Machining Strategies Research Articles

Milling or grinding for manufacturing of an Alloy 718 gas turbine component? – A comparison of surface integrity and productivity

Milling is traditionally the most used machining method when manufacturing complex gas turbine components. In particular those made from nickel-based superalloys. However, for larger free form surfaces, grinding may be an efficient alternative that could be used throughout the complete manufacturing route, from roughing to finishing. Hence, in this work the two processing methods has been compared in regard to surface integrity and productivity.Machining tests have been performed on case plates of heat-treated Alloy 718 using best practise setting for roughing and finishing with grinding and milling. The surface integrity of the work pieces was evaluated regarding surface topography, residual stresses, and deformation. This comparison showed that the main advantage with grinding is the ability to switch between roughing and finishing by just altering the depth of cut. Further, grinding offers lower surface roughness, compressive residual stresses, and significantly lower degree of deformation.From a productivity perspective, deep grinding may offer high material removal rates and ability to machine several work pieces in the same setup. However, grinding is limited to simpler free form geometries and may result in minor surface damages and abrasive surface residue. For selection of machining strategy, advantages and drawbacks shown in this work need to be considered for the application at hand in respect to productivity, surface integrity and requirements on fatigue life.

Impact of smoothing interpolations on the non-conventional machining of pockets

Smoothing curves have been used in computer aided design software for years. This is the reason why machinists very early wondered about the possibilities of creating toolpaths directly generated from this type of mathematical curves. In addition, in recent research work, we have shown that the non-uniform rational Bspline interpolation is the most favorable towards high-speed machining of complex shapes. As a result, we are thinking of simulating by this type of interpolation the pocket recess machining strategies proposed by a computer aided manufacturing software, currently developed in linear and circular interpolations only. In this article we propose a study of the influence of the parameters of Bspline curves on the hollowing out of pockets by smoothing initial cutting methods. To do this, we chose the smoothing of the zigzag method by Bspline and NURBS while varying the order and the discretization parameters to know the impact on the dynamics of the high-speed machining mechanism and on the non-conventional machining precision. This work has shown that parametric curves are favored for this type of machining provided that this tool is mastered.

An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries

The State estimation and determination of time-varying model parameters are crucial for ensuring the safe management of lithium-ion batteries. This paper designs a limited memory recursive least square algorithm to improve the accuracy of online parameter identification. An adaptive radial basis correction-differential support vector machine model is constructed to correct the state of charge value by considering the dynamic characteristic parameters. It greatly reduces estimation error and noise, while monitoring the critical conditions for safe and reliable online battery operation. The estimation effects of the proposed model are verified under hybrid pulse power characterization and dynamic stress test working conditions. The maximum error values obtained are 0.037 % and 0.336 %, respectively, thus achieving high-precision estimation. The proposed method is adaptive to real-time battery management applications, laying a foundation for robust state estimation of lithium-ion batteries used in urban transportation electric vehicles.

Investigation of ultrasonic vibration assisted orthogonal turning under dry and minimum quantity lubrication conditions and performing sustainability analyses

Recently, increasing pressure on industries to innovate and adopt sustainable techniques has sparked investigations into machining strategies that have minimal carbon emissions, minimal energy consumption by machine tools, and low machining costs. The primary aim of this paper is to identify the machining conditions that can fulfill the criteria of clean production and sustainable chip forming for low-alloy steel which is extensively used in the many industrial field. To achieve this goal, experiments were done under both dry and Minimum Quantity Lubrication (MQL) conditions, aided by ultrasonic vibration assisted hybrid machining conditions at various cutting speeds (100 and 140 m/min) and uncut chip thicknesses (0.1, 0.14, and 0.18 mm). The study was conducted in two stages. In the first part, machining experiments were performed afterward, cutting forces, cutting temperature, chip morphology, surface roughness, and texture were examined. The second part of this study evaluates the sustainability of various machining parameters and cutting environments. The analysis of economic feasibility or rather the total machining costs were estimated by evaluating the costs incurred due to investment of machine tools, lubricant/coolant delivery systems and ultrasonic vibration system costs, waste processing and management costs, cutting tool and labor costs, cost of cutting fluid and energy consumed. On the other hand, environmental viability was evaluated by estimating the carbon emissions (CE) due to the power consumed, material utilization (i.e., the cutting tool and cutting fluid), and the waste processing and management (i.e., disposal of cutting tool, cutting fluid and chip recycling). Results indicate that both dry and MQL ultrasonic-assisted machining processes resulted in improved chip breakability, and produced short-comma chips. Additionally, it reduced cutting force by 28% and the cutting temperature was lowered by 30 %. Besides, Minimum Quantity Lubrication + Ultrasonic Vibration Assisted Machining (MQL + UVAM) hybrid method prevented surface defects and reduced average surface roughness by 24%–35% compared to dry conditions. While utilizing the MQL + UVAM method, the overall machining cost decreased in the range of about 20–30 % as compared to dry and dry + UVAM conditions. From an environmental standpoint, the MQL + UVAM hybrid machining process was found to be a significant contribution to the overall CE due to use of cutting fluids and increased machine tool utilization. This paper contributed to the understanding of sustainability and cleaner production to mitigate CO2 emissions by using the MQL + UVAM hybrid method.

