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This study examines the impact of cutting regimes on determining cutting resistance in the processing of polypropylene (PP) using the CNC lathe EMCO F5. The rationale for this research stems from polypropylene's rarity among thermoplastics in possessing structural stability, allowing for its comparison to metals and practical application in products replacing metal parts. Leveraging its favorable mechanical properties, polypropylene finds utility in producing parts subject to dynamic loads, boasting high resistance to impact loads—particularly undesirable in machining. An advantageous characteristic of polypropylene is its affordability, rendering it an economical choice across numerous applications. Despite these merits, polypropylene's exploration in cutting processing remains limited, underscoring the novelty of this research endeavor. The main method for determining cutting resistance involves measuring electric current strength during processing. This direct measurement, facilitated by input cutting regime parameters, is recorded by the PLC controller, with the current value extracted from the machine tool's ammeter. The experimental approach entails varying cutting regime parameters—cutting speed (v), feed rate (s), and depth of cut (a)—across minimum and maximum values, recognized as pivotal factors influencing cutting force development and the attainment of the desired machined surface quality. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The research aims to predict the parameters between the cutting speed range correlated to the depth of cut for the CNC lathe. Design/methodology/approach: The model predicts the cutting speed parameters carried out based on the data range between the depth of the cut and the cutting speed. That information has been derived from the machine tool handbook and expert engineer recommendations. The fuzzy logic-based methods were used to predict cutting speed parameters for three different materials: aluminium, machine steel, and stainless steel. The data range in each material was used to condition the membership function. Findings: The result shows that the prediction cutting speed parameters are related to the range of the depth of the cut between 0.15 and 0.4 mm. It is observed that if the depth of the cut is very high, the cutting speed is lower. The information obtained is slightly different from the machine tool handbook. It can be used with the feed rate parameters to perform the machining process of the CNC lathes in the smart factory. Research limitations/implications: Further research should focus on predicting surface roughness and tool wear in the turning. Practical implications: The cutting speed selection has a significant impact on manufacturing. It affects production time, tool wear, cost, etc. Generally, the parameter has been derived from machining handbooks or machine tools textbooks, and some data is vague because it has only maximum and minimum. The data between ranges is unclear for operation. Executing production planning for new engineers was hard, which can affect manufacturing systems. Therefore, proper and precise cutting parameters are required. Originality/value: General machine tool manuals often provide vague information on recommended parameters and only show the maximum and minimum values. In past research, it has only a determined parameters range for the experiment. The data between ranges is unclear for operation. In this research, the parameter prediction was performed between the cutting speed range related to the cutting depth, which is for use in the CNC lathe process. [ABSTRACT FROM AUTHOR]

This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

This paper presents a thermal error model for a lathe CNC machine using temperature on moving parts, which is usually a difficult measurement. Different working conditions are performed to obtain temperature and thermal displacement of the machine. A multi-linear regression model is applied for modelling relationship between temperatures and thermal errors. Data of two constant working conditions are implemented to find out the fitting function. Through analysis, it shows that thermal errors for X- and Z-axes can be estimated based on only four temperature points (T2, T3, T10 and T12 for the X-axis and T4, T6, T7 and T11 for the Z-axis). Results reveal that using movable part temperature data (T12), the average and maximum error in the predicted thermal displacement of the X-axis can be reduced about 1/3 and 2/5 compared to without using T12, while the movable temperature point on Z-Screw (T8) has a tiny effect on the average and maximum error of predicted thermal displacement of the Z-axis. Results herein can provide useful information to develop a thermal compensation for the CNC lathe machine. [ABSTRACT FROM AUTHOR]

CNC lathes often use hydraulic systems or motors to lock the turret, which causes the turret to have a high temperature rise and a large thermal deformation. The traditional measuring method couples the thermal distortions of the spindle and the turret together, which is not conducive to establishing the thermal error model. To solve this problem, a new measuring method was used in this research to decouple the thermal linear and angular distortions of the spindle and the turret. In addition, constraints on the model coefficients were proposed by studying the effects of long-term and short-term variations in ambient temperature on the thermal deformation of machine tools, thus transforming the thermal deformation modeling of the spindle and turret into nonlinear programming problems. After building the thermal deformation models of the spindle and the turret, the thermal distortion model of the whole machine tool was obtained by combining them. Finally, three experiments were designed to verify the validity of the established models, and the models were compared with those established using conventional methods. The experimental results showed that the models built based on thermal distortion decoupling and nonlinear programming had higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

