Dimensional Accuracy Research Articles

Experimental investigation of FDM manufacturing of 316 l stainless steel

Continuous research in the field of metal additive manufacturing has led to the need for constant improvement of manufacturing parameters especially in the case of FDM (Fused Deposition Modeling) manufacturing. In recent years, the main directions outlined for productivity and quality improvement were related to higher printing speed and the use of ironing-type processes. This article aims to study the manufacturing parameters of the dimensional accuracy and surface quality of FDM-manufactured 316L stainless steel. The degree of novelty is given by the application of the ironing process for the green part. A full factorial 33 experimental design was designed for this study, in which the factors studied were ironing angle, ironing speed, and layer spacing during ironing. The dimensional accuracy and surface roughness were analyzed by means of deviation measurement from CAD to the green part and final part after the sintering process. Using the design of experiments offers the possibility of applying the analysis of variance (ANOVA) which provides information about the degree of influence of each of the studied factors. The results obtained for the dimensional accuracy showed that the ironing direction had the biggest influence on the Z-axis shrinkage. Overall, approximately 6% shrinkage in the Z and Y directions was obtained while in the X directions, the shrinkage percentage was around 20%. Surface roughness showed an improvement with higher ironing speeds for the green part while for the sintered part the most significant factor was ironing spacing.

Development of an Additively Manufactured Stationary Diffusion System for a Research Aeroengine Centrifugal Compressor

Abstract This paper introduces the next generation of the Centrifugal Stage for Aerodynamic Research (CSTAR) facility at the Purdue University Compressor Research Lab. The research centrifugal compressor is designed with an additively manufactured stationary diffusion system which comprises a vaned diffuser, a turn-to-axial bend, and deswirler vanes. The dimensional accuracy and surface finish of the diffusion system has allowed for rapid prototyping of several iterative diffusion system designs and has allowed for configuration of in situ pressure and temperature measurements to experimentally evaluate the aerodynamics and performance of centrifugal compressor diffusion systems. The diffusion system is manufactured from a commercially available stereolithography (SLA) resin, and the design of the parts is adapted to the constraints imposed by current technological and material limits of resin 3D printing. The implementation of additive manufacturing in the prototyping of the diffusion system has allowed for decreased cost and lead time while allowing rapid turnaround to generate experimental data. Performance data have verified the repeatability and temperature independence of the additively manufactured diffusion system through several design iterations. A survey of candidate materials for additively manufacturing these parts is also presented, and the tradeoffs of their material properties are discussed.

Effect of tool orientation on surface roughness and dimensional accuracy in ball end milling of thin-walled blades

AbstractIn the aerospace industry, high-precision components like impellers and blisks present considerable challenges in machining due to their intricate geometries and the need for reliable performance. Blades, often classified as thin-walled parts with complex, free-form surfaces, are crucial in ensuring the efficiency and safety of aircraft engines. Their geometries and materials require strict control over cutting parameters, such as tool path and orientation, to avoid deformation and maintain surface integrity. This study investigates the impact of tilt angle variation during ball end milling of Ti6Al4V thin-walled parts. Four different tilt angles (15°, 30°, 45°, and 60°) were analysed to evaluate their effect on dimensional accuracy and surface roughness. The results show that tilt angle significantly influences surface quality and dimensional precision. A tilt angle of 30° achieved the best balance between surface finish and dimensional tolerances, with flatness values ranging from 0.038 mm at 30° to 0.101 mm at 60°, representing a 152.5% increase in flatness. The findings provide practical guidelines for optimizing ball end milling of thin-walled components, emphasizing the importance of tool orientation in reducing deformation and enhancing surface quality.

Fundamentals of simultaneous machining and coating (SMaC) through combination of extreme high-speed laser material deposition (EHLA) and turning

Simultaneous machining and coating (SMaC) is a novel hybrid technology developed by the Fraunhofer Institute for Laser Technology ILT that combines additive manufacturing through extreme high-speed laser material deposition (EHLA) with a simultaneously engaged turning process. In the present work, the parallelization of the two subprocesses is successfully demonstrated. For the first time, systematic investigations into the influence of residual heat from the deposition process on the machining process and the properties of the resulting coating, respectively, are conducted. The study specifically examines geometric deviations, surface roughness, and tool wear. One of the main parameters affecting the residual heat introduced into the turning process is the distance between the tool center points of the EHLA deposition head and the turning tool Δz. This parameter is identified as a primary factor influencing the dimensional accuracy of the coating geometry. The results show that SMaC not only offers potential for increasing the productivity of the process chain but also has a positive effect on the service life of the cutting tools involved, potentially improving the workability of hard coating materials. Improvements in terms of the attainable surface roughness are also observed. These investigations provide a basis for research into further aspects of the SMaC process, such as adaptive tool path control methods to enhance dimensional accuracy and the influence of induced compressive stresses in processing highly brittle coating materials.