Proposal of a Combined AHP-PROMETHEE Decision Support Tool for Selecting Sustainable Machining Process Based on Toolpath Strategy and Manufacturing Parameters

Sustainable manufacturing technologies are the new challenge faced by enterprises, industries, and researchers. The development of a sustainability-based assessment method considering the environmental and economic impacts is crucial to realize viable manufacturing. However, few studies have addressed environmental economics and social flows using a common perspective. Mechanical machining is one of the most-used manufacturing techniques. The overall ecological, economic, and social footprint requires accurate and effective estimation and optimization. Several studies have addressed this issue by examining the entire process of machining, but sustainability flows for machining parameters and toolpaths have remained relatively unexplored. The lack of systematic assistance tools bridging the gap between decision-maker preferences and the three sustainability pillars—economic, social, and environmental—has impeded the widespread adoption of sustainable machining practices. To this end, this paper proposes an integrated approach to the decision-making problem that combines the Analytical Hierarchy Process (AHP) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for selecting a sustainable machining strategy. The sustainability criteria are driven by manufacturing process parameters commonly employed and regulated during the manufacturing phase. This includes toolpath strategies as a qualitative input factor and manufacturing parameters such as cutting speed, feed rate, depth of cut, and stepover as quantitative input factors, affirming the practical applicability of the method in industrial contexts. New fundamental methods are also presented for selecting the most efficient machining parameters and toolpaths according to the weights assigned to each ecological, social, and economic footprint by the decision-maker (the manufacturer or production manager). In this way, sustainable machining strategies in the manufacturing industry will be strengthened in integrity. In a case study of part-end milling, both manufacturing parameters and toolpath strategies are considered to establish sustainable feature-based machining decisions.

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

This research aims to predict the cost and energy consumption associated with pocket and groove machining using the hybrid particle swarm optimization-artificial neurons network (PSO-ANN) algorithm and the response surface method (RSM). A parametric study was conducted to determine the best predictions by adjusting the swarm population size (pop) and the number of neurons (n) in the hidden layer. The results showed that machining strategies and sequences can have a significant impact on energy consumption, reaching a difference of 99.25% between the minimum and maximum values. The cost ( Ctot) and energy consumption ( Etot) values with the PSO-ANN algorithm increased significantly by 99.99% and 92.41%, respectively, compared to the RSM model. The minimum mean square error values for Etot and Ctot with the PSO-ANN models are 3.0499 × 10−5 and 4.6296 × 10−10, respectively. This study highlights the potential of the hybrid PSO-ANN algorithm for multi-criteria prediction and highlights the potential for improved machining of 2017A alloy.

