Tool Forces Research Articles

Upgraded closed-form cutting force models for general-helix cylindrical milling tools with application to cutting power and energy demand modeling

This paper upgrades the original closed-form models of cutting force for general-helix milling tools for higher accuracy and demonstrates an application of the upgraded models in closed-form modeling of cutting power. The proposed models are shown to be numerically exact for the conventional fixed helix angle milling tools while the original models are not even though they are more accurate than the numerical methods. Errors of 0.00%, 12.15%, and 50.66% are recorded for an upgraded closed-form model, the equivalent original closed-form model, and an equivalent numerical method. Higher accurate applicability of the upgraded closed-form models to variable helix tools is also demonstrated for the harmonic case. Typical errors of 1.37%, 4.84%, and 9.94% are recorded for an upgraded closed-form model, the equivalent original closed-form model, and an equivalent numerical method. The proposed closed-form cutting force models are used to formulate new closed-form cutting power models for general-helix cylindrical milling tools which are applied in numerical evaluation of average cutting power. Evaluated data sets of average cutting power (seen to agree with published values) and spindle speed are then used in empirical calibration of average milling machine power demand. The high goodness-of-fit of the models with three published measured data sets are reflected in the high [Formula: see text] values of 0.9980, 0.9834, and 0.9472 and low mean percentage errors (MPE) of −0.1247, −0.4137, and −0.6242.

Establishment of empirical relations amidst mechanical attributes of friction stir welded distinctive alloys of Mg and optimized process parameters

This experimental investigation aims to formulate quadratic regression based empirical model taking into account the parameters of friction stir welding (FSW) process for predicting the optimized process parameters to maximize the response (i.e., ultimate tensile strength) of the distinctive alloys of Mg joints. Parameters of FSW process taken into consideration includes tool’s traverse speed, axial force and rotational speed of tool and response being the fabricated joint’s tensile strength. A central composite rotatable category 3–factor, 5 level design based matrix was formulated and response surface methodology was used to obtain regression based models, to generate contour plots and to visualize the interactive impacts of parameters on the joint’s tensile strength. Formulated quadratic regression based model was validated employing analysis of variance. Comparison amidst the realistic and anticipated values of the response announced the superior fitting accuracy of the formulated quadratic model. For a constant tool’s rotational speed (of 1000 rpm to 1250 rpm), the tensile strength was observed to be highly sensitive to the axial force values than the tool traverse speed values. Mean tensile strength of the friction stir welded AZ31B, AZ80A, AZ91C, AM50A and ZK51A-T5 Mg joints during the employment of optimized process parameters were found to be 217.5 MPa, 251.4 MPa, 231.9 MPa, 192.1 MPa and 173.2 MPa respectively, thereby exhibiting perfect agreement with the anticipated values.

ChatGPT and the future of plastic surgery research: evolutionary tool or revolutionary force in academic publishing?

ChatGPT and the future of plastic surgery research: evolutionary tool or revolutionary force in academic publishing?

Effects of process parameters on tool vibration and force transmissibility in high-speed micro-milling machine

Effects of process parameters on tool vibration and force transmissibility in high-speed micro-milling machine

An approach for predicting the tool forces in friction stir welding of AA6061-T6 aluminium alloy

An approach for predicting the tool forces in friction stir welding of AA6061-T6 aluminium alloy

МОДЕЛЮВАННЯ ТИСКУ ХОДОВОЇ СИСТЕМИ НА ГРУНТ В ЗАЛЕЖНОСТІ ВІД КОНСТРУКТИВНИХ ПАРАМЕТРОВ ТРАКТОРІВ

The article defines the influence of the traction resistance of the machine-tractor unit working tool on the weight distribution of the tractor along the running system axles. Depending on the type of field work, the resistance force of tools aggregated with a tractor has a different value, accordingly, the tractor has a different weight distribution along the axles and, thus, different ground pressure. An increase in pressure on the soil leads to additional costs for tillage, and in the future to a decrease in agricultural production efficiency. The article presents a mathematical model for determining the reactions of the soil, and, accordingly, the pressure of tractor engines on the ground, depending on the structural parameters of the tractor, its adequacy estimation, and an example of calculating the weight distribution along the tractor axles.

