Slow Machine Research Articles

Machining and wear rates in EDM of D2 steel: A comparative study of electrode designs and materials

Electrical discharge machining (EDM), especially the die-sinking variant, is a well-known slow machining process among non-conventional manufacturing processes. Different approaches are being researched to improve EDM performance characteristics especially increasing its material removal rate (MRR) and reducing the tool wear rate (TWR). This study aims to improve the EDM process performance through modifications in tool design and employing different tool materials. The performance of conventional EDM tool is compared with different tool designs while machining D2 steel. Moreover, tool designs are employed on different tool materials including copper (Cu), copper-tungsten alloy (CuW), and graphite (Gr) to investigate the combined effect of tool designs and materials on important performance indicators of EDM die-sinking. The machining time (MT), material removal rate (MRR), lateral tool wear (TWR_L), and diametric tool wear (TWR_D) are selected as the response measures. The modified tool designs reduce the machining time by 45%. Likewise, Cu and CuW remarkably improve the material removal rate by 80%. Similarly, the lateral and diametric tool wear of modified designs are less than the wear corresponding to conventional design. Microscopic analysis revealed two wear zones which are named as aggressive sparking zone and mild sparking zone. All materials (Cu, CuW, and Gr) with the modified tool designs show significantly less sparking zone compared with the conventional design. Moreover, the most appropriate combination of tool design and material is proposed to achieve better EDM performance.

Development and Optimization of an Offset Spiral Tooth Fertilizer Discharge Device

Due to factors such as a small amount of fertilizer application during rice topdressing and slow machine speed, the ordinary fertilizer discharge device fails to distribute the fertilizer uniformly and accurately as required, making it difficult to meet the needs of precise rice topdressing. This research focuses on the development of an offset spiral tooth fertilizer discharge device suitable for rice topdressing. The study analyzes the amount of fertilizer discharged in one cycle, the fertilizer force, and the motion of the fertilizer particles. In order to enhance the uniformity of the fertilizer discharge device at a low speed and small volume, the discrete element method is employed to conduct experimental research on the key structural parameters that affect the one-cycle amount of discharged fertilizer and the dynamics of the fertilizer discharge device. Through single-factor tests, it was found that the angle, height, number of spiral teeth, and diameter of the fertilizer discharge wheel are closely associated with the fertilizer discharge performance. To further investigate the impact of these four parameters on the fertilizer discharge performance, a regression combination test of the four factors is conducted based on the range optimized by the single-factor tests. A multi-objective mathematical model, considering the key parameters of fertilizer uniformity coefficient, one-cycle amount of fertilizer, and fertilizer discharge torque, is established at three speeds: 20, 55, and 90 rpm. The response surface method is utilized to analyze the influence of the interaction factors on the fertilizer discharge performance. The optimal combination of the key structural parameters was determined as follows: spiral tooth angle of 35.42°, tooth height of 9.02 mm, discharge wheel diameter of 57.43 mm, and tooth amount of 9.37. The bench test results of the device, using the optimal parameter combination and a fertilizer discharge speed of 0–90 rpm, were obtained for four commonly used rice fertilizers. The maximum variation coefficient of fertilizer discharge was found to be 10.42%. The one-cycle amount of fertilizer discharge was measured to be 19.82 ± 0.72 (A Kang), 17.20 ± 0.69 (Ba Tian), 20.34 ± 0.54 (Yaran), and 14.51 ± 0.44 (granular urea). The fertilizer discharge torque remained stable. These results indicate that the achieved optimization meets the precise fertilizer application requirements and can provide technical support for precise topdressing operations.

Simultaneous electrochemical and electrodischarge machining process: An approach to sustainable manufacturing