Microstructure formation mechanisms and property regulation methods during ceramic additive manufacturing

Anisotropic surface lamellar defects, dimensional shrinkage, and bending strength are important scientific issues that constrain vat photopolymerization additive manufacturing of biological, electronic, and core ceramics with high precision and complex structures. Through the single-factor experiment of photoinitiator concentration, average particle size, volume fraction, and refractive index constant in ceramic-resin slurry, this work studies the photopolymerization characteristics and profile curves, surface lamellar defects, and crosslinking density, verifies the reliability of the photopolymerization profile equations affected by the slurry composition, and acquires the anisotropic property formation mechanisms and control methods. The single-point scanning photopolymerization profile shape is parabolic, resulting in x-, y-, and z-direction lamellar defects before and after sintering (1550 °C), with no obvious weakening. After the quantitative description of the lamellar defects, the average surface roughnesses before and after sintering of the green body are 6.82 and 4.92 μm, respectively. Additionally, the laser energy attenuation along the penetration direction induces a gradient distribution of crosslinking density in the photopolymerization profile region. The crosslinking density gradient distribution of the decay from the slurry surface toward the depth direction is influenced by the slurry component concentration, leading to a gradient of cured powder concentration, which induces anisotropic behaviors. Through the crosslinking density controlling, the average bending strength in x(y) and z directions is increased by 63.19 % (46 %), the difference in anisotropic strength is reduced by 22.55 %, and the dimensional shrinkage difference of x and z directions is reduced by 48.32 %. This provides a theoretical and experimental research basis for ceramic vat photopolymerization with high dimensional accuracy, excellent performance, and morphology control.

Evaluation of lubrication mechanism of hybrid nanolubricants in turning hardened AISI D6 tool steel

Turning of hardened tool steels has posed a significant challenge for machining professionals. Usually, hardened tool steels are machined under dry with ceramic or PCBN inserts. However, dry machining imposes a condition where high cutting temperatures are developed, which becomes prohibitive when the part's geometric and dimensional accuracy is required. On the other hand, using mineral oil-based cutting fluid has encountered increasing restrictions because of its unsustainable nature. In this context, developing new sustainable lubri-cooling techniques, such as using vegetable oils blended with nanoparticles, could be an appropriate alternative. Thus, the main objective of this study was to investigate the performance of three different vegetable oil-based nanolubricants blended with three different nanoparticles (CuO, a-C:H, and CuO + a-C:H), applied under MQL when machining a quenched and tempered AISI D6 tool steel. For this purpose, facing turning trials were performed using solid PCBN inserts with the following cutting parameters: Vc = 100 m/min, ap = 0.3 mm, and f = 0.1 mm/rev. For comparison, facing turning tests were performed under dry and pure vegetable oil (without nanoparticles) and applied under MQL. Output variables included average surface roughness (Ra), flank wear (VBC), wear mechanisms of the cutting edge, chip shape, and chip compression ratio (Rc). The results showed that the vegetable oil-based nanolubricants applied under MQL improved the tribological conditions in the chip-tool and workpiece-tool interfaces, mainly in the case of CuO + a-C:H nanolubricant. In this case, it enhanced the lubricating action of the vegetable oil, decreasing cutting tool wear probably because of the combination of rolling mechanism, - provided by the CuO nanoparticles, and the formation of a protective film, supplied by the a-C:H nanoparticles.

Acoustics and vibration based milling diagnostics using supervised machine learning approaches

In industrial machining processes, tool failures may result in losses in surface and dimensional accuracy of a finished part, or possible damage to both the work piece and the machine. Consequently, tool condition monitoring has become essential to achieve high-quality machining as well as cost-effective production. Moreover, cutting tool degradation may vary considerably under different operation conditions and materials behaviour. Therefore, real time identification of the tool state during machining is critical. In this study the acoustic and vibration responses of the cutting tool and of the workpiece material are measured with the help of a microphone and a piezoelectric accelerometer. Many milling experiments were conducted for different conditions and tool failures, from which features were calculated in time and frequency domains and then split into two groups: 80% for the machine learning model training, 20% for the test phase. Then several supervised machine learning approaches have been applied and compared. Results of our study are implemented in a real tool wear predictive maintenance framework.