Efficient cutting path planning for a non-spherical tool based on an iso-scallop height distance field

Iso-scallop height machining means, when machining a freeform surface, the scallop height between any two neighboring tool paths on the surface will be a constant (i.e., the given threshold), which is preferable among various freeform surface machining strategies due to its high machining efficiency as well as better machine tool’s dynamics. However, all the existing iso-scallop height path planning methods pertain to only the ball-end or flat-end types of tools. In recent years, the non-spherical cutting tool has become more and more popular, especially for five-axis machining of complex freeform surfaces, majorly owing to its non-constant curvature which can be utilized to adaptively fit the tool to the surface to both avoid the local gouging and enlarge the cutting width. However, there have been no reported works on iso-scallop height five-axis tool path generation for a non-spherical tool, and, in this paper, we present one. Specifically, we first define and construct two fields on the surface to be machined-the collision-free tool orientation field (vector) and the iso-scallop height distance field (scalar). The iso-lines of the scalar field and their associated tool orientation field vectors then naturally serve as potential iso-scallop height five-axis tool paths, and we present a propagation-based algorithm to construct the desired tool path from the iso-lines. The computer simulation and physical cutting experiments confirm that everywhere on the surface, except maybe near the saddle curves of the scalar filed, the scallop height is exactly the given threshold. By adding the saddle curves as extra tool paths, the final machined surface then is assured of the required scallop height requirement.

Analysis of Workbench Flatness Measurement and Product Result of 3 Axis CNC Router Machine

The development of technology in the industrial sector is increasing rapidly, especially the application of computers in the field of machinery. Demands from consumers who want good quality workpieces, precision, completed in a short time and in large quantities, will be easier to work with a CNC Router machine. Surface flatness is an absolute thing that must be considered in the machining process, especially the 3 Axis CNC Router machine. This type of research is an experimental research, using Dutch teak wood as a workpiece with a height of 15 mm, a length of 100 mm and a width of 80 mm, the machining process uses a 3 Axis CNC Router Machine. The flatness of the workbench is first measured, then the product is prepared with a depth of 2 mm in the product area. The research uses a variation of the spindle speed of 600 rpm, 800 rpm and 1000 rpm. After that, measurements are taken to obtain the flatness value. Measuring flatness using a Dial indicator measuring instrument with accuracy (1µm). The best product flatness test or the lowest value is (32.00 µm) obtained from the spindle speed of 1000 rpm and the most uneven product evenness is (56.00 µm) obtained from the spindle speed of 600 rpm, knowing the effect of the flatness value of the workbench on the product flatness value 3 Axis CNC Router Machine, that the flatter the workbench, the flatter the product results. The results of the research can be used to refine machining strategies to achieve optimal machining.

Opportunities and challenges in machining of aluminium metal matrix composites using electrical discharge machining

The machining of aluminium metal matrix composites (aluminium MMCs) presents a dynamic landscape of opportunities and challenges, with electrical discharge machining (EDM) emerging as a promising technique. This review article systematically navigates the intricate interplay between AMMCs’ unique characteristics and the EDM process. Through a comprehensive analysis of existing literature, we uncover the underlying mechanisms that govern the machining dynamics of AMMCs, exploring their compositional variations, reinforcement distribution and thermal properties. The article delves into the opportunities offered by EDM, such as precision machining of intricate geometries, inherent thermal control and the ability to navigate the varying hardness levels in AMMCs. Furthermore, the review addresses the challenges that arise, including electrode wear, surface integrity preservation and the influence of reinforcement particles on material removal rates. The authors highlight the role of parameter optimisation in achieving desirable machining outcomes, underlining the need to strike a balance between material removal efficiency and surface finish quality. Additionally, environmental considerations in EDM of AMMCs are discussed, emphasising sustainable machining strategies in line with contemporary ecological concerns. As the field of advanced materials and manufacturing continues to evolve, this review article provides valuable insights for researchers, engineers and industries seeking to harness the potential of EDM in machining AMMCs. By navigating the intricate landscape of opportunities and challenges, this article contributes to a holistic understanding of the nuanced relationship between EDM and AMMCs, laying the foundation for future advancements in precision machining of these composite materials.