Manufacturing of Net Finished Shape (NFS) microholes in Inconel 718 materials with faster machining rate can offer significant benefits in terms of reduced waste, improved efficiency, and lower production costs, which are essential for sustainable manufacturing. However, the existing manufacturing method, namely electrochemical machining (ECM), results in slow machining with uncontrolled dissolution, leading to increase production time, energy consumption, and carbon emission associated with the manufacturing process. The current manuscript investigates the reason attributes for these problems through potentiodynamic polarization test. Subsequently, a new machining process known as Simultaneous electrochemical and electrodischarge machining (SECEDM) has been proposed. This frugal manufacturing-based machining process offers high machining rate (MR) and better overall hole circularity (OHC) through the synergistic effect of anodic dissolution and electrochemical discharges. The obtained results reveal that the SECEDM process offers nearly 261 % and 141 % higher MR and 1.23 % and 3.78 % better OHC than electrochemical machining (ECM) and electrodischarge machining (EDM), respectively. Moreover, SECEDM outperforms the simultaneous micro-EDM and micro-ECM process (SEDCM) process by seven times in machining rate and promotes localized dissolution than ECM. Overall, SECEDM's benefits, including its high machining rate, superior surface quality, sustainability, eco-friendliness, and elimination of the need for secondary finishing processes, make it an ideal candidate for the next-generation manufacturing process.

Surface modeling and influencing factors for microlens array by slow tool servo machining

Microlens arrays (MLAs) are widely used in a variety of important fields due to their unique optical properties. Slow tool servo machining (STSM)technology is a manufacturing technology for machining non-rotationally symmetric surfaces. In order to solve the problems of many influencing factors of STSM machining quality, high experimental trial and error cost, and mutual limitation of accuracy and efficiency, on the basis of analyzing and determining reasonable tool trajectories, a microlens array machining surface morphology prediction model is established from the theoretical point of view, and the prediction model is optimized by combining the two-dimensional Gaussian filtering method, and the surface accuracy and roughness are used to evaluate the processing quality. The prediction model is used to analyze the influence, like feed per revolution, radius of tool tip, and tool setting error on the surface quality. After the Microlens machining experiments, the results are matched with the simulation, which shows us the model is correct, and the MLAs were machined by the optimal parameters with the surface shape error PV = 0.741 μm, surface roughness Sa = 22 nm, Sq = 28 nm, and Sz = 59 nm.

Transition Waste Optimization for Coded Elastic Computing

Distributed computing, in which a resource-intensive task is divided into subtasks and distributed among different machines, plays a key role in solving large-scale problems. Coded computing is a recently emerging paradigm where redundancy for distributed computing is introduced to alleviate the impact of slow machines (stragglers) on the completion time. We investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. This is motivated by recently available services in the cloud computing industry (e.g., EC2 Spot, Azure Batch) where low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted on short notice. Our contributions are three-fold. We first introduce a new concept called transition waste that quantifies the number of tasks existing machines must abandon or take over when a machine joins/leaves. We then develop an efficient method to minimize the transition waste for the cyclic task allocation scheme recently proposed in the literature (Yang et al. ISIT'19). Finally, we establish a novel solution based on finite geometry achieving zero transition wastes given that the number of active machines varies within a fixed range.

Effect of Machining Conditions on Temperature and Vickers Microhardness of Chips during Planing

For the machining of long and narrow surfaces and when processing multiple pieces, planing technology is used, the productivity of which can be higher than that of milling, although it is relatively slow machining. The article aims to study the degree of influence of the geometry of the tool (the angle of cutting-edge inclination and the angle of the tool-orthogonal rake), as well as the cutting conditions (cutting depth and cutting speed) on the chip characteristics (temperature and microhardness) in orthogonal and oblique slow-rate machining of steel 1.0503 (EN C45). The experiments were carried out on specially prepared workpieces designed for immediate stopping of machining. The results of the experiments were statistically processed, and behavioural models were created for temperature and Vickers microhardness of chips for individual combinations of factors. The obtained dependencies revealed how the geometry of the cutting tool and the cutting conditions affect the temperature and microhardness in the cutting area and at the same time allowed the best conditions for both orthogonal and oblique machining to be set up.

Soft BIBD and Product Gradient Codes

Gradient coding is a coding theoretic framework to provide robustness against slow or unresponsive machines, known as stragglers, in distributed machine learning applications. Recently, Kadhe et al. (2019) proposed a gradient code based on a combinatorial design, called balanced incomplete block design (BIBD), which is shown to outperform many existing gradient codes in worst-case adversarial straggling scenarios. However, parameters for which such BIBD constructions exist are very limited (Colbourn and Dinitz, 2006). In this paper, we aim to overcome such limitations and construct gradient codes which exist for a wide range of system parameters while retaining the superior performance of BIBD gradient codes. Two such constructions are proposed, one based on a probabilistic construction that relax the stringent BIBD gradient code constraints, and the other based on taking the Kronecker product of existing gradient codes. The proposed gradient codes allow flexible choices of system parameters while retaining comparable error performance.

