Cutting forces that affects the cutting tool during machining are one of the important parameters must be known to select the economical cutting conditions and mount the workpiece on machine tools, securely. In this paper, the effects of the feed rate in machining of aluminum alloy 5083, used in lightweight structural application in automotive, shipbuilding and aerospace industries, on the cutting tool stresses have been investigated. Cutting forces were measured by a series of experimental measurements while stress distributions on the cutting tool were analyzed using a commercial finite element method (FEM) (ABAQUS). The results showed that the feed rate is the most relevant cutting parameter affecting cutting tool stresses.

Surface quality along with the low production cost, play significant role in today’s manufacturing market. Quality of a product can be described by various parameters. One of the most important parameters affecting the product quality is surface roughness of the machined parts. Good surface finish not only assures quality, but also reduces the product cost. Before starting any machining process, surface finish is predictable using cutting parameters and estimation methods. Establishing a surface prediction system on a machine tool, avoids the need for secondary operation and leads to overall cost reduction. On the other hand, creating a surface estimation system in a machining plant, plays an important role in computer integrated manufacturing systems (CIMS). In this study, the effect of cutting parameters, cutting tool vibration, tool wear and cutting forces on surface roughness are analyzed by conducting experiments using different machining parameters, vibration and dynamometers sensors to register the amount of tool vibration amplitude and cutting force during the machining process. For this, a number of 63 tests are conducted using of different cutting parameters. To predict the surface quality for different parameters and sensor variables, an ANN model is designed and verified using the test results. The results confirm the model accuracy in which the R2 value of the tests was obtained as 0.99 comparing with each other.

Metal cutting can be associated with high heat generation in the tool-chip interface zone and hence, the thermal aspects of the cutting process strongly affect the accuracy of the ultra precision machining. This study presents the effect of tool edge radius and cutting speed on heat generation and balance of energy in nanomachining process using three dimensional molecular dynamics simulation with Embedded Atom Method (EAM) metal potential energy. Results show whenever tool scratches the work material, atoms velocity of surface layers is increased dramatically, which influence kinetic energy and workpiece temperature. By increasing cutting speed to 400%, cutting forces are increased between 21-27%. Although tool forces is not increased considerably by cutting speed, a big fraction of potential energy, kinetic energy and heat transfer are remained in workpiece. So, workpiece temperature and temperature gradient is increased dramatically. Increasing cutting speed form 50 m/s to 200 m/s affect on temperature increment near machined surface from 300oC to 700oC which can affect the surface roughness. In addition, tool edge radius increases in contact area between tool and workpiece when tool fore is increased especially in trust direction. Consequently, temperature gradient is raised locally in chip formation area by tool edge radius.

The effect of texturing the tool rake surface on the surface quality in hard turning of 1191/1 steel with a surface hardness of 45 HRC was studied in this research. The pattern parameters including, cavity diameter, pitch, and depth, as well as the pattern distance from the main cutting edge were changed in 3 levels, assuming the cutting tool with regular cavity texture. Nine tests were designed using the Taguchi DOE and conducted in dry and lubricated conditions with 2 repeats. Machining forces during the tests and surface roughness of the machined workpieces were measured in machining under lubricated and dry conditions. The results showed that in turning with a textured tool under lubrication, changing the parameters of the texture pitch and the distance from the cutting edge increased the surface roughness of the workpiece by 57.6% and 39.2%, respectively. This is while the increase in the diameter of the tissue cavity, due to the reduction of the contact area in the tool-chips interface and better lubrication near the cutting region, improved the surface roughness up to 40.7%. The cavities depth of also did not have a significant effect on improving lubrication and reducing the roughness of the final surface. In dry turning, increasing the cavities diameter in texture and decreasing the pattern pitch, reduced the surface roughness by 10.6% and 29%, respectively. Examining the SEM images also indicated the production of the workpiece surface with smoothed texture when turning using optimized textured tool.

Broaching is a specific method of removing metal by tools that have successively increasing cutting edges, the part is made through tool moving in a certain path. The cutting force is high as a result of high removal rate in the broaching process. The process may have too many surface errors since high displacement arose by this cutting force. For this reason, proper attention should be paid in the broaching tool design, geometry analysis and the process conditions. Considering the broach machining table and accessories, the dynamometer installation is complicated. Therefore, the finite element modeling can be used for evaluating the cutting force. In this study, the simulation of broaching process is done using Abaqus Software. After modeling the process, the effect of rake angle and cutting speed parameters on cutting forces are investigated.

This work aims to develop models to investigate the effects of flank wear on cutting forces during the turning of ck45 steel using carbide tools. Therefore, various turning experiments were performed with different cutting conditions. Flank wear and cutting forces were recorded at different stages of each experiment. The data obtained from the experiments showed that the tangential component of the cutting force was not significantly correlated with the tool flank wear. Instead, there was a good correlation between the axial and radial components of the cutting forces against flank wear. Since the cutting forces depend on both the cutting conditions and the tool flank wear, different cutting forces and cutting condition ratios were used to find the cutting force models that are more sensitive to tool wear. These ratios were used to develop artificial neural network models. The statistical results showed that the tool wear obtained from the artificial neural network models was very close to the results obtained from the experiments. In addition, the accuracy of the models including the axial component of cutting force was higher than in other models.