Unveiling Deep Learning Insights: A Specialized Analysis of Sucker Rod Pump Dynamographs, Emphasizing Visualizations and Human Insight

This study delves into the heightened efficiency and accuracy of 11 deep learning models classifying 11 dynamograph classes in the oil production sector. Introducing a novel framework with the Grad–CAM method, we address the “black box” issue, providing transparency in the models’ decision-making processes. Our analysis includes a comparative study with human experts, revealing a comprehensive understanding of both machine and human interpretive strategies. Results highlight the notable speed and precision of machine learning models, marking a significant advancement in rapid, reliable dynamograph classification for oil production decision-making. Additionally, nuanced findings in the model’s diagnostic accuracy reveal limitations in situations featuring the simultaneous occurrence of multiple pump issues. This underscores the need for additional features and domain-specific logic to enhance discernment and diagnostic precision in complex scenarios. The exploration of qualitative aspects distinguishes interpretive approaches, highlighting strengths and limitations. Machines, driven by algorithmic patterns and data processing, excel in rapid identification, albeit with occasional misclassifications. In contrast, human experts leverage experience and domain-specific knowledge for nuanced interpretation, providing a comprehensive understanding of both quantitative metrics and qualitative nuances. In conclusion, this study not only demonstrates the accelerated and enhanced accuracy of dynamograph classification by machine learning models compared to junior and medior domain experts, but also provides valuable insights into specific features and patterns guiding the decision-making process. This understanding allows continuous refinement, combining machine speed with human understanding for improved results in oil production. The potential for further studies and improvements in this domain is substantial.

MAUNext: a lightweight segmentation network for medical images

Objective. The primary objective of this study is to enhance medical image segmentation techniques for clinical research by prioritizing accuracy and the number of parameters. Approach. To achieve this objective, a novel codec-based MAUNext approach is devised, focusing on lightweight backbone design and the integration of skip connections utilizing multiscale, attention mechanism, and other strategic components. The approach is composed of three core modules: a multi-scale attentional convolution module for improved accuracy and parameter reduction, a collaborative neighbourhood-attention MLP encoding module to enhance segmentation performance, and a tiny skip-connected cross-layer semantic fusion module to bridge the semantic gap between encoder and decoder. Main results. The study extensively evaluates the MAUNext approach alongside eight state-of-the-art methods on three renowned datasets: Kagglelung, ISIC, and Brain. The experimental outcomes robustly demonstrate that the proposed approach surpasses other methods in terms of both parameter numbers and accuracy. This achievement holds promise for effectively addressing medical image segmentation tasks. Significance. Automated medical image segmentation, particularly in organ and lesion identification, plays a pivotal role in clinical diagnosis and treatment. Manual segmentation is resource-intensive, thus automated methods are highly valuable. The study underscores the clinical significance of automated segmentation by providing an advanced solution through the innovative MAUNext approach. This approach offers substantial improvements in accuracy and efficiency, which can significantly aid clinical decision-making and patient treatment.

Effects of Machining Parameters and Tool Reconditioning on Cutting Force, Tool Wear, Surface Roughness and Burr Formation in Nickel-Based Alloy Milling.

Nickel-based superalloys are among the most difficult materials to machine because they have high thermal strength, they are prone to hardening, carbides severely abrade the tool, and they have very poor thermal conductivity. Slot milling is a specific issue as it is characterized by rapid tool wear and frequent tool breakages. This is why reconditioned tools are frequently employed in industrial environments, as they can considerably decrease the expenses associated with tools. The chosen machining strategy also plays a crucial role in the tool's lifespan and the quality of the machined surface, making it essential to select the appropriate strategy. Hence, the authors have opted for two conventional trochoidal strategies, namely the circular and swinging toolpath, along with a contemporary toolpath known as the Autodesk Inventor HSM Adaptive strategy. The authors investigated the effects of technological parameters and toolpaths on cutting forces, tool wear, surface roughness and burr formation on machined edges. The results show that lower cutting parameters and adaptive strategies lead to the smallest tool loads, tool wear, the best quality of surface roughness and burr formation on machined edges.

