Machining Cost Research Articles

Ultralow-field MRI machine costs less than a car

Ultralow-field MRI machine costs less than a car

FULLY AUTOMATED SOLAR GRASS CUTTER

In present world grass cutter machines are becoming very popular . Rapid growth of different high-tech tools and equipment makes our jobs much easier . Nowadays we are facing issues of pollution and power failures in our daily life . As we know pollution is manmade, so there are solutions for this problems too .Earlier grass cutters contains IC engine and hence because of its environmental impact, pollution level rises .IC engines are costly and hence the grass cutter is also costly.This was one of the main disadvantage of old model grass cutters. Maintenance cost of this conventional machine is also high.In order to overcome these drawbacks , we have thought about the device, which performs its functions without causing such problems.The project aims at implementing a grass cutting machine system which runs on solar energy .This model is economical compared to the previous one .Solar power doesn’t release any green house gases.Hence it is safe and environmentally friendly. Also use of solar power reduces use of electricity .General principle of mowing were used in the designinig and developing of solar powered grass cutter .The designing of the project consists of solar panel,DC motor,motor driver,arduinoUNO,cutting blade,ultrasonic sensor.Arduino is used to control the entire system and hence the device is automated.Ultrasonic sensor is used for avoiding obstacles and the working of the same is also controlled by the microcontroller.The battery recharges by the solar charging controller and also less energy is required for cutting the grass.Hence the performance is more efficient than the conventional machine.Levelling of grasses in playgrounds,gardens etc.can be achieved by using this machine.The cutted grass left behind will also be collected along with the cutting operation by using grass collection technique.This is additionally inbuilted on the device.Since the device is fully automated no man power is needed and also skilled labourers are not neeeded to operate the device.Hence normal people can use this automated grass cutter without much efforts. Keywords-Solar,Grass cutter,Levelling,Economical,Pollution- Free

An easy-to-build, accessible volumetric 3D printer based on a liquid crystal display for rapid resin development

Additive manufacturing has revolutionized the way prototypes and one-off parts are produced, but standard additive manufacturing techniques such as material extrusion or vat photopolymerization still present various issues like high print times and defects due to the layer-by-layer nature of the print process. Computed axial lithography (CAL), a novel volumetric additive manufacturing technique, solves many of the issues associated with traditional 3D printing, like staircasing defects and long printing times. However, state of the art CAL requires complex optical setups, limiting access to a working machine, greatly reducing the pace of new material and technologies being adapted to CAL. The Liquid Crystal Display-based computed axial lithography (LCD-CAL) setup here presented keeps many of the advantages of CAL, but utilizes a single lens and an LCD for its optical setup. By moving to LCDs, the machine cost is greatly reduced, and the extremely simplified optical setup of LCD-CAL makes CAL widely accessible. To highlight the versatility of the developed LCD-CAL, we design and characterize two high-viscosity resins for CAL printing based on highly crosslinked and bio-based acrylates, respectively. This work paves the way for widespread adoption of volumetric 3D printing by making a low cost, high speed CAL printer available.

Force and wear prediction model of disc cutter under compression shear failure mode based on dense core–cavity expansion theory (U-713)

Calculating disc cutter wear and predicting wear life are long-standing issues affecting the efficiency and cost of tunnel boring machines (TBMs). During the TBM excavation process, the wear of the disc cutter is a comprehensive outcome of the interaction between the tool and rock. Studying the mechanisms of tool rock breaking and wear can assist in developing a more precise model for predicting cutter wear. Therefore, based on the rock-breaking and wear mechanisms of a cutter, this study developed a model for predicting the cutter wear rate and life. The model is based on the theory of dense core–cavity expansion combined with the motion state of a cutter. First, the TBM excavation process and rock-breaking mechanism of a cutter were analyzed in detail. A cutting force model under the compression shear failure mode based on the dense core–cavity expansion theory was established. Based on the force model, a prediction model for the wear rate and life of the disc cutter was established, considering the working characteristics and wear mechanism. Furthermore, the impact of penetration on the prediction model was investigated. In addition, to demonstrate the practicality of the method, this study conducted a comprehensive examination using engineering examples. The results showed that the average difference in the cutter average wear rate using the developed model was less than 3% compared with the actual engineering result. In addition, the average difference in the wear life rate was less than 5%. Finally, the developed prediction method was compared with other models. The wear rate fitting accuracy was improved by 4.91–10.85% and the wear life fitting accuracy was improved by 5.11–12.19% by the developed method. This improvement confirms the reasonableness and reliability of the method. This method can estimate the wear rate and lifespan of a cutter more accurately and reliably than other methods, providing a practical tool to TBM engineers to make informed decisions and optimize tool replacement programs.

