of Caldie and Sverker 21 tool steels. The results of the tests showed differences in mechanical and structural properties depending on the method of sample preparation. Based on the test results, significant differences in the adhesion of anti-wear coatings were found. The results revealed that the most favourable friction conditions are provided by the CrN coating. The (AlTi)N interlayer in the (AlTi)N/(AlCr)N coating adheres to the substrate over the entire tested area and no detachment from its surface was observed, which proves good bonding at the substrate/coating interface. The tested AlTiN/TiAlSiXN coating is characterised by a more homogeneous, compact microstructure compared to the (AlTi)N/(AlCr)N coating.

Cold Drawing of AISI 321 Stainless Steel Thin-Walled Seamless Tubes on a Floating Plug.

The paper presents the results of an analysis of the process of drawing AISI 321 stainless steel thin-walled seamless tubes on a floating plug. The influence of the geometry of dies and plugs, drawing velocity, and lubricants on the possibility of carrying out the pipe drawing process without a loss of strength of the lubricating film and, consequently, disturbance of the forming process and tube cracking, and also on the temperature in the drawing process, the mechanical properties of the tubes drawn, and the microhardness and roughness of the inner and outer surface of the tubes was investigated. The parameters of the drawing tools used were as follows: angle of drawing dies α = 16° and floating plugs with angles of inclination of the conical part of the plug ß = 11.5°, 13°, and 14°. The drawing dies and floating plugs were made of G10 sintered carbide. Drawing speed was varied over the range 1 to 10 m/min. The study used several lubricants. Tubes with dimensions (outer diameter D0, wall thickness g0 before drawing process) D0 = 19 mm, g0 = 1.2 mm and D0 = 18 mm, g0 = 1.2 mm were drawn to produce tubes with dimensions (outer diameter Dk, wall thickness gk after drawing process) Dk = 16 mm, gk = 1.06 mm on a drawbench with the same total elongation, while the diameter and wall thickness were changed. During the process, continuous measurements were made of the drawing force and temperature in the deformation zone and on the tube surface. It was found that the drawing process causes a decrease in the roughness parameters Ra and Rz of the inner surface of the tubes. Moreover, after drawing, an increase of 30-70% was observed in the microhardness of the tube material in relation to the microhardness of the charge material. Based on the test results, it can be concluded that the work of frictional forces is the main direction of optimization of tube drawing on a floating plug process of hard-deforming materials.

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron.

This paper is devoted to the determination of the coefficient of friction (COF) in the drawbead region in metal forming processes. As the test material, AW-5251 aluminium alloys sheets fabricated under various hardening conditions (AW-5251-O, AW-5251-H14, AW-5251-H16 and AW-5251H22) were used. The sheets were tested using a drawbead simulator with different countersample roughness and different orientations of the specimens in relation to the sheet rolling direction. A drawbead simulator was designed to model the friction conditions when the sheet metal passed through the drawbead in sheet metal forming. The experimental tests were carried out under conditions of dry friction and lubrication of the sheet metal surfaces with three lubricants: machine oil, hydraulic oil, and engine oil. Based on the results of the experimental tests, the value of the COF was determined. The Random Forest (RF) machine learning algorithm and artificial neural networks (ANNs) were used to identify the parameters affecting the COF. The R statistical package software version 4.1.0 was used for running the RF model and neural network. The relative importance of the inputs was analysed using 12 different activation functions in ANNs and nine different loss functions in the RF. Based on the experimental tests, it was concluded that the COF for samples cut along the sheet rolling direction was greater than for samples cut in the transverse direction. However, the COF's most relevant input was oil viscosity (0.59), followed by the average counter sample roughness Ra (0.30) and the yield stress Rp0.2 and strength coefficient K (0.05 and 0.06, respectively). The hard sigmoid activation function had the poorest R2 (0.25) and nRMSE (0.30). The ideal run was found after training and testing the RF model (R2 = 0.90 ± 0.028). Ra values greater than 1.1 and Rp0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9-35 for viscosity and 105-190 for Rp0.2, with a gap between 110 and 130 when the oil viscosity was added. The COF was low when the oil viscosity was 9-35, and the Ra was 0.95-1.25. The interaction between K and the other inputs, which produces a relatively limited range of reduced COF values, was the least relevant. The COF was reduced by setting the Rp0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.

The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process.