Analysis of torque controlling strategies of interior permanent magnet synchronous machine in hybrid electric vehicle

Significance of this research is based on torque controlling strategies in hybrid electric vehicle. The traction motor of a hybrid electric commercial vehicle must have a high power density-to-weight ratio and excellent torque at extremely low speeds. A proportional-integral controller estimates the high-resolution rotor position based on the difference between actual as well as reference output power. This technique can correct for hall position sensor installation error and also accurately estimate rotor position. In this study, a current proportional integral controller is also used to determine the permanent magnetic flux linkage. Experiments are used to test other key metrics such as d-axis and q-axis inductances, stator resistance, and energy loss. The measured parameters are kept in lookup tables that span the whole operating range at various current levels. A maximum torque per ampere control methodology, paired with the feed forward parameter iteration method, may be used to produce accurate and efficient torque control based on these precise parameters. The standard driving cycle performance requirements of the modelled Federal Test Procedure vehicle is analysed in the article for understanding the real model requirements of the vehicle. The efficacy of the proposed methodology using Matlab/Simulink environment is analysed. Both modelling and experimental findings validate the efficacy, torque ripple, stator and rotor flux, sensitivity of the vehicle parameter as well as the computational complexity of the proposed methodology.

Investigation of machining quality indicators and effect of tool geometry parameters during the machining of difficult-to-machine metal

Producing high-quality surfaces from hard and brittle metals presents a formidable challenge that calls for a meticulous machining strategy and an effective cutting tool. The mechanism of tool failure, chip formation behavior, and micro-morphology of the machined surface are crucial factors in determining a correlation between the used parameters and the machined surface quality. Among the metrics employed to assess the effectiveness of the tool. However, the determination of optimum machining parameters requires the repetition of precise experimental investigations with every change in the cutting conditions. The current research work proposes a set of suggestions for choosing the optimal tool parameters based on the micro-morphological characteristics of the tool and the machining performance indicators. A WC-Co ball end mill tool was used for the machining of an H13 tool steel workpiece to establish the relationship between changes in tool geometrical parameters and the machining performance indicators. Tool geometry with a rake angle of 3°, a clearance angle of 10°, and a helix angle of 32° was found to have the least wear band length at 56 μm. Moreover, the impact of tool geometry on the quality of the surface produced through machining was also investigated.

Surface morphology improvement and residual stress distribution of SiCp/Al composites using high-power fiber laser-CNC milling cooperative machining strategy

This work proposes a novel high-power fiber laser-CNC milling cooperative strategy for cutting SiCp/Al composites, which aims to machine MMCs with high efficiency and limited damage, and achieve compressive residual stress distribution within machined surface layer in order to subsequently improve service performance of composite components. Experimental results shows that the cooperative machining strategy has a sufficient improvement in machining efficiency with limited surface defects compared to conventional methods. Completely removal of laser cutting induced surface defects, including striations, combustion and cataphracted dross, was achieved under high speed milling with small cutting depth down to 0.2 mm. Additionally, it was found that the roughness of final machined surface was impacted by the parameters of both laser cutting and milling process. The surface roughness decreased by 35 % with the laser power increased from 4.0 kW to 4.5 kW, while decreased by 60 % when milling speed decreased from 180 m/min to 90 m/min. Compressive residual stress was prevalent within the surface layer during fiber laser-CNC milling cooperative machining process. The experimental findings in this work verified the feasibility and advantages of the cooperative machining strategy for processing difficult-to-cut metal matrix composites.

A 3.02 pJ/Bit 3T-APS-Based In-Sensor Strong PUF Featuring Near-100% Hardware Reuse Ratio and High Resilience to Machine Learning Attacks

In this brief, we present an energy-efficient in-sensor strong physical unclonable function (PUF) based on the static entropy of 3-transistor active pixel sensor (3T-APS) structure that is widely-adopted in the CMOS image sensors (CIS). With ultra-small silicon area overhead of 0.25% dedicated to the added bias circuitries for column line and digital comparator for PUF bit generation, traditional 3T-APS-based CIS can be well-reused to construct the proposed strong PUF for authenticating the recorded images or videos. Taking advantage of the inherent high-resolution pixel array of CIS, the proposed strong PUF significantly extends the challenge-response-pair (CRP) space of the previous implementations. In addition, large CRP nonlinearity can be obtained by biasing the selected 3T-APS pixels at the subthreshold region, which further increases the complexity of the above extended CRP space and its resilience to various machine learning (ML) attacks. Moreover, the proposed strong PUF design is validated by the extensive measurement results of the prototype chips fabricated using a standard 65-nm CMOS process. Featuring a low energy consumption of 3.02 pJ/bit and a high area efficiency of 1.64×1033 bit/F2, prediction error of 49.1% 51.84% can be achieved under various ML attacks based on artificial neural network, logistic regression, support vector machine and covariance matrix adaptation evolution strategy, with the number of adopted CRPs for training up to 10 million.