Study on Kinematic Structure Performance and Machining Characteristics of 3-Axis Machining Center

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the natural frequency of the machines is low or close to the commonly used cutting frequency, they vibrate considerably, resulting in poor workpiece surfaces and thus shortening the lifespan of the tool. The main objective of this study was to develop an experimental technique for measuring the effect of machine tool stiffness. The static rigidity of the X-axis was found to be 2.20 kg/μm, while the first-, second-, and third-order natural frequencies were 27.3, 34.4, and 48.3 Hz, respectively. When an external force of 1000 N was applied, the Y-axis motor load was found to be approximately 2740 N-mm. In this study, the finite element method was mainly used to analyze the structure, static force, modal, frequency spectrum, and transient state of machine tools. The results of the static analysis were verified and compared to the experimental results. The analysis model and conditions were modified to ensure that the analysis results were consistent with the experimental results. Multi-body dynamics analyses were conducted by examining the force of each component and casting of the machine tools and the load of the motor during the cutting stroke. Moreover, an external force was applied to simulate the load condition of the motor when the machine tool is cutting to confirm the feed. In this study, we used topology optimization for effective structural optimization designs. The optimal conditions for topology optimization included lightweight structures, which resulted in reduced structural deformation and increased natural frequency.

Microstructure and Mechanical Property Evolution of Robotic Friction Stir-Welded Al–Li Alloys

2198 aluminum–lithium alloy was friction stir-welded with a KUKA Robot integrated with a compact friction stir-welding head with a rotation speed of 800 rpm at different welding speeds. The real-time tool force in the three directions of Fx, Fy and Fz was measured with a load sensor. Mechanical properties and microstructure evolution were investigated systematically. The results showed that Fz force increased from 3.2 kN to 8.5 kN as welding speed increased from 50 mm/min to 500 mm/min. Ultimate tensile strength of 383 MPa, 88% of base metal, was obtained when the welding speed was 100 mm/min. The nugget zone consisted of refined grains with an average size of 4 μm. TEM investigation demonstrates that T1 precipitation predominated in the base metal and disappeared in the nugget zone, as a small amount of δ’ was retained. The W-shape hardness profile in all weldments and higher welding speed lead to a higher hardness value.

Implementing commercial inverse design tools for compact, phase-encoded, plasmonic digital logic devices

Numerical simulations have become an essential design tool in the field of photonics, especially for nanophotonics. In particular, 3D finite-difference-time-domain (FDTD) simulations are popular for their powerful design capabilities. Increasingly, researchers are developing or using inverse design tools to improve device footprints and performance. These tools often make use of 3D FDTD simulations and the adjoint optimization method. In this work, we implement a commercial inverse design tool with these features for several plasmonic devices that push the boundaries of the tool. We design a logic gate with complex design requirements, as well as a y-splitter and waveguide crossing. With minimal code changes, we implement a design that incorporates phase encoded inputs in a dielectric-loaded surface plasmon polariton waveguide. The complexity of the requirements in conjunction with limitations in the inverse design tool force us to make concessions regarding the density of encoding and to use on-off keying to encode the outputs. We compare the performance of the inverse-designed devices to conventionally-designed devices with the same operational behavior. Finally, we discuss the limitations of the inverse design tools for realizing complex device designs and comment on what is possible at present and where improvements can be made.

Non-invasive milling force monitoring through spindle vibration with LSTM and DNN in CNC machine tools

Milling force reflects the complex mechanical interaction between machine tool and workpiece, it is of great significance to acquire milling force accurately to monitor and optimize machining process. In this paper, a convenient method to monitor milling force non-invasively is proposed, which can predict milling force based on spindle vibration in real-time. Long and short-term memory network (LSTM) and deep neural network (DNN) are used to build the mapping between milling force and spindle vibration in data-driven manner. Temporal features of training data are extracted through LSTM, while DNN is used to reinforced key features. Accuracy of the proposed method is demonstrated by experiments. Comparing with existing data-driven prediction methods, prediction accuracy is improved at least by 16.7% using LSTM-DNN. This paper provides an easy implemented and accurate approach to acquire milling force of machine tool and has great value for machining process monitoring.