Hierarchical Control in Mechatronic Technological Systems

The topic of hierarchical control of technological machines is one of the most relevant in mechanical engineering technology. The most difficult issue in this area is the organization of interactions between different control levels, on the one hand, and the choice of automatic control methods for each of these control levels (control by deviation, control by disturbance, mixed control, etc.), on the other. In this article, in relation to machining technology, a method and corresponding device are proposed that make it possible to implement the control of cutting force parameters (axial cutting force and cutting torque) in an automatic control system for the deviation of cutting torque by changing the axial cutting force (lower level of control). The lower-level control ensures the required quality of the surface layer (surface integrity) of the machined parts. At the same time, the required dimensional accuracy of parts is ensured at the upper level of control, which is implemented by the CNC system of the machine. At the upper level, automatic control is carried out based on the deviation of the kinematic parameters of the movement of the working parts of the CNC machine (acceleration, speed, displacement). Control switching from upper to lower level and back is carried out without using a spindle linear axial movement sensor. Instead of this expensive sensor, a limit switch (a closed and opened pair of contacts) is used, which fixes the lowest axial position of the spindle (and cutting tool). Based on the signal of closing the specified contacts of the limit switch, a transition from the lower control level to the upper one is carried out. Thus, the upper-level system operates only when these contacts are closed, and the lower-level system operates only when they are open. In relation to the upper-level system, the lower-level control system implements the control “by disturbance” principle, also known in control theory as the “disturbance compensation principle”.

METROLOGICAL ANALYSIS OF THE IMPACT OF THE ARRANGEMENT OF MODELS ON THE 3D PRINTER PLATFORM ON THE DIMENSIONAL ACCURACY OF MODELS MANUFACTURED USING PHOTO-CURING TECHNOLOGY - PJM

The paper presents the results of metrological tests and measurements of dimensional accuracy of models manufactured using the photo-curing additive technology of POLYJET MATRIX - PJM. The material from which the samples were manufactured was a liquid polymer resin with the commercial name MED610, which is characterized by a high degree of biocompatibility, which increases the validity of the research in medical and dental applications. The key parameter considered was the location of digital CAD models in selected places of the virtual building platform and it influence of dimensional accuracy. The research results showed a clear impact of the arrangement of models on the building platform on the accuracy of the produced models, both in terms of external and internal diameters.

Laser powder bed fusion of a nanocrystalline Finemet Fe-based alloy for soft magnetic applications

The aim of this work is to explore the laser powder bed fusion (LPBF) processability window of the nanocrystalline soft magnetic Finemet alloy. With that purpose, several laser power and scan speed values and a meander scanning strategy were probed to process simple geometry specimens. Good dimensional accuracy was obtained within the entire processing window investigated. Relative densities as high as 89% were achieved for processing conditions including high laser power and low scan speeds. The fraction of amorphous phase, which peaked at 49%, was found to be mostly dependent on the scan speed and only slightly influenced by the laser power. The microstructure of the crystalline domains is formed by ultrafine, equiaxed grains with random orientations. Irrespective of the processing conditions, the LPBF-processed samples exhibit a similar saturation magnetization, lower permeability, and higher coercivity than fully amorphous melt-spun ribbons of the same composition. The coercive field of the additively manufactured specimens is fairly independent of the relative density and exhibits a moderate inverse variation with the amorphous fraction. Consistent with earlier works, this study suggests that the average grain size is an important contributor to coercivity.

An experimental study of ultrasonic-knife cutting for a woven carbon fiber preform by an industrial robot