Wood-based composites are increasingly used in the industry not only because of the shortage of solid wood, but above all because of the better properties, such as high strength and aesthetic appearance compared to wood. Medium-density fiberboard (MDF) is a wood-based composite that is widely used in the furniture industry. In this work, an attempt was made to predict the surface roughness of the machined MDF in the milling process based on acceleration signals from an industrial piezoelectric sensor installed in the cutting zone. The surface roughness parameter Sq was adopted for the evaluation and measurement of surface roughness. The surface roughness prediction was performed using a radial basis function (RBF) artificial neural network (ANN) and a Takagi-Sugeno--Kang (TSK) fuzzy model with subtractive clustering. In the research, as inputs to the ANNs and fuzzy model, the kinematic parameters of the cutting process and selected measures of the acceleration signal were adopted. At the output, the values of the surface roughness parameter Sq were obtained. The results of the experiments show that the surface roughness is influenced not only by the kinematic parameters of the cutting, but also by the vibrations generated during the milling process. Therefore, by combining information on the cutting kinematics parameters and vibration, the accuracy of the surface roughness prediction in the milling process of MDF can be improved. The use of TSK fuzzy modelling based on the subtractive clustering method for integrating the information from many acceleration signal measurements in the examined range of cutting conditions meant the surface roughness was predicted with high accuracy and high reliability. With the help of two tested artificial intelligence tools, it is possible to estimate the surface roughness of the workpiece with only a small error. When using a radial neural network, the root mean square error for estimating the value of the Sq parameter was 0.379 µm, while the estimation error based on fuzzy logic was 0.198 µm. The surface of the sample made with the cutting parameters vc = 76 m/min and vf = 1200 mm/min is characterized by a less concentrated distribution of ordinate densities, compared to the surface of the sample cut with lower feed rates but at the same cutting speed. The most concentrated distribution of ordinate density (for the cutting speed vc = 76 m/min) is characterized by the surface, where the feed rate value was vf = 200 mm/min, with 90% of the material concentrated in the profile height of 28.2 µm. When using an RBF neural network, the RMSE of estimating the value of the Sq parameter was 0.379 µm, while the estimation error based on fuzzy logic was 0.198 µm.

Improving the Surface Integrity of 316L Steel in the Context of Bioimplant Applications.

Bioimplants should meet important surface integrity criteria, with the main goal of the manufacturing process to improve wear and corrosion resistance properties. This requires a special approach at the cutting stage. During this research, the impact of the cutting parameters on improving the surface integrity of AISI 316L steel was evaluated. In this context of bioimplant applications, the mean roughness Sa value was obtained in the range of 0.73-4.19 µm. On the basis of the results obtained, a significant effect was observed of both the cutting speed and the feed rate on changes in the microstructure of the near-surface layer. At a cutting speed of 150 m/min, the average grain size was approximately 31 µm. By increasing the cutting speed to 200 m/min, the average grain size increased to approximately 52 µm. The basic austenitic microstructure of AISI 316L steel with typical precipitation of carbides on the grain boundaries was refined at the near-surface layer after the machining process. Changing the cutting speed determined the hardness of the treated and near-surface layers. The maximum value of hardness is reached at a depth of 20 µm and decreases with the depth of measurement. It was also noted that at a depth of up to 240 µm, the maximum hardness of 270-305 HV1 was reached, hence the height of the machining impact zone can be determined, which is approximately 240 µm for almost all machining conditions.

Microstructure and Mechanical Properties of Solid-State Rotary Friction Welded Inconel 713C and 32CrMo4 Steel Joints Used in a Turbocharger Rotor.

The aim of this work was to determine the effect of selected parameters of friction welding, such as friction pressure and welding speed, on the mechanical properties and microstructure of friction-welded Inconel 713C-32CrMo4 joints. Tensile strength and hardness tests were carried out to determine the mechanical properties of the resulting welded joints. The results of the ultimate tensile strength, hardness, and microstructure were linked to the parameters of the welding process. It was found that the highest tensile strength was 1222 N/mm2. There was a significant increase in the hardness value in the thermo-mechanically affected zone for all samples. However, as the friction pressure increased, the zone with the higher hardness value migrated towards the 32CrMo4 material. In all weld tests, the fracture was found on the 32CrMo4 steel side. A distinct band of carbide formation was observed between the thermo-mechanically affected zone and the Inconel 713C base material.

Friction Behaviour of 6082-T6 Aluminium Alloy Sheets in a Strip Draw Tribological Test.

Aluminium alloy sheets cause many problems in sheet metal forming processes owing to their tendency to gall the surface of the tool. The paper presents a method for the determination of the kinematic friction coefficient of friction pairs. The determination of coefficient of friction (COF) in sheet metal forming requires specialised devices that 'simulate' friction conditions in specific areas of the formed sheet. In this article, the friction behaviour of aluminium alloy sheets was determined using the strip drawing test. The 1-mm-thick 6082 aluminium alloy sheets in T6 temper were used as test material. Different values for nominal pressures (4.38, 6.53, 8.13, 9.47, 10.63, and 11.69 MPa) and different sliding speeds (10 and 20 mm/min.) were considered. The change of friction conditions was also realised with several typical oils (hydraulic oil LHL 32, machine oil LAN 46 and engine oil SAE 5W-40 C3) commonly used in sheet metal forming operations. Friction tests were conducted at room temperature (24 °C). The main tribological mechanisms accompanying friction (adhesion, flattening, ploughing) were identified using a scanning electron microscope (SEM). The influence of the parameters of the friction process on the value of the COF was determined using artificial neural networks. The lowest value of the COF was recorded when lubricating the sheet metal surface with SAE 5W40 C3 engine oil, which is characterised as the most viscous of all tested lubricants. In dry friction conditions, a decreasing trend of the COF with increasing contact pressure was observed. In the whole range of applied contact pressures (4.38-11.69 MPa), the value of the COF during lubrication with SAE 5W40 C3 engine oil was between 0.14 and 0.17 for a sliding speed of 10 mm/min and between 0.13 and 0.16 for a sliding speed of 20 mm/min. The value of the COF during dry friction was between 0.23 and 0.28 for a sliding speed of 10 mm/min and between 0.22 and 0.26 for a sliding speed of 20 mm/min. SEM micrographs revealed that the main friction mechanism of 6082-T6 aluminium alloys sheet in contact with cold-work tool steel flattens surface asperities. The sensitivity analysis of the input parameters on the value of COF revealed that oil viscosity has the greatest impact on the value of the COF, followed by contact pressure and sliding speed.