Experimental study on surface characteristics in μED milling of Al6063%-5%B4C-5%ZrSiO4 composite using TOPSIS method

Al6063 composites are widely used in automobile, aerospace and biomedical industries due to their excellent mechanical properties. Machining of micro channels in composites is difficult for conventional machining process due to presence of hard reinforcement. Materials with base metal. Micro Electrical discharge (μED) milling is popular micromachining technique for machining simple, intricate shapes and microchannels on any conductive material. However, it a slow machining process, the identification of optimum condition has become wide research area in μEDM. The present work aims to study the influence of process variables namely voltage, spindle speed and threshold on machining characteristics of μED milling of Hybrid Metal Matrix Composites (HMMCs). Experimental trials are carried out with copper electrode at different parametric condition. Al6063%-5%B4C-5%ZrSiO4 composite was fabricated using stir casting method and experimental runs were designed using general full factorial method. The significant parameters are identified using Analysis of Variance (ANOVA) and the ideal machining conditions for multi-response are determined using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The morphology of machined surface with best and worst conditions is examined using SEM. Results indicated that the voltage and threshold are the influencing parameters for considered response indicators. Recast layer thickness seems to be low with best machining conditions as compared to worst conditions. Increase in voltage and threshold increases the Material Removal Rate (MRR) and decreases the Electrode Wear Rate (EWR). Surface finish is better when the lower order of capacitance and voltage is used. MRR is increased by 48% with best machining conditions compared to worst machining condition.

Dynamic Byzantine Broadcast in Asynchronous Message-Passing Systems

The <i>reconfiguration</i> problem is considered a key challenge in distributed systems, especially in <i>dynamic</i> asynchronous message-passing systems. To keep the data reliability and availability in long-lived systems, any protocols should support reconfigurations, to dynamically add resources, or remove old and slow machines with newer faster ones. Previous results in reconfigurations either rely on consensus, or study the problem restricted to crash failures only. However, it is difficult to argue that real-world systems experience crash failures only. In this paper, we study the dynamic reconfiguration problem in fully asynchronous message-passing systems with Byzantine faults. We first specify dynamic Byzantine broadcast, and then specify a clean and explicit liveness condition. We show that dynamic Byzantine broadcast is solvable by presenting a dynamic Byzantine consistent broadcast algorithm and a dynamic Byzantine reliable broadcast algorithm.

Research on Slow Tool Servo Machining of Complicated Planar Microlens Array

Research on Slow Tool Servo Machining of Complicated Planar Microlens Array

Impact of Gaussian Noise for Optimized Support Vector Machine Algorithm Applied to Medicare Payment on Raspberry Pi

A relatively large dataset coupled with efficient but computationally slow machine learning algorithm poses a great deal of challenge for Internet of Things (IoT). On the contrary, Deep Learning Neural Networks (DLANNs) are known for good performances in terms of accuracy, but by nature are computationally intensive. Based on this argument, the purpose of this article is to apply a pipelined Support Vector Machine (SVM)) learning algorithm for benchmarking public health data using Internet of Things (IoT). Support Vector Machine (SVM) a very good performing machine learning algorithm but has constraints in terms of huge training time and its performance is also susceptible to noise. The applied software pipelined architecture to SVM was to minimize its computational time under a resource constrained device like raspberry pi. It was tested with a medicare dataset with Gaussian noise to assess the impact of noise. The classification results of Total Medicare Standardized Payment Amount obtained indicated that the proposed pipelined SVM model was optimal in performance compared to DLANN model by 79.74% in terms of computational time. Also the performance of SVM in terms of area under curve (AUC) was better compared to other models and outscored Logistic Regression by 7.2%, and DLANN model by 22.65%.

Subregional Process Method With Variable Parameters Based on a Potential Field in Slow Tool Servo Machining for a Complex Curved Surface

Abstract Precision turning with slow tool servo (STS) plays an increasingly important role in advanced manufacturing nowadays. However, it is difficult to promote machining quality for surfaces with local complex geometric features by the conventional global machining method. Hence, a subregional processing method in STS is proposed. First, the continuous equipotential line is taken to express the local geometric feature. Thus, a potential field is built, where the surface could be divided into subregions. Then, a subregional toolpath with variable feed rates is generated by the field and stitched to ensure the feeding motion stability of X-axis. Finally, the surface is subdivided for variable spindle speed planning, considering the feeding motion stability of Z-axis. It is found that the profile arithmetic average error reduces by 31.58% with the proposed method compared with that with the conventional method and the machining time is shortened by 41.00%. Thus, it is proved that the new processing method effectively promotes machining quality and efficiency.