This study investigates the effect of the cutting process parameters on the cutting forces and the quality parameters of a woven carbon fiber preform during robotic ultrasonic-knife cutting. An ultrasonic cutting device, with a power of 1200 W, a frequency of 24 kHz, and an amplitude of 60 µm, mounted on the end effector of a six-axis degree of freedom industrial robot to make linear cuts. A three-level factorial experimental design was used to examine the effect of the feed rate (1 m/min to 5 m/min) and the knife’s attack angle (45° to 75°) on the cutting forces, the dimensional accuracy of the machined preform coupons, and the damage on the machined preform edges. The cutting force analysis results show that the increasing feed rate resulted in increasing feed force and thrust force. However, the increase of the attack angle increases the feed force but decreases the thrust force. The average width and damage of the ultrasonic knife cut preform coupons are highly related to the process conditions. The combination of the low feed rate, 1 m/min, and the low attack angle, 45°, resulted in dimensional errors ranging from 253 μm to 365 μm oversized from the programmed 15.0 mm width with no damage. When the feed became 3 m/min and 5 m/min at the attack angle of 75°, the preform coupons’ dimensional accuracy and damage formation worsened. In these conditions, the ultrasonic knife attached to the industrial robot arm could not cut the preform plate effectively, so the tows on the preform were unevenly cut or dislodged.

Influence of milling interventions on the geometry of wall-shaped structures in hybrid wire-arc direct energy deposition

The Hybrid Wire-Arc Direct Energy Deposition (Hybrid Wire-Arc DED) process integrates Wire-Arc Direct Energy Deposition (Wire-Arc DED) with machining (typically milling) interventions, offering the potential for creating intricate geometries and finished surfaces. However, if milling is employed as a hybrid intervention rather than as a final part-finishing process, the interplay between these processes remains under-investigated. This paper examines the influence of milling interventions on the geometry of a wall-shaped structure, quantified by the transverse cross-sectional width, built using a Hybrid Wire-Arc DED. Through experiments on mild steel, the underlying causes of observed wall-width variations are analyzed. Initial observations suggested that thermo-mechanical deformations from milling influence the width variations. However, evidence indicates the significant role of additional remelting cycles experienced by the milled surface layer during subsequent layer depositions. The study also reveals that the observed increase in wall width for each milling intervention occurs at approximately the same depth below the milled surface. A mechanistic explanation for this observation is given. Crucially, the findings suggest that unless milling is done at higher frequencies, like after each layer deposition, the resultant unevenness might render the Hybrid Wire-Arc DED process less efficient in terms of surface quality and dimensional accuracy than its non-hybrid counterpart.

Thermal environment optimization and process product design of CNC grinding machine based on improved neural network

In the machining process of CNC grinding machine, the non-uniformity of heat source often leads to the deformation of the workpiece, the intensification of wear and the machining error, so the optimization of thermal energy environment has become an important research direction to improve the machining performance. The aim of this study is to optimize the thermal energy environment of CNC grinding machine by improving the neural network algorithm, so as to achieve the goal of improving the machining precision and surface quality. We hope that by establishing thermal environment model, we can deeply analyze the characteristics of thermal energy distribution and put forward effective optimization measures. The improved neural network model is used to model the heat energy distribution of CNC grinding machine. The accuracy of the model is improved by introducing multi-layer perceptron and convolutional neural network. At the same time, combined with the experimental data, training and testing are carried out to verify the effectiveness of the model. Genetic algorithm and fuzzy control are used to adjust the thermal energy environment and obtain the best process parameters. The experimental results show that the prediction accuracy of the improved neural network model is significantly improved, and the prediction error of the model is significantly reduced compared with the traditional method. After thermal optimization, the surface roughness of the workpiece is reduced, the dimensional accuracy is significantly improved, and the overall processing efficiency is significantly improved. By improving the neural network model, the thermal energy environment of CNC grinding machine is effectively optimized, and the machining precision and surface quality are successfully improved.

Deep learning of 3D point clouds for detecting geometric defects in gears

Geometric integrity directly impacts the functionality, reliability, and safety of final manufactured products, making the qualification of parts based on measurements of their geometry a fundamental quality control activity in modern manufacturing. Recent advancements in three-dimensional (3D) metrology technologies have enabled fine-scale inspection of geometric integrity characterized by dimensional accuracy, surface quality, and shape conformity. However, the widespread adoption of high-resolution 3D metrology in manufacturing faces some significant challenges posed by the inherent data structures of 3D point clouds such as high dimensionality, unstructured nature, and sparsity in defective regions. To address these challenges, this paper first creates a “MFGNet-gear” dataset, which is a scalable and comprehensive benchmark dataset comprising 12 part designs with four quality classes for each design. Subsequently, we develop a deep learning model adapted from the PointNet++ architecture to enable automated, end-to-end analysis of 3D point clouds. The model can be configured for different decision-making tasks including part design classification and multi-class geometric defect detection. Implementations of the proposed model on the “MFGNet-gear” dataset achieve accuracies up to 100% in classifying gear designs and up to 85% in four-class quality inspection. Additionally, we systematically investigate the impacts of measurement resolution and precision on the classification performance through a series of case studies. The obtained results highlight the potential of using deep learning methods for automated analysis of 3D point clouds for a variety of quality control tasks beyond gear manufacturing. This study also proposes future research directions, including the development of new deep learning architectures specifically designed for manufacturing 3D point clouds and strategies for adaptive measurement planning.