Analysis of Mechanical Excavation Characteristics by Pre-Cutting Machine Based on Linear Cutting Tests

Mechanical methods of tunnel excavation are widely used because of their high excavation output, and the selection of appropriate technology depends on ground composition and project-related features. Compared with tunnel boring machines (TBMs) and roadheaders, mechanical pre-cutting machines are used in tunnel widening and have proven to be reliable in tunnel capacity expansion. Compared to other machines, the excavation characteristics of pre-cutting machines are not systematically analyzed because of their rare use. In this study, the excavation characteristics of a pre-cutting machine are analyzed in a laboratory based on linear cutting tests performed on four rock specimens with different uniaxial compressive strengths. During testing, changes in tool forces, cutting volume, and specific energy are determined while maintaining different penetration depths, spacings, and rock strengths. The variations in these variables are selected accordingly. The results showed high similarity with the case of TBMs and roadheaders. However, in the excavation by the pre-cutting machine, the ratios of the peak-to-mean cutting forces and cutting-to-normal forces reached a maximum value at a specific s/p (spacing and penetration ratio), which is related to the optimal cutting conditions. This study can provide useful information for the operation and design of pre-cutting machines.

Next in Surgical Data Science: Autonomous Non-Technical Skill Assessment in Minimally Invasive Surgery Training.

It is well understood that surgical skills largely define patient outcomes both in Minimally Invasive Surgery (MIS) and Robot-Assisted MIS (RAMIS). Non-technical surgical skills, including stress and distraction resilience, decision-making and situation awareness also contribute significantly. Autonomous, technologically supported objective skill assessment can be efficient tools to improve patient outcomes without the need to involve expert surgeon reviewers. However, autonomous non-technical skill assessments are unstandardized and open for more research. Recently, Surgical Data Science (SDS) has become able to improve the quality of interventional healthcare with big data and data processing techniques (capture, organization, analysis and modeling of data). SDS techniques can also help to achieve autonomous non-technical surgical skill assessments. An MIS training experiment is introduced to autonomously assess non-technical skills and to analyse the workload based on sensory data (video image and force) and a self-rating questionnaire (SURG-TLX). A sensorized surgical skill training phantom and adjacent training workflow were designed to simulate a complicated Laparoscopic Cholecystectomy task; the dissection of the cholecyst's peritonial layer and the safe clip application on the cystic artery in an uncomfortable environment. A total of 20 training sessions were recorded from 7 subjects (3 non-medicals, 2 residents, 1 expert surgeon and 1 expert MIS surgeon). Workload and learning curves were studied via SURG-TLX. For autonomous non-technical skill assessment, video image data with tracked instruments based on Channel and Spatial Reliability Tracker (CSRT) and force data were utilized. An autonomous time series classification was achieved by a Fully Convolutional Neural Network (FCN), where the class labels were provided by SURG-TLX. With unpaired t-tests, significant differences were found between the two groups (medical professionals and control) in certain workload components (mental demands, physical demands, and situational stress, p<0.0001, 95% confidence interval, p<0.05 for task complexity). With paired t-tests, the learning curves of the trials were also studied; the task complexity resulted in a significant difference between the first and the second trials. Autonomous non-technical skill classification was based on the FCN by applying the tool trajectories and force data as input. This resulted in a high accuracy (85%) on temporal demands classification based on the z component of the used forces and 75% accuracy for classifying mental demands/situational stress with the x component of the used forces validated with Leave One Out Cross-Validation. Non-technical skills and workload components can be classified autonomously based on measured training data. SDS can be effective via automated non-technical skill assessment.

Stochastic Evaluation of Cutting Tool Load and Surface Quality during Milling of HPL

The topic of the article concerns the mechanics of machining plastics and their machined surface. This article deals with measurements and their stochastic (probabilistic) evaluation of the force and moment loading of the machine tools and workpiece. It also deals with the quality of the machined surface in relation to its surface roughness and surface integrity. Measurements were made under different cutting conditions on a CNC milling machine using a newly designed cutter with straight teeth. The statistical evaluation is presented by bounded histograms and basic statistical characteristics that give a realistic idea of the machining process. The practical focus of the experiments is on the milling of HPL (high-pressure plastic–laminate composite material). The listed procedures can also be applied to other materials and machining methods, and can be used for numerical modelling, setting the optimum parameters of machining technology, or for the design of cutting tools. Numerical modelling and other solution options are also mentioned. We have not yet found detailed information in the literature about the milling of HPL material, and our results are therefore new and necessary.