This study examines the impact of cutting regimes on determining cutting resistance in the processing of polypropylene (PP) using the CNC lathe EMCO F5. The rationale for this research stems from polypropylene’s rarity among thermoplastics in possessing structural stability, allowing for its comparison to metals and practical application in products replacing metal parts. Leveraging its favorable mechanical properties, polypropylene finds utility in producing parts subject to dynamic loads, boasting high resistance to impact loads—particularly undesirable in machining. An advantageous characteristic of polypropylene is its affordability, rendering it an economical choice across numerous applications. Despite these merits, polypropylene’s exploration in cutting processing remains limited, underscoring the novelty of this research endeavor. The main method for determining cutting resistance involves measuring electric current strength during processing. This direct measurement, facilitated by input cutting regime parameters, is recorded by the PLC controller, with the current value extracted from the machine tool’s ammeter. The experimental approach entails varying cutting regime parameters—cutting speed (v), feed rate (s), and depth of cut (a)—across minimum and maximum values, recognized as pivotal factors influencing cutting force development and the attainment of the desired machined surface quality.

A machining center is a common machine tool that can machine complex free-form surfaces such as the press mold, automobile’s cam profile. When these curved surfaces are machined by the conventional machining center and/or the grinding machine, it takes lot of time to complete with enough accuracy. In order to solve this problem, this study aims not only to improve the machining accuracy of this curved surface but also to make it possible to shorten the machining time using a CNC lathe. In this study, the NACS-Turning (Non-axisymmetric curved surface turning) method that we proposed originally was used. The spindle of C-Axis, the moving table with the cutting tool of X1-Axis, the other counter moving table of X2-Axis (without involved in shape creation, only used to suppress inertial force), the head stock of Z-Axis, and the tool rotation axis of B-Axis are adapted. In this 4-Axis machining method, all the axes are controlled with the synchronized manner. This method can machine the same rotational position of workpiece with the same cutting edge of the rotary tool. As an experiment, we compared 4-Axis machining with synchronized spindle and tool rotation, 3-Axis machining, and machining without synchronous rotation. Our new 4-Axis machining method with synchronized spindle and tool rotation reduced the form error from 323 ㎛ to 247 ㎛ and the surface roughness from Rz 9.6μm to Rz 3.7μm. Moreover, the repetitive machining resulted in error of 99.6μm and a surface roughness of Rz2.9μm.

CNC Lathe machines are machines used in the manufacturing industry to produce components for the engineering sector in large quantities quickly. This study aims to find out the time of the tool holder manufacturing process and also to find out the time of the chisel age. This study used cutting speeds of 190 m / min, 180 m / min, and 170 m / min, feed rate 0.3 mm / round, depth of cut 0.7 mm. Based on the research conducted for the time of making a tool holder is 7:25 and the use of a chisel required for the manufacture of one tool holder is 1 piece. The Ra value obtained is also very influential with the age of the chisel. [ABSTRACT FROM AUTHOR]

Thermal error always exists in a machine tool and accounts for a large part of the total error in the machine. Thermal displacement in X-axis on a CNC lathe is controlled based on a rapid heating system. Positive Temperature Coefficient (PTC) heating plates are installed on the X-axis of the machine. A control temperature system is constructed for rapid heating which further helps the thermal displacement to quickly reach stability. The system then continuously maintains stable compensation of the thermal error. The presented rapid heating technique is simpler than the compensation of machine thermal errors by interference in the numerical control system. Results show that the steady state of the thermal displacement in the X-axis can be acquired in a shorter time. In addition, almost all thermal errors in constant and varying working conditions could be significantly reduced, by above 80% and 60%, respectively, compared to those without using the rapid heating. Therefore, the proposed method has a high potential for application on the CNC lathe machine for improving its precision.