Materials requirement prediction challenges addressed through SDM and MEIO

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting.

Optimizing Machine Selection and Cost of Quality: A Comprehensive Approach for Enhanced Industrial Efficiency and Economic Growth

Abstract: In today's modern industrial economy, selecting suitable machinery and efficiently managing quality costs are critical for achieving sustainable growth and competitiveness. This research paper presents a comprehensive approach to optimizing machine selection and cost of quality (COQ) within industrial operations. The study commences by delineating the criteria for machine selection and determining their respective weights using the Analytic Hierarchy Process (AHP). Subsequently, the VIKOR method is applied to select the most suitable machine based on the established criteria and weights. Moreover, the paper explores the concept of COQ, underscoring its importance as a performance measurement tool for organizations. The research investigates various strategies for minimizing quality-related expenses and maximizing benefits, including defect prevention, quality assurance, and continuous improvement initiatives. A case study analysis, focusing on the selection between mechanical cutting CNC machines and laser cutting CNC machines, provides practical insights into the implementation of the proposed methodologies. Real data analysis of cost and quality metrics, coupled with formula-based calculations, offers valuable insights into the decision-making process. The research underscores the significance of market analysis and leveraging modern technology trends to inform machine selection decisions. Overall, the findings contribute to enhancing industrial efficiency and promoting economic growth by facilitating informed decision-making in machine selection and COQ management

Enhancing Industrial Efficiency: A Holistic Review of Machine Selection and Cost of Quality Optimization Strategies for Sustainable Growth

Enhancing Industrial Efficiency: A Holistic Review of Machine Selection and Cost of Quality Optimization Strategies for Sustainable Growth

Application of Taguchi Method, ANOVA Analysis, and TOPSIS Technique in Optimization of Process Parameters for Surface Roughness and Material Removal Rate in Electrochemical Machining of Al-SiC MMCs

Objectives: To evaluate the significance of advanced machining techniques, such as EDM, ECM, and USM, in increasing productivity and overcoming challenges associated with outdated Al-SiC MMC machining. To assess the surface roughness, tool wear, and machining cost implications of employing advanced machining methods for Al-SiC MMCs. Methods The parameters studied were voltage (V), feed rate (F), and electrolyte concentration (C) in electrochemical machining (ECM) of Al/15%SiC composites. To optimise process parameters, the Taguchi method for Design of Experiments (DOE) with an L27 orthogonal array was used. Signal responsiveness is optimised using the Taguchi approach. The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is used to find optimal machining settings. Findings: The outcome of this research is that the parameters affecting surface roughness and material removal rate are voltage, electrolyte concentration and feed rate. The minimum Surface Roughness achieved by selecting the best combination level is A2, B3, C3 (smaller is better) i.e., voltage 20 V, feed rate (f) 0.4 mm/min., electrolyte concentration (c) 30 g/lit. The maximum Material Removal Rate achieved by selecting the best combination level is A3, B3, C3 (larger-is-better) i.e., voltage 25V, feed rate (f) 0.4 mm/min., electrolyte concentration (c) 30 g/lit. Novelty : In this work, TOPSIS technique paired with Taguchi method is used which is rarely studied by other researchers. TOPSIS technique provides the best optimal solution as compared to other techniques. Keywords: Al-SiC MMCs, ECM, MRR, Ra, Taguchi method, TOPSIS

Modelling the weld cladding process to predict weld clad position and shape error