Transfer Learning for Test Time Reduction of Parameter Extraction in MEMS Accelerometers

Parameter extraction during the final test of MEMS sensors poses a highly time-critical challenge. The progressing miniaturization, test stimuli and structural complexity lead to nonlinear couplings and inhomogeneity in system differential equations, which cannot be linearized and are therefore dependent on either slow numerical solution methods or machine learning algorithms requiring many labeled data. A transfer learning approach is presented making use of high complexity ASIC-MEMS models for Monte-Carlo generation of simulated devices, which are used to pre-train neural networks on the task of parameter extraction. In a first step, it is shown that for both, high quality factor and low quality factor systems, neural networks are not only able to fit the relation between time-series recorded during final testing and the two performance parameters natural frequency and damping factor but also to extract Brownian noise, mass, and epitaxial layer thickness. Subsequently, it is shown that the transfer learning approach is particularly useful for the determination of parameters, which cannot be measured directly during the final test and for which it is expensive to record labeled data like Brownian noise for systems in a harsh production test environment. If only very few labeled samples are available – in the performed experiments, 25 devices under test were sufficient - the transfer learning approach outperforms a neural network purely trained on measured data. These findings emphasize the practical advances of the transfer learning approach and motivate the evaluation of further applications in the field of parameter extraction in MEMS sensors. [2020-0375]

Quantitative monitoring of the environmental hydrogen embrittlement of Al-Zn-Mg-based aluminum alloys via dynamic hydrogen detection and digital image correlation

In this study, a novel analytical system was developed to monitor the environmental hydrogen embrittlement of Al-Zn-Mg-based aluminum alloys dynamically and quantitatively under atmospheric air pressure. The system involves gas chromatography using a SnO2-based semiconductor hydrogen sensor, a digital image correlation step, and the use of a slow strain rate testing machine. Use of this system revealed that hydrogen atoms are generated during the plastic deformation of Al-Zn-Mg alloys caused by the chemical reaction between the water vapor in air and the alloy surface without oxide films. Digital image correlation also clarified that the generated hydrogen atoms caused numerous localized grain boundary cracks on the specimen surface, resulting in a localized grain boundary fracture. The amount of hydrogen atoms evolved from the embrittled fracture surface was 2.7 times as high as that from the surface without embrittlement.

EDM of D2 Steel: Performance Comparison of EDM Die Sinking Electrode Designs

Electric discharge machining (EDM) of tool steel (D2 grade) has been performed using different tool designs to produce through-holes. Machining performance has been gauged with reference to machining time, hole taper angle, overcut, and surface roughness. Inaccuracies and slow machining rate are considered as the most common limitations of the electric discharge machining (die-sinking). Traditionally, a cylindrical tool is used to form circular holes through EDM. In this study, the hole formation is carried out by changing the tool design which is the novelty of the research. Two-stage experimentation was performed. The newly designed tools substantially outperformed a traditional cylindrical tool, especially in terms of machining time. The main reason for the better machining results of modified tools is the sparking area that differs from the traditional sparking. Comparing against the performance of a traditional cylindrical tool, the newly designed tools offer a considerable reduction in the machining time, radial overcut, and roughness of the inside surfaces of machined holes, amounting to be approximately 50%, 30.6%, and 38.7%, respectively. The drop in the machining time along with a condensed level of radial overcut and surface roughness can shrink the EDM limitations and make the process relatively faster with low machining inaccuracies.