In order to realize energy saving and high-efficiency production in the CNC turning process of parts, a novel hybrid approach for cutting parameters' optimization considering processing energy and efficiency is proposed. Through experimental fitting and orthogonal experiments, the no-load power and cutting power functions of the CNC lathe were obtained, and a multi-objective optimization model of energy saving and high efficiency was established. The model takes the minimum energy consumption and the fastest processing time as the goal, and comprehensively considers the actual constraints under the processing conditions of the lathe. An improved multi-objective teaching-learning-based optimization (TLBO) algorithm is designed to solve the multi-objective model, a self-study method is added in the learning stage to improve the global search ability of the algorithm, and a minimum partial optimization strategy is added in the teaching stage to ensure the balance of multi-objective optimization results. Finally, the proposed model and algorithm are verified via a typical case, the cutting parameter decision model is established by using analytic hierarchical process (AHP), and the Pareto front solution of cutting parameter optimization is obtained, which objectively selects a better combination of turning parameters. The results show that this method can guide the operator to select cutting parameters from qualitative random to quantitative, and provides theoretical guidance for enterprises implementing energy saving and high-efficiency production. [ABSTRACT FROM AUTHOR]

Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the fault propagation model, disregarding the dynamics and influence of fault propagation. Therefore, this paper proposes a key component identification method based on the dynamic influence of fault propagation. Based on the CNC lathe architecture and fault data, the cascaded faults are analyzed. The improved Floyd algorithm is used to iterate and transform the direct correlation matrix expressing the cascaded fault information, and the fault propagation structure model of each component is constructed. The coupling degree function is introduced to calculate the dynamic impact degree between components, and the dynamic fault propagation rate of each component is calculated with the dynamic fault rate model. Based on this, the dynamic influence value of fault propagation is obtained by using the improved ASP algorithm. The key components of the system are identified by synthesizing the fault propagation structure model and the dynamic influence value of fault propagation. Taking a certain type of CNC lathe as an example, the proposed method is verified to be scientific and effective by comparing with the traditional identification method of key components based on fault propagation intensity. [ABSTRACT FROM AUTHOR]

Recently nanofluids have pinpricked a tremendous research interest among the institutions and manufacturing industries because of a good coefficient of conductive heat transfer and a high impact of lubrication in machining. In this study, nanofluids of powders (Al2O3 and TiO2) each with concentrations of 0.5 and 1 gm in 100 ml of ACTIV 4T SAE20W40 engine oil were prepared and compared with dry and lubricant condition for the turning of Inconel 718 superalloy using MQL methodology. Turning was being done in MTAB CNC lathe machine with 200, 300 and 400 rpm speed keeping a constant feed of 0.05 mm/rev and constant depth of cut 0.5 mm by using carbide insert with a positive rake angle of 15 degrees. Machining characterisation through SEM was carried out. The experimental results show that the surface roughness, chip thickness and chip morphology are more suitable at a speed of 400 rpm for machining of Inconel 718 for a concentration 1 gm of Al2O3. The influence of cutting parameter and nanoparticle concentration was also being analysed with ANOVA. The machining results obtained for every concentration are analysed so that the optimisation of different parameters is to be obtained for the structured machining of Inconel 718. [ABSTRACT FROM AUTHOR]

Due to the many components in the multi-unit system (i.e., CNC machine tools), the existing preventive maintenance (PM) strategies conducting independent PM for every component are time-consuming and waste unnecessary maintenance and downtime costs. Hence, this paper introduces the idea of opportunistic maintenance into the PM and proposes an opportunistic imperfect PM approach, which is a more realistic PM policy for a multi-unit system. In this approach, the effect of PM is assumed to be imperfect to correct the failure rate of each unit. According to the corrected failure rate function, the imperfect PM strategy of a single unit is optimized by minimizing the proposed unit maintenance cost rate under the limitation of minimum reliability. Then, these independent imperfect PM strategies for units are rescheduled by the proposed opportunistic PM strategy under the condition of judging the reliability OM threshold of units. We further optimize the opportunistic PM strategy by minimizing the total maintenance cost. Finally, the advantage of our approach is verified by the case study of 18 CNC lathes. [ABSTRACT FROM AUTHOR]

Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the fault propagation model, disregarding the dynamics and influence of fault propagation. Therefore, this paper proposes a key component identification method based on the dynamic influence of fault propagation. Based on the CNC lathe architecture and fault data, the cascaded faults are analyzed. The improved Floyd algorithm is used to iterate and transform the direct correlation matrix expressing the cascaded fault information, and the fault propagation structure model of each component is constructed. The coupling degree function is introduced to calculate the dynamic impact degree between components, and the dynamic fault propagation rate of each component is calculated with the dynamic fault rate model. Based on this, the dynamic influence value of fault propagation is obtained by using the improved ASP algorithm. The key components of the system are identified by synthesizing the fault propagation structure model and the dynamic influence value of fault propagation. Taking a certain type of CNC lathe as an example, the proposed method is verified to be scientific and effective by comparing with the traditional identification method of key components based on fault propagation intensity.

Abstract The current research of reliability allocation of CNC lathes always treat CNC lathes as independent series systems. However, CNC lathes are complex systems in the actual situation. Failure correlation is rarely considered when reliability allocation is conducted. In this paper, drawbacks of reliability model based on failure independence assumption are illustrated, after which, reliability model of CNC lathes considering failure correlation of subsystems is established based on Copula theory, which is an improvement of traditional reliability model of series systems. As the failure time of CNC lathes often obeys Weibull or exponential distribution, Gumbel Copula is selected to build correlation model. After that, a reliability allocation method considering failure correlation is analyzed based on the model established before. Reliability goal is set first and then failure rates are allocated to subsystems according to the allocation vector through solving the correlation model. Reliability allocation is conducted for t = 1. A real case of a CNC lathe and a numerical case are presented together to illustrate the advantages of the reliability model established considering failure correlation and the corresponding allocation method. It shows that the model accords to facts and real working condition more, and failure rates allocated to all the subsystems are increased to some extent. This research proposes a reliability allocation method which takes failure correlation among subsystems of CNC lathes into consideration, and costs for design and manufacture could be decreased.

Due to the complexity of the tool and cutting geometry in machining, many undesirable effects occur during the cutting. Vibrations affecting the poor surface quality of the workpiece and the tool life are one of the most important undesirable effects. These vibrations causing loss of production should be detected and the most appropriate working conditions in which the machine tools will work more stable should be determined. In this work, an artificial neural network estimator has been designed in order to analyze the oscillating effects on computer numerical control (CNC) lathes. Firstly, experimental studies have been carried out, and then vibration analysis has been performed by using two types of artificial neural networks. The experimental part of this manuscript analyses has been carried out for the different material types, spindle speeds and feed rates which are affecting the vibration. After the experimental part, two different artificial neural network structures have been proposed in order to analyze the vibration data measured from the CNC lathe under the relevant working conditions. As a result of these experimental and simulation-based approaches, it was seen that the proposed RBNN structure has a good performance even in real-time parameters in capturing the vibrations occurring in the CNC lathe. [ABSTRACT FROM AUTHOR]

The article aims to use the generated sound as operational information needed for adaptive control of the metalworking process and early monitoring and diagnosis of the condition of the machined materials using a newly introduced surface roughness quality index due to the sound-controlled machining process. The object of the measurement was correlation between the sound intensity generated during cutting and the material parameters of the machined surface, i.e., the roughness of the machined surface and the degree of wear of the cutting tool. The roughness was measured during longitudinal turning of a steel billet with a P25 insert made of 12X18H10T steel and a T15K6 cutting insert made of a titanium, cobalt, and tungsten group alloy. The correlation between the sound and roughness of the machined surface was 0.93, whereas between the sound and wear of the cutting tool was 0.93. The correlation between sound and tool wear in the experiment with P25 and T15K6 cutting inserts and the correlation between sound and roughness is positive.