On the cyclic deformation behavior of wire-based directed energy deposited Fe-Ni Invar alloy

Fe-basis alloys with Ni concentrations of approximately 36 wt.-% are widely used for components with high requirements on mechanical properties and dimensional accuracy. Due to the remarkably low coefficient of thermal expansion, these so-called Invar alloys provide for dimensional accuracy over a wide temperature range. Generally, additive manufacturing technologies have already been qualified for processing of Invar alloys in terms of maintaining mechanical as well as functional properties. However, the mechanical behavior under cyclic loading still is not comprehensively understood. Therefore, the present study focuses on the fatigue properties of the Fe-Ni36 Invar alloy manufactured by wire-based directed energy deposition (DED) employing either an electric arc (DED-Arc) or a laser beam (DED-LB) as energy source. Here, a wire-based DED-LB Invar condition is comprehensively studied for the first time in open literature. For initial assessment, specimens were analyzed using optical and scanning electron microscopy as well as computed tomography and tensile testing. Here, the DED-LB material is characterized by a heterogeneous grain size distribution, less pronounced crystallographic texture as well as increased tensile strength and ductility compared to the DED-Arc counterparts. Despite increased strength and ductility, the DED-LB Invar consistently shows inferior fatigue properties. This behavior is discussed based on microstructural heterogeneity, cyclic deformation response, process-induced porosity and the contribution of plastic strain. Furthermore, investigations on the thermal expansion behavior show that the DED Invar retains its outstanding thermal properties, respectively.

Effects of Post-Processing Parameters on 3D-Printed Dental Appliances: A Review.

In recent years, additive manufacturing (AM) has been recognized as a transformative force in the dental industry, with the ability to address escalating demand, expedite production timelines, and reduce labor-intensive processes. Despite the proliferation of three-dimensional printing technologies in dentistry, the absence of well-established post-processing protocols has posed formidable challenges. This comprehensive review paper underscores the critical importance of precision in post-processing techniques for ensuring the acquisition of vital properties, encompassing mechanical strength, biocompatibility, dimensional accuracy, durability, stability, and aesthetic refinement in 3D-printed dental devices. Given that digital light processing (DLP) is the predominant 3D printing technology in dentistry, the main post-processing techniques and effects discussed in this review primarily apply to DLP printing. The four sequential stages of post-processing support removal, washing, secondary polymerization, and surface treatments are systematically navigated, with each phase requiring meticulous evaluation and parameter determination to attain optimal outcomes. From the careful selection of support removal tools to the consideration of solvent choice, washing methodology, and post-curing parameters, this review provides a comprehensive guide for practitioners and researchers. Additionally, the customization of post-processing approaches to suit the distinct characteristics of different resin materials is highlighted. A comprehensive understanding of post-processing techniques is offered, setting the stage for informed decision-making and guiding future research endeavors in the realm of dental additive manufacturing.

Real-time monitoring and control of the layer height in laser metal deposition with coaxial wire feeding using optical coherence tomography

Laser metal deposition (LMD) with coaxial wire feeding is an additive manufacturing technology in which a metal wire is fed into a laser-induced melt pool. The repeated deposition of weld beads allows three-dimensional geometries to be created that can be used for manufacturing, repair, and modification of metal components. However, the process is highly sensitive to disturbances because the fed wire must always be fully melted, and no self-regulating effects as in powder-based LMD exist. The layer height is particularly important for process stability, as even small deviations accumulate over many layers and, ultimately, lead to the termination of the process. Therefore, monitoring and closed-loop control of the layer height during the deposition process are crucial. Due to process emissions, an interruption of the process is usually necessary for the accurate optical measurement of the layer height, which negatively affects the overall productivity. In order to overcome this drawback, an in-axis optical coherence tomography (OCT) sensor was employed in this work, which enabled real-time measurements of the layer height. It was found that positioning the OCT measurement spot as close as possible to the center of the wire provided the highest signal quality. Based on the real-time height data, a closed-loop layer height control was implemented, applying the wire feed rate as the manipulated variable. The experimental results showed that the proposed system was able to compensate for significant disturbances, ensuring dimensional accuracy and process stability.