Hybrid optimisation of input process parameters of deep-drawn cylindrical cups from directional rolled copper strips

Deep drawing is a method of producing sheet metal. It is extensively used in the automobile, packaging and home appliance industries. Incorrect parameter selection might induce defects in stamped components. Trial and error can improve product accuracy, but at the expense of execution and calculation. An optimisation approach and a response surface model are used to determine the optimal parametric parameter design. This study proposes a full and efficient optimisation approach, starting with modelling by RSM (Response Surface Methodology) and concluding with optimal process parameter identification. Based on this, a neural network was developed to improve cylindrical cup deep drawing. Three characteristics of fabricating the cups are clearance, punch radius and coefficient of friction. This parameter demonstrates the resultant tool force, spring back, maximum forming limiting curve and maximum thinning rate of cylinder cups. Initially, the three inputs are varied to measure the outcome using simulation software by Altair inspire form. Then, the equations for each output are designed using quadratic-based linear model fitting. The generated objective function is then used to establish the ideal process parameters for the cylindrical cups. The proposed method is compared to real-time physical experiments. The optimised parameter microstructure at the high-stress zone and the product quality are satisfactory without defects.

Influence of alloy composition and lubrication on the formability of Al-Mg-Si alloy blanks

The formability of 1.0 mm-thick Al-Mg-Si alloy blanks with different Si and Mg contents was numerically and experimentally investigated using two deep drawing tools: (i) a cross-shaped tool with open die and (ii) a complex-shaped panel tool with closed die. For the cross-shaped tool, forming velocity and blankholder force were constant and the formability was characterized based on the maximum drawing depth at crack initiation. For the complex-shaped panel tool the forming velocity and the drawing depth was constant and the formability was characterized based on the maximum blankholder force at crack initiation. Two types of commercial lubricants were applied on the blank surfaces. In order to investigate critical forming conditions, numerical models of both deep drawing processes were built using the AutoForm finite element (FE) software. Parameters required as input for these models were experimentally determined. The flow curve and the anisotropy of each of the aluminum alloys were determined using uniaxial tensile tests. Based on the results of pin-on-plate tests an advanced friction model which considers different contact pressures and sliding velocities was created using the TriboForm software. Good agreement between deep drawing simulations and experiments was achieved in terms of forming force, local thinning and strains. Experiments and simulations confirmed that the maximum drawing depth or blankholder force, respectively, tended to increase with increasing Si and Mg contents. However, the influence of lubrication on the formability of the Al-Mg-Si alloy blanks was much more significant than the influence of the alloy composition.

Modeling of Probeless Friction Stir Spot Welding of AA2024/AISI304 Steel Lap Joint.

In the present study, AA2024 aluminum alloy and AISI304 stainless steel were welded in a lap joint configuration by Probeless Friction Stir Spot Welding (P-FSSW) with a flat surface tool. A full factorial DOE plan was performed. The effect of the tool force (4900, 7350 N) and rotational speed (500, 1000, 1500, 2000 RPM) was analyzed regarding the microstructure and microhardness study. A two-dimensional arbitrary Eulerian-Lagrangian FEM model was used to clarify the temperature distribution and material flow within the welds. The experimental results for the weld microstructures were used to validate the temperature field of the numerical model. The results showed that the tool rotation speed had an extensive influence on the heat generation, whereas the load force mainly acted on the material flow.

Turning of titanium alloy with PCD tool and high-pressure cooling

Titanium alloys are difficult to cut materials due to their low thermal conductivity, which leads to intensive tool wear. The general issue is finding the best combination of cutting tool material and cutting conditions to achieve high productivity. This study used PCD cutting tool material in combination with high-pressure cooling (HPC). The main task was to find the most suitable HPC mode (various HPC settings on the rake and flank faces of the cutting tool) and intensity to reduce tool wear at a high cutting speed. Tool wear, chips, and forces were measured, and surface quality was evaluated to gain an understanding of the machining process under these particular conditions. An ANOVA test was used to determine the significance of control factors such as tool life and HPC mode and intensity. The most suitable cutting speed was 300 m/min, where a limit spiral cutting length (SCL) of 3000 m was achieved. Setting the HPC mode revealed the necessity of using the HPC on the rake face. However, the HPC on the flank face further decreased tool wear. HPC intensity should be chosen based on knowledge of the cutting process. A very intense HPC above 140 bars can lead to mechanical damage to the cutting edge or unmachined surface by chip blasting but using a 60-bar HPC can reduce tool wear similarly, without causing further damage to the cutting edge.