Health Assessment of High-Speed Train Running Gear System under Complex Working Conditions Based on Data-Driven Model

It is very important for the normal operation of high-speed trains to assess the health status of the running gear system. In actual working conditions, many unknown interferences and random noises occur during the monitoring process, which cause difficulties in providing an accurate health status assessment of the running gear system. In this paper, a new data-driven model based on a slow feature analysis-support tensor machine (SFA-STM) is proposed to solve the problem of unknown interference and random noise by removing the slow feature with the fastest instantaneous change. First, the relationship between various statuses of the running gear system is analyzed carefully. To remove the random noise and unknown interferences in the running gear systems under complex working conditions and to extract more accurate data features, the SFA method is used to extract the slowest feature to reflect the general trend of system changes in data monitoring of running gear systems of high-speed trains. Second, slowness data were constructed in a tensor form to achieve an accurate health status assessment using the STM. Finally, actual monitoring data from a running gear system from a high-speed train was used as an example to verify the effectiveness and accuracy of the model, and it was compared with traditional models. The maximum sum of squared resist (SSR) value was reduced by 16 points, indicating that the SFA-STM method has the higher assessment accuracy.

Fundamental Limits of Approximate Gradient Coding

In the distributed graident coding problem, it has been established that, to exactly recover the gradient under s slow machines, the mmimum computation load (number of stored data partitions) of each worker is at least linear (s + 1), which incurs a large overhead when s is large [13]. In this paper, we focus on approximate gradient coding that aims to recover the gradient with bounded error ε. Theoretically, our main contributions are three-fold: (i) we analyze the structure of optimal gradient codes, and derive the information-theoretical lower bound of minimum computation load: O(log(n)/log(n/s)) for ε = 0 and d ≥ O(log(1/ε)/log(n/s)) for ε &gt; 0, where d is the computation load, and ε is the error in the gradient computation; (ii) we design two approximate gradient coding schemes that exactly match such lower bounds based on random edge removal process; (iii) we implement our schemes and demonstrate the advantage of the approaches over the current fastest gradient coding strategies. The proposed schemes provide order-wise improvement over the state of the art in terms of computation load, and are also optimal in terms of both computation load and latency.

A Competitive Online Algorithm for Minimizing Total Weighted Completion Time on Uniform Machines

We consider the classic online scheduling problem on m uniform machines in the online setting where jobs arrive over time. Preemption is not allowed. The objective is to minimize total weighted completion time. An online algorithm based on the directly waiting strategy is proposed. Its competitive performance is proved to be max2smax1−1/2∑si,2smax/1+smax2.5−1/2m by the idea of instance reduction, where sm is the fastest machine speed after being normalized by the slowest machine speed.

English

English

The impact load cell as a tool to link comminution properties to geomechanical properties of rocks

Current tests for characterising rock breakage in comminution do not allow decoupling of rock breakage properties from the machine environment. Furthermore, it is difficult to predict the comminution behaviour of different ores or how they might behave in another breakage device. Consequently, this decoupling process is essential for comminution modelling of variable ores and blends, which interact with different breakage equipment. Previous studies have shown that the Short Impact Load Cell (SILC) is a versatile tool for characterising the primary ore breakage properties such as strength, apparent stiffness and mass-specific fracture energy, regardless of breakage environment. However, its potential as an ore characterisation technique to relate these mechanical properties to the ore characteristics (e.g. mineralogy, texture) has not been investigated in detail. In this first paper, the results from breaking cylindrical and irregular particles from different ore types in the SILC and Slow Compression Testing demonstrate the SILC’s ability to accurately measure strength, stiffness and specific energy of different ore types at different particle sizes which is relevant either for comminution modelling or for rock mechanics testing. The results of breaking cylindrical particles of nine different rock types show that the SILC is capable of matching the results of the slow compression machine for Indirect Tensile Strength from the Brazilian Test and the apparent stiffness results from the Uniaxial Compression test, allowing the accurate calculation of the mass-specific fracture energy distribution for each rock at different sizes. In addition to the cylindrical samples, sets of irregular particles were produced for three rock types and were broken with the SILC. The results show that the median values of strength and stiffness do not change significantly with the shape of the particle, but the standard deviation does increase when irregular particles are tested. If these results are compared with the cylindrical particle results, it can be seen that the geological characteristics of the rock control the median values of the parameters. When the variation around median value is considered it is apparent that particle shape exerts the major control, but geological characteristics still contribute up to 30% of the variation in strength, 25% in stiffness and 25% in the mass-specific fracture energy. The results indicate that the SILC is a suitable device that allows measurement of the primary breakage properties relevant to comminution, which can be easily associated with the geomechanical properties of the rock mass and geological characteristics. The further development of the proposed testing methodology can provide a more comprehensive and less empirical comminution testing for future modelling, incorporating geological and geomechanical properties of the rock.