Effects of ultrasonic vibration assisted milling with laser ablation pretreatment on fatigue performance and machining efficiency of SiCf/SiC composites

Due to the poor machinability of SiCf/SiC parts in machining, many problems are caused, such as low machining efficiency, poor machining quality and high processing cost, which seriously limit its manufacturing and application. A novel process ultrasonic vibration assisted milling with laser ablation pretreatment (UVAMLAP) was proposed to optimize the fatigue performance and machining efficiency of SiCf/SiC parts. This process was used to fabricate specimens, which were then tested for tensile strength and fatigue performance. The results show that UVAMLAP could enhance surface quality, and increase the tensile strength and residual tensile strength of the sample by 9.4% and 13.5%. This process can avoid damage aggravation in the initial stage of failure, weaken matrix fracture and interface debonding velocity, and reduce fatigue performance degradation caused by machining damage. In addition, comprehensive evaluation based on multi-dimensional indicators such as milling quality, machining efficiency and tool cost for machining strategy was carried out by taking tensile sample machining as an example. The UVAMLAP process can not only improve the machined surface quality, but also reduce the machining time by 31.3% and the tool cost by 75%. Therefore, UVAMLAP provides a feasible process scheme for high-efficiency and low-damage machining of SiCf/SiC parts.

Preliminary experimental study on a servo control strategy for dry electrical discharge machining

Preliminary experimental study on a servo control strategy for dry electrical discharge machining

Investigation of stochastic toolpath strategy in three-axis ball-end milling of 2D and free-form surfaces

The machining of free-form components by ball-end milling inherently produces surface error in the form of scallops. The objective of any free-form toolpath strategy is to balance productivity while minimizing scallop height to reduce surface error. Conventional machining strategies produce repeatable material patterns (constant scallop height) that may limit workpiece function in areas such as lubricity, directional anisotropy, and aesthetic appearance. These strategies also involve steady-state cuts, which allow accumulation of temperature, restrict the permissible depth and speed. In the present paper a novel complex stochastic toolpath strategy has been proposed that comprises pseudo-random circular contours concatenated into a smooth path. The approach enables continuous variation of chip load, force, and direction, and avoids conditions of continuous, periodic high cutting loads and heat accumulation. Based on initial testing, it has been observed that stochastic toolpaths are longer than conventional toolpaths. However, a decrease in average cutting loads enable reduction in cutting time with feed optimization. Additionally, the proposed strategy resulted in lower scallop height than conventional machining, thereby improving surface condition.

An integration of PSO-ANN and ANFIS hybrid models to predict surface quality, cost, and energy (QCE) during milling of alloy 2017A

An alarming pace of increase in worldwide energy consumption is being caused by population expansion and economic development, particularly in emerging market countries. Due to their efficiency and reproducibility in accomplishing high-precision machining, CNC machine tools are widely employed in most metal machining processes. The use of simple machining features to search for the assignment of cutting parameters and the machining process on the output variables is limited since a part, in reality, can contain complex interacting features. Therefore, this study focuses on pocket/groove features by integrating grey relational analysis (GRA) and hybrid PSO-ANN and ANFIS algorithms to optimize and predict surface quality, cost and energy consumption (QCE). Taking into account the population size of the swarm (pop) and the number of neurons (n) in the hidden layer, a parametric study was carried out to find the best prediction using the hybrid algorithm PSO-ANN. This study reported the highest trained correlation values (R2) for all output variables (greater than 0.97%). The study shows that the assignment of machining strategies and sequences on energy consumption can reach 99.25% between the minimum and maximum values. The mean square error (MSE) data demonstrates that the PSO-ANN model is effective. Indeed, an MSE improvement of 99.84%, 99.87%, and 97.62% has been demonstrated in terms of Etot, Ctot, and Ra, respectively, of the PSO-ANN model compared to ANFIS. This study reveals the potential of the PSO-ANN hybrid for multi-criteria prediction (quality, cost, and energy: QCE) by comparing it with the ANFIS model.