Copyright of Materials & Technologies / Materiali in Tehnologije is the property of Institute of Metals & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Real-time monitoring of the cutting force in the machining process is critical for improving machining accuracy, optimizing the machining process, and optimizing tool lifetime; however, the dynamometers are too expensive to be widely used by machine tool users. Therefore, this paper presents a simple and cheap apparatus—a smart tool holder—to measure the cutting force of turning tools in the finishing turning. The apparatus does not change the structure of the turning tool. It consists of a tool holder and a piezoresistive force sensor foil, and transmits the signal through Bluetooth wireless communication. Instead of dealing with the circuit hardware, this paper uses the Artificial Neural Network (ANN) model to successfully calibrate the warm-up shift problem of the piezoresistive force sensor. Such a software method is simple, and considerably cheaper than the hardware method. For the force measurement capability of the smart tool holder, the cross-interference between orthogonal forces are very small and thus can be ignored. The force reading of the smart tool holder possesses high repeatability for the same turning parameters and high accuracy within the experiment groups. The authors apply the smart tool holder to cut the low carbon steel S15C, and to determine its specific cutting force in fine turning. The resulting fine turning force model agrees very well with the measurement. Its mean absolute deviation is 3.87% and its standard deviation is 1.55%, which reveals that the accuracy and precision of the smart tool holder and the fine turning force model are both good.

The reliability of CNC lathe was allocated based on the failure information of CNC lathe. It used Edgeworth series method to expand the fault data into standard normal variable expansion to obtained the reliability. Tested with data fitting method,the distribution curve of Edgeworth was well resembled. To obtain subsystem reliabilities,the Edgeworth series method was introduced. Concentrating three kinds of influence factors from the perspective of fault information,the allocation model based on Date Envelopment Analysis（ DEA） was proposed. Based on failure information of CNC lathe,this method was considerably practical to improve the redistribution of the machine’s reliability.

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Identification of critical components and their prioritization for implementation of maintenance is an important task in industry. It is also one of the essential step of reliability-centered maintenance. In this paper, an attempt made is being made to find out the key factors associated with the criticality of components. Five major criteria affecting criticality of components i.e. cost, functional dependency, complexity, maintainability, and safety impact were proposed for criticality analysis. In addition, to identify the critical components a hierarchical network is proposed based on analytic network process. The proposed methodology evaluated with a case study on CNC lathe machine. This study can provide a realistic solution to decision-making problems for maintenance planning in prioritizing the critical component for reliability-centered maintenance. [ABSTRACT FROM AUTHOR]

The article describes the identification of the impacts of the machining process itself, as well as the influence of the properties of the machine tool construction, allows to predict and subsequently optimize its design in terms of its working accuracy. The practical importance of virtual machining is primarily the reduction of financial costs and acceleration of machine design without the need to produce a physical prototype. Using simulation models, it is possible to repeatedly analyze the weaknesses of machine construction, to identify the impacts of individual machine components on their properties and to optimize them, but also to take account of ergonomic and other requirements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In turning process, regenerative chatter stability is regarded as an outcome favor in achieving reliable cutting performance. Since nowadays there is great demand in producing very high quality parts, researchers devoted great efforts in developing theoretical and analytical means for understanding, analyzing and solving the stability of a given machining process. In general, the stability of turning process could be influenced by random variables such as machine stiffness, damping and cutting force. Therefore, in this paper, a new method for predicting the reliability of regenerative chatter stability in turning process is proposed. This involves application of Monte Carlo simulation (MCS) tool to study the statistical distribution of regenerative chatter stability during turning process. The employed dynamic model of the regenerative chatter was defined, and the relationship between cutting depth and spindle speed was obtained by Laplace transformation. The sampling distribution information of kinetic parameters of the cutting system was evaluated for the regenerative chatter model. Thus, the limit cutting width of the samples was obtained. The statistical characteristics probability showed that the actual cutting width is less than the limit cutting width. Therefore, the regenerative chatter stability prediction by Monte Carlo simulation was regarded as a criterion. [ABSTRACT FROM AUTHOR]