Wire arc additive manufacturing (WAAM) is one of the most productive metal additive manufacturing methods. One of its most promising applications holds in the manufacturing of difficult-to-cut materials where production costs can be reduced with minimizing the time of machining and total tool costs. To develop a correct WAAM, technological processes for manufacturing complex-shaped components welding torch path corrections and welding power corrections have to be made especially in critical sections such as corners and sharp edges. A predictive mathematical model of the material cladding during the WAAM process has been developed for the purposes of generating an optimal toolpath of the WAAM clads. This predictive mathematical model is simplified to reflect the important physical phenomena in the weld pool but also to optimize computing time. In this paper, the principle of the mathematical model is described, and its functionality is verified by the welding experiments with five different welding power settings. For the initial calibration of the model parameters single straight-line weld clads with 5 different welding power settings (wire feeds) ranging from 5.0 to 8.6 m/min were investigated. 3D scans of these welded samples are used for the verification. With the calibrated simulation model, it was possible to predict the precise shape with a maximum deviation circa 0.20 mm. The start portions of the weld clads seem more complex having the deviation circa 0.30 mm. These are valuable results as the WAAM technology is generally considered to be reasonably rough.

Impact of Soundbox by Third Party Application Providers (UPI) on Retailers, with Special Reference to Greater Chennai Corporation

Before the advent of UPI, payments primarily relied on debit and credit cards, proving advantageous for medium-scale business retailers while disadvantaging small-scale or micro retailers using cash due to the high cost of POS machines. The introduction of the UPI digital payment system by the National Payments Corporation of India in 2016 marked a significant boon for customers and retailers of all types. Despite its benefits, some retailers hesitated due to perceived authenticity issues in receiving payments. However, since 2019, the third-party application provider Paytm revolutionized UPI adoption by proposing the installation of a soundbox. This innovative addition led to increased UPI utilization by retailers, driven by enhanced security, user-friendliness, simplified transactions, and lower monthly rental costs. This paper explores the impact and role of the soundbox post-installation by third-party application providers, examining its merits, demerits, and gauging retailers' perspectives on this transformative technology.

The Transformative Impact of AI in Finance and Banking

The transformative impact of artificial intelligence (AI) in finance and banking is pro7 found, revolutionizing the way financial institutions operate, interact with customers, and make 8 decisions. This article explores the technological landscape that has paved the way for AI adoption, including falling data storage costs, data availability, advancements in machine learning, cost reduction, regulatory compliance, competitive advantage, risk management, customer experience, fraud detection, increased connectivity, and rapid advances in AI technologies. It discusses how AI is enhancing customer support, improving security through fraud detection algorithms, and enhancing credit scoring accuracy through machine learning. The value creation potential of AI in banking includes unlocking up to $1 trillion of incremental value annually through personalized services, cost reduction via automation, and uncovering new opportunities. Additionally, it provides examples of AI applications in banking, including personalization, automation, and insight extraction, showcasing how AI is transforming the industry. Despite these advancements, the article acknowledges challenges such as a lack of clear strategy, legacy systems, and fragmented data assets and proposes solutions like promoting a growth mindset and responsible AI deployment. It also addresses current issues in the banking sector, such as cyberattacks, voice cloning, and fraud, emphasizing the importance of AI in addressing these challenges. Overall, the article highlights the transformative potential of AI in finance and banking, urging banks to embrace an AI-first mindset for sustained growth and innovation.

Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing

Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking machining energy, machining time and machining cost as optimization objectives. Next, the IHNN for multi-objective optimization of STEP-NC machining parameters combining with Pareto theory, improved immune algorithm and non-monotone activation function is designed. Based on it, the optimal Pareto solutions of STEP-NC machining parameters are obtained, which intelligently realizes the multi-objective optimization of STEP-NC machining parameters and provides a decision support for the decision-maker. Finally, performance comparisons among the IHNN, classic non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are done by classic test functions with three objectives and multiple constraints which correspond to the mathematical model established in this paper, and its effectiveness and feasibility is verified by case study.