Effect of materials and process parameters on machinability of stainless steels

Stainless steels, recognized for their corrosion resistance attributed to a minimum of 11 % Chromium, encompass a variety of alloys with distinctive microstructures and properties. Machinability significantly varies among these alloys. Austenitic steels such as SS303 and 304 present challenges, demonstrating poor surface finish and high power consumption. This study, employing a central composite design, investigates the machinability of AISI 303, 304, 316, AISI 416, and AISI A36. Turning tests with PVD TiAlN-coated inserts revealed optimal parameters for cutting speeds (90.5256–244.411 m/min), feed (0.0635–0.4826 mm/rev), and depth (0.00016–0.00187 m.). Surface finish analysis identified AISI 316 as the best, closely followed by AISI 303. From a power consumption standpoint, AISI 303 performed the best, and concerning fragmented chip morphology, AISI 303 also excelled. The superior performance of AISI 303 is attributed to 2 % Manganese and 0.15 % Sulfur, proving to be the most effective combination compared to the other four steels, resulting in a higher percentage of MnS2, optimal for improving machinability. The depth of cut emerges as the most influential factor affecting dimensional accuracy. These findings hold practical significance in the selection of stainless steels and corresponding process parameters across various industries, including the manufacturing of heavy earthmoving equipment. By shedding light on the optimal composition and machining conditions, this study contributes valuable insights for enhancing performance and efficiency in stainless steel applications.

Evaluation of the effect of forming strategy in newly introduced flexible roll forming process

In recent years, with the emergence of Industry 4.0 trends and the impact of the COVID-19 pandemic, the agile manufacturing paradigm has gained increasing significance. Substantial efforts have been directed towards introducing new methods, driven by the dual objectives of flexibility and agility. This paper presents an innovative machine that employs two rollers to apply localized deformation to sheet metal through repetitive movement along the length of the sheet. As with all incremental forming processes, the forming strategy is a critical parameter influencing both the forming force and the dimensional accuracy of the manufactured workpiece. In this research, two different forming strategies, internal and external, were implemented for manufacturing elongated 90 deg bends on 1 mm and 3 mm thick AISI 304 sheets. Both numerical and experimental analyses were performed to assess the effects of these different strategies. The results confirm that the forming force with the external strategy is 50 % and 47 % less for the 1 mm and 3 mm sheets, respectively. Moreover, the external forming strategy allows for better control of the obtained angle, but limits the minimum obtainable bending radius.

Jerk limited continuous tool path generation for flexible systems in non-cartesian coordinate systems

Inspired by computer numerical control (CNC) technology and drastic changes in the world of electronics and microcontrollers, the idea of using non-cartesian coordinate system for CNC machines became a topic of interest. Non-cartesian coordinate systems provide freedom of movement in systems with large working spaces where high dimensional accuracy is not a prime concern. Applications of such systems can be found in murals, where a painting is applied to a large wall of any arbitrary build material. In the present paper, a new design was introduced to automate the process of drawing murals on large surfaces. The proposed mechanism utilized chain and sprocket to overcome the size limitations of traditionally available x-y tables. In addition to that, the developed mechanism does not utilize the common robust structures used in the conventional CNC systems and the tool motion is created using flexible arms that are not heavily constrained. The effect of systems unwanted vibration has been eliminated using continuous trajectory and kinematic profiles. Parametric curve interpolation algorithms for trajectory planning were implemented to increase the flexibility of drawing complex curves. Parametric curves such as B-spline and non-uniform rational B-spline (NURBS) were employed to generate a jerk limited trajectory for motion control and extraction of kinematic profiles. Such trajectory constrains the jerk of the system and guarantees a smooth motion free of feed-rate fluctuations. Compared to other methods available for extracting the kinematic profiles, jerk limited method decreases the travel time and trajectory error. Ultimately, motion commands were produced by using kinematic profiles and the second order Taylor interpolator. STM32746ZG card was used as the main processor and two stepper motors controlled by a Bit Pattern interpolation algorithm were utilized to drive the proposed mechanism. The input pulses of stepper motors were stored as binary bits and transmitted to drivers in real-time. The feedback was also evaluated by two rotary encoders, placed at the end of the motors’ shafts. Encoder data were acquired and transmitted using a TCP/IP protocol to guarantee zero data loss during the transmission.