The operation principles of both new and improved clamping mechanisms of the CNC lathe with the automatic no–reinstallation workpiece manipulation are described in this article. The theoretical analysis of power characteristics with the spindle in rotary and non-rotary modes has been carried out with the account of unbalanced chuck jaws’ centrifugal forces. The efficiency of the new clamping mechanism at a higher spindle velocity has been justified. [ABSTRACT FROM AUTHOR]

In this paper, the problem of accuracy control of machined complex profiles is investigated. Presented are schemes of the mechanical load of the cutting tool as a function of the current angle of the machined profile. A simple method for compensating the profile error caused by mechanical deformations is suggested. Presented is an algorithm for the automatic measurement of the machined profile and for the compensation of the profile error. [ABSTRACT FROM AUTHOR]

In the era of mass manufacturing, MRR (material removal rate) is of primary concern even in manufacturing using contemporary CNC (computer numerical controlled) machines. In this paper Taguchi method has been employed with L25 (5^3) Orthogonal Array for three parameters namely Speed, Feed and Depth of cut. For each of these parameters five different levels have been identified and used to perform the turning parameters for maximization of material removal rate on an EMCO Concept Turn 105 CNC lathe. The material selected for machining was SAE 1020 with carbide cutting tool. The MRR is observed as the objective to develop the combination of optimum cutting parameters. This paper proposes an optimization approach using orthogonal array for the maximized MRR and the result from this study confirms the same. This study also produced a predictive equation for determining MRR with a given set of parameters in CNC turning. Thus, with the proposed optimal parameters it is possible to increase the efficiency of machining process and decrease production cost in an automated manufacturing environment. [ABSTRACT FROM AUTHOR]

Bu çalışma, CNC torna tezgahlarında programlama yapmanın kolaylaştırılmasını, hazırlanan parça programının tezgaha ait sınır parametrelerine göre optimizasyonunu sağlamaktadır. Çalışmada deney ve optimizasyon işlemlerinde BOXFORD 250 B CNC torna tezgahı kullanılmıştır. CNC torna tezgahında kaleme gelen kuvvetlerin ölçülmesi gerçekleştirilmiştir. Yapılan deneylerde yüzey tornalama işleminde kesici uçları için oluşan kesme kuvvetleri; değişik talaş derinlikleri, ilerleme ve kesme hızları ile kesici uçları için tespit edilerek bir veri tabanı elde edilmiştir.

Some results of the investigation of factors influencing the roughness of turned surfaces are presented In the paper. The main parameters affecting the surface roughness of machined sourfaces are: cutting speed, feed and axial depth of cut. By choosing different cutting regimes in finishing turning on a CNC lathe, it was investigated the variation of the surfaces roughness. [ABSTRACT FROM AUTHOR]

Incremental sheet forming (ISF) is a manufacturing method for sheet metal parts which has a high rate of flexibility combined with relatively simple and cheap technological equipment. The main objective of the paper is to evaluate the surface quality parameters in the case of conical parts made with the ISF process on a usual CNC lathe. The main conclusion is that ISF on a CNC lathe can be successfully used for manufacturing hollow conical parts in mild steel DC04 Am with a certain advantage for small batch production [ABSTRACT FROM AUTHOR]

In the present investigation, A356 alloy 5 wt% SiC composite is fabricated by electromagnetic stir casting process. An attempt has been made to investigate the effect of CNC lathe process parameters like cutting speed, depth of cut, and feed rate on surface roughness during machining of A356 alloy 5 wt% SiC particulate metal-matrix composites in dry condition. Response surface methodology (Box Behnken Method) is chosen to design the experiments. The results reveal that cutting speed increases surface roughness decreases, whereas depth of cut and feed increase surface roughness increase. Optimum values of speed (190 m/min), feed (0.14 mm/rev) and depth of cut (0.20 mm) during turning of A356 alloy 5 wt% SiC composites to minimize the surface roughness (3.15μm) have been find out. The mechanical properties of A356 alloy 5 wt% SiC were also analyzed. [ABSTRACT FROM AUTHOR]