Enhancing Machining performance in Stainless Steel Machining using MXene Coolant: A Detailed Examination

Metal cutting, a complex process in manufacturing, involves various factors that significantly affect the quality of the final product. Notably, the turning process is crucial, with outcomes that heavily depend on multiple machining parameters. These parameters encompass speed, depth of cut, feed rate, the type of coolant used (specifically, high heat transfer MXene coolant), and insert types, among others. The material of the workpiece is also a critical factor in the metal-cutting operation. This study focuses on achieving optimal surface quality and minimizing cutting forces in the turning process. It recognizes the substantial impact of numerous process parameters, directly or indirectly affecting the product's surface roughness and cutting forces. Understanding these optimal parameters can lower machining costs and improve product quality. Our research concentrates on turning a stainless-steel alloy workpiece using a carbide insert tool. We employ the Response Surface Method (RSM) to optimize cutting parameters within a set range of cutting speed (100, 125, 150 m/min), feed rate (0.1, 0.2, 0.3 mm/rev), and depth of cut (0.4, 0.8, 1.2 mm). Additionally, we use various tool geometries and the RSM design of experiments to enhance and analyze the multi-response parameters of surface roughness and tool life. Optimal machining parameters for MXene-NFC involve a cutting speed of 140 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 0.5 mm. These settings ensure minimal surface roughness, maximum tool life, and the greatest total length of cut, achieving a composite desirability of 0.695.

Multi-material laser powder bed fusion of embedded thermocouples in WC-Co cutting tools

During machining processes, due to deformation of the material and friction of the chip along the tool surface, a significant amount of heat is generated, particularly in the cutting zone, thus leading to a wear increase and consequent tool life reduction. Therefore, the ability to assess the cutting temperature in real time is extremely important to better understand heat generation, thus contributing to the development of more effective strategies for reducing machining costs and increasing tool life and productivity.This work proposes the fabrication of embedded additively manufactured K-type and N-type thermocouples in sintered WC-Co substrates by multi-material laser powder bed fusion for cutting temperature measurement in real time. Results showed that this approach is able to produce dense and continuous K-type and N-type embedded thermocouples with no evidence of severe defects, being found in the calibration tests a linear relationship with temperature, with a Seebeck coefficient at 20 °C of 40.8 μV/°C for K-type thermocouples and 16.5 μV/°C for N-type thermocouples. Turning tests revealed that the developed multi-material laser powder bed fusion approach is effective for manufacturing WC-Co cutting tools with 3D printed embedded thermocouples with the ability to measure precisely and accurately cutting temperature in real time during machining processes.

Investigation of Flexible Job Shop with Machine Availability Constraints

In real-world scheduling applications, machines may be unavailable during certain time periods for deterministic and stochastic reasons. This situation does not pose any problems if the jobs always have more than one machine available for processing. However, it becomes an issue if the only available machine is the one which more than one job needs for processing. Thus, the investigation of limited machine availability, along with the practical requirement to handle this feature of scheduling problems, are of huge significance. This paper examined a flexible job shop environment of a manufacturing firm to optimize different performance criteria related to makespan, due dates, priorities and penalties by using a metaheuristic approach, taking into account the precedence constraints. The work investigated the case in which all machines are available for processing and another case in which some of the machines are known in advance to be unavailable. From the results, the best schedules are analysed and the perspectives of the findings to decision-makers are discussed with the purpose of achieving high machine utilization, cost reduction and customer satisfaction.

Research on energy-saving control of agricultural hybrid tractors integrating working condition prediction.

To address the issues of tractors using too much fuel and not being energy efficient, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed for agricultural hybrid tractors. The Dongfanghong 1804 tractor is being used for research. Firstly, the main parameters of the hybrid drive system are determined and modeled. Secondly, based on the adaptive cubic exponential forecasting method, the working condition information for a period of time in the future is predicted through historical working condition information. Furthermore, combining the predicted working conditions information, the goal is to minimize the total energy consumption cost of the entire machine. Motor power and diesel engine power are control variables. The battery state of charge is a state variable. Subsequently, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed. Finally, the simulation test is carried out based on the MATLAB simulation platform. Research indicates: under plowing conditions, compared with the power following control strategy, the proposed predictive control strategy can effectively manage the performance of the diesel engine and motor, ensuring they operate at their most efficient level. The total energy consumption costs of the power following control and predictive control strategies are 37.17 China Yuan (CNY) and 33.67 CNY, respectively. The cost of energy used is decreased by 9. 42%, which helps make tractor field plowing more efficient and economical.