A Comparative Study of Self-Piercing Riveting and Friction Self-Piercing Riveting of Cast Aluminum Alloy Al–Si7Mg

Abstract Cast aluminum alloys are promising materials that can simplify the manufacturing process of automobile body structures. However, the low ductility of cast aluminum poses significant challenges to existing riveting technologies. In the present work, dissimilar AA6061-T6 aluminum alloy and Al–Si7Mg cast aluminum were joined by self-piercing riveting (SPR) and friction self-piercing riveting (F-SPR) processes to reveal the effect of friction heat on rivetability of low-ductility cast aluminum alloys. The joint macro-morphology, microstructure, peak tooling force, microhardness distribution, tensile-shear, and cross-tension performance of the two processes were comparatively studied. Results indicated that the in-situ softening effect of friction heat in the F-SPR process could effectively improve the ductility of cast aluminum, avoid cracking, and reduce the tooling force by 53%, compared to the SPR process. The severe plastic deformation and friction heat induced by rivet rotation results in refined equiaxed grains of aluminum near the rivets and solid-state bonding between aluminum sheets in the rivet cavity. The F-SPR joints are superior to SPR joints in both tensile-shear and cross-tension performance due to the avoidance of cracking, increase of mechanical interlocking, and solid-state bonding of interfaces. Significantly, when Al–Si7Mg is placed on the lower layer, the peak tensile-shear and cross-tension loads of the F-SPR joints are 7.2% and 45.5% higher than the corresponding SPR joints, respectively.

Study on the Intercropping Mechanism and Seeding Improvement of the Cavity Planter with Vertical Insertion Using DEM-MBD Coupling Method

In the dry areas of Northwest China, cavity planters with vertical insertions are used for seeding on film. Due to the uncertain mechanism between cavity planters and maize seeds and soil, research on the cavity planter has been slow. Several theoretical and experimental methods have been developed to investigate the interaction between the cavity planter and maize seeds in soil. These methods enable exploration of the mechanism to reduce soil disturbance and improve seeding performance. However, these methods are unable to predict the dynamic force of tools and soil behavior because of non-linear soil properties. A simulation experiment was conducted using the DEM-MBD coupling method to explore soil disturbance caused by cavity seeders and the resistance to entry. Additionally, the effect of the maize shape and the cavity planter motion on the seed number qualification and the empty cavity rate was investigated. It was proposed that the inverted hook be used to prevent the movement of maize seeds up and down in cavity seeders, thereby improving seed filling performance. Simulations and experiments were conducted, and the results showed that the average empty cavity rate and the seed number qualification were 2.0% and 91.3%, respectively, which met the requirements of the maize sowing standards.

An investigation of hybrid models FEA coupled with AHP-ELECTRE, RSM-GA, and ANN-GA into the process parameter optimization of high-quality deep-drawn cylindrical copper cups

Deep drawing is one of the primary sheet metal forming processes that is used all over the world. The current study focused on using Analytical Hierarchy Process-Elimination and Choice Translating the Reality (AHP-ELECTRE I), Response Surface Methodology-Genetic Algorithm (RSM-GA), and Artificial Neural Network-Genetic Algorithm (ANN–GA) for determining deep–drawing performance parameters. A hybrid FEA–MCDA–RSM–ANN–GA was built using an experimental design obtained from RSM to develop better quality products. It is integrated with finite element-based numerical deep drawing simulation to understand the intended responses and the impact of design factors without the need for costly trial tests. To improve the quality of drawn cups characterization, three process parameters-clearance, punch radius, and coefficient of friction-have been tuned to their optimum values like resultant tool force (N), spring back (µm), max forming limit curve (%), and max thinning rate. The optimization results showed the efficacy of the technique for process design, resulting in the reduction of both cost and time. The desirability index was calculated and compared with all the predictions. The hybrid models that have been developed may be suggested for accurate prediction and optimization of various process parameters and outcomes for any industrial application issues that could arise.