The present paper has been dedicated to finite element simulation and experimental study of cutting tool temperature during laser-assisted machining. To achieve this objective, a finite element model of the processes has been developed for Inconel 718 super alloy and the results have been verified by experimental measurements of cutting forces and cutting tool temperature. In this regard, first of all, a finite element model of the laser-assisted turning process was developed and then an experimental setup was designed and manufactured. Finally, a series of experimental tests were arranged to achieve a proper range of process parameters and also to measure cutting forces and cutting tool temperatures during the machining process. Experimental results were then used to verify the results of the finite element model. Using the developed model, the effect of laser source power, cutting speed, and feed on cutting tool temperature were studied. According to the achieved results, using a laser heat source, in the range without microstructural effects, will lead to a 25% reduction in the average main component of cutting force and an 80% reduction in the average maximum temperature of the cutting tool in comparison to conventional turning.

Objective The cutting tool layout in the TBM cutter head is considered one of the most effective factors in improving the efficiency of boring operations, the TBM service life, the proper performance of the cutting tool, and reducing drilling costs. Method In the design process of the TBM cutting tool layout, we faced a multi-objective optimization problem with nonlinear constraints, which caused computational complexity. In the present research, in order to evaluate the impact of cutting tool star layout on TBM performance, a numerical model has been developed using meta-heuristic GWO to design a star layout. Also, two types of star layouts (1: 8-pointed star, 2: 12-pointed star) were designed using the presented model, and the TBM performance was evaluated. Findings In order to evaluate the performance of the developed design model, the layout design process of the cutting tool in the cutter head of an operational TBM sample was evaluated. Based on the results, it is determined that based on the optimal 8-pointed star layout of the cutting tool, the lateral force Fs has decreased by 78.88% compared to the original layout of the cutting tool, and the eccentricity torque has decreased by 15.03%. Also, by evaluating the performance results of the TBM with optimal 12-pointed star layout, it was determined that the total lateral force Fs decreased by 87.89% based on the optimal 12-pointed star cutting tool layout compared to the original cutting tool layout and the eccentricity torque is reduced by 23.68%. From the comparison of the performance results of the TBM with an 8-pointed star layout and a 12-pointed star layout, it was found that the TBM with a 12-pointed star layout showed better performance. Based on the results, it is clear that the optimal star layout of the cutting tool in the TBM cutter head has improved the TBM performance from the point of view of drilling engineering (increasing efficiency and drilling progress). The most important result of this research included providing an efficient numerical model for designing the optimal star layout of the TBM cutting tool based on the grey wolf optimization algorithm. The presented model can be implemented under different operating conditions and for different types of TBMs.

The arrangement and layout of cutting tools in the cutter head are among the most critical factors affecting the performance of the Tunnel Boring Machine (TBM). These factors directly impact the drilling operation efficiency, the TBM's useful lifespan, and the cutting tool's overall performance. In general, designing the cutting tool layout poses a multi-objective optimization challenge with non-linear constraints, resulting in computational complexity during the design process. Researchers have faced significant challenges in developing efficient computational models for designing cutting tool layouts in TBMs due to the complexities arising from the technical requirements of TBM structures and drilling engineering constraints. In this study, the primary aim is to assess the influence of different cutting tool layouts on TBM performance. To achieve this, a numerical model has been created, employing the Grey Wolf Optimization (GWO) metaheuristic algorithm to design three types of layouts: stochastic, spiral, and star. To evaluate the performance of the developed design model, a practical TBM for rock excavation was selected, and the process of designing the cutting tool layout in its cutter head was analyzed. According to the research findings, it is evident that the TBM's performance has shown remarkable improvement with all three types of cutting tool layouts: stochastic, spiral, and star, compared to the original setup. The results indicate that the TBM with a stochastic cutting tool layout outperformed the spiral and star layouts, achieving an approximately 8% reduction in the overall lateral force compared to the star layout, and a 10% reduction compared to the spiral layout. Furthermore, the stochastic layouts led to an 11% decrease in eccentric torque compared to the star layout, and a 14% decrease compared to the spiral layout. After analyzing the results and assessing the TBM's performance under the spiral and star layouts, it was evident that the TBM with the star cutting tool layout outperformed the spiral layouts. The star layout resulted in a more significant reduction, approximately 4%, in the overall lateral force of the TBM and a 2.5% decrease in the eccentric torque compared to the spiral layouts. The most crucial outcome of this research was the successful development of an efficient numerical model for designing optimal cutting tool layouts, including stochastic, spiral, and star layouts in the TBM cutter head, utilizing the GWO algorithm. The proposed model exhibited versatility, making it applicable to different operational conditions and various types of TBMs.