Minimizing the Main Strains and Thickness Reduction in the Single Point Incremental Forming Process of Polyamide and High-Density Polyethylene Sheets.

Polymeric materials are increasingly used in the automotive industry, aeronautics, medical device industry, etc. due to their advantage of providing good mechanical strength at low weight. The incremental forming process for polymeric materials is gaining increasing importance because of the advantages it offers: relatively complex parts can be produced at minimum cost without the need for complex and expensive dies. Knowing the main strains and especially the thickness reduction is particularly important as it directly contributes to the mechanical strength of the processed parts, including in operation. For the design of experiments, the Taguchi method was chosen, with an L18 orthogonal array obtained by varying the material on two levels (polyamide and polyethylene) and the other three parameters on three levels: punch diameter (6 mm, 8 mm and 10 mm), wall angle (50°, 55° and 60°) and step down (0.5 mm, 0.75 mm and 1 mm). The output parameters were strain in the x direction, strain in the y direction, major strain, minor strain, shear angle and thickness reduction. Two analyses were conducted: signal-to-noise ratio analysis with the smaller-is-better condition and analysis of variance. The optimum values for which the thickness was reduced were the following: wall angle of 50°, punch diameter of 10 mm and step down of 0.75 mm.

Experimental Research and Numerical Modelling of the Cold Forming Process of the Inconel 625 Alloy Sheets Using Flexible Punch.

The paper presents the numerical and experimental results of research aimed at determining the influence of hardness in the range of 50-90 Shore A of layered tools composed of elastomeric materials on the possibility of forming Inconel 625 nickel-based alloy sheets. A stamping die composed of 90MnCrV8 steel (hardness 60HRC) was designed for forming embosses in drawpieces, ensuring various stress states on the cross-section of the formed element. The principle of operating the stamping die was based on the Guerin method. The finite-element-based numerical modelling of the forming process for various configurations of polyurethane inserts was also carried out. The drawpieces obtained through sheet forming were subjected to geometry tests using optical 3D scanning. The results confirmed that, in the case of forming difficult-to-deform Inconel 625 Ni-based alloy sheets, the hardness of the polyurethane inserts significantly affected the geometric quality of the obtained drawpieces. The assumptions determined in numerical simulations were verified in experimental studies. Based on the test results, it was concluded that the selection of polyurethane hardness should be determined by the shape of the formed element. Significant nonuniform sheet metal deformations were also found, which may pose a problem in the process of designing forming tools and the technology of the plastic forming of Inconel 625 Ni-based alloy sheets.

The Effect of the Extrusion Method on Processing and Selected Properties of Poly(3-hydroxybutyric-co-3-hydroxyvaleric Acid)-Based Biocomposites with Flax and Hemp Fibers.

The paper presents a comparative analysis of two extrusion methods of biocomposites with a poly(3-hydroxybutyrate-co-3-hydroxyvalerate acid) (PHBV) matrix filled with flax and hemp fibers in terms of biopolymer production, its processing in the further injection process, and an evaluation of the mechanical and functional properties of the products. Biocomposites containing 15% by weight of the filler were produced using single- and twin-screw extruders. The biocomposites were then processed by injection molding and then, among other things, the pressures in the mold cavity during processing were analyzed. The produced samples were tested by means of the following tests: uniaxial tensile strength, hardness, and impact tensile strength. The biocomposite's microstructure was also analyzed using scanning electron microscopy (SEM), as were the shrinkage and water absorption of the manufactured products. In addition, thermal gravimetric analysis (TGA) and differential scanning calorimetry (DSC) tests were performed. It was found that the extrusion method changed significantly the geometry of the filler fibers and the processing capabilities of the manufactured materials. Significant differences in the mechanical and functional properties of the obtained biocomposite products were also found. On their basis, the advantages and disadvantages of both extrusion methods were discussed. Most of the obtained properties of injection products indicate the choice of single-screw extrusion. The products were characterized by slightly better mechanical properties and lower processing shrinkage. In turn, composites obtained by the screw method were characterized by lower water absorption and lower viscosity of the composite during injection molding.