Special Issue on Abrasive Technology for High-Precision and High-Efficiency Machining of High-Performance Materials

The demand for high-precision and high-efficient machining of high-performance materials and components has increased in various industries such as optical, automotive, communication, life sciences, and medical sciences. Certain difficult-to-machine materials can be reliably machined using deterministic precision cutting processes. However, hard and brittle materials such as ceramics, carbides, hardened steel molds, glassy materials, or semiconductor materials, require precision abrasive technologies with super abrasives like diamond, cBN, or new tool materials for machining. However, machining high-precision components and their molds/dies using abrasive processes is considerably more difficult due to their complex and non-deterministic nature and textured surfaces. Furthermore, high-energy processes such as laser technology can assist abrasive technologies in ensuring higher precision and efficiency. Precision grinding and polishing processes are primarily used to generate high-quality and functional components, typically made of difficult-to-machine materials. Thus, the surface quality achievable by precision grinding and polishing processes becomes more important for reducing machining time and costs. This special issue features 10 papers on the most recent advances in precision abrasive technologies. These papers cover the following topics: - Ultrasonic grinding of micro holes using cemented WC tools - Drilling holes in CFRP aircraft using cBN electroplated ball end mill - In situ evaluation of drill wear using tool images - Grinding belt based on modified information entropy - Radial directional vibration-assisted grinding of Al2O3 ceramics - Chatter vibration suppression using fixed superabrasive polishing stone - Free abrasive finishing of internal channels with different cross-sectional geometries - High-quality machining of cemented carbide using PCD ball end mills - Fixed-abrasive machining with magnetic brush for Ti-6Al-4V ELI alloy - High-efficient polishing of polymer surfaces using catalyst-referred etching This issue will provide an understanding of the recent developments in abrasive technologies, leading to further research. We deeply appreciate the careful work of all the authors, and we thank the reviewers for their incisive efforts.

Reliability analysis of PVD-coated carbide tools during high-speed machining of Inconel 800

Predicting the cutting tool life is crucial for effectively managing machining costs, ensuring product quality, maintaining equipment availability and minimising waste in machining processes. When machining heat-resistant superalloys such as Inconel, the concern for tool life becomes even more pronounced. Cutting tool failure is a complex phenomenon that depends on several variables, including tool type and material, workpiece material, machine tool type and machining parameters. Traditional run-to-fail tests to predict tool life are costly and time-consuming. To address these challenges, accelerated degradation testing (ADT) offers a promising solution. ADT involves subjecting the component to higher levels of parameters, causing it to fail faster than under normal conditions. This approach saves time and reduces expenses associated with tool life tests for valuable workpieces. In implementing the concept of ADT, the experimental cutting speed [Formula: see text] values are selected much higher than the normal usage levels in the present study. The tool life tests are performed at three levels of [Formula: see text], feed rate [Formula: see text], depth of cut [Formula: see text] and tool nose radius [Formula: see text] using the Taguchi L9 orthogonal array. Parametric statistical approaches, that is, accelerated failure time (AFT) models, are applied with distributions, namely the Weibull, lognormal and log-logistic distributions, to analyse the cutting tool’s reliability based on predictor variables. Various tool wear modes are considered criteria for tool failure. The comparison is made among the mean time to failure (MTTF) of cutting tools as predicted by various fitted models. Additionally, a favourable tool failure pattern is observed when using the middle level of [Formula: see text] and operating at relatively higher [Formula: see text] values while ensuring that [Formula: see text] and [Formula: see text] values fall within the recommended range. The proposed approach has the potential for diverse applications, including assessing the reliability of cutting tools and tool condition monitoring.

Performance analysis of conventional and high-feed turning tools in machining the thermally affected zone after plasma arc cutting of low carbon manganese-alloyed steel

The service life of cutting tools significantly affects machining costs. Establishing end-of-life criteria is crucial to mitigate wear effects on part quality, machine integrity, and maximize tool utilization. This is particularly important in machining operations with materials featuring numerous surface defects, as observed in those processed by prior thermal cutting operations, leading to accelerated tool wear rates. This study compares tool life and wear mechanisms in conventional and high-feed turning of low carbon manganese-alloyed steel bushes, previously profiled by plasma thermal cutting. Machinability tests utilized Class P carbide tools with triangular geometry for conventional turning and rhombic geometry for high-feed turning, both coated for enhanced performance. Tool's end-of-life criterion was established per ISO 3685 standard. The machining parameters adopted include a standard cutting depth of 1.5 mm, varying cutting speeds (140, 160, and 200 m/min) and feed rates (0.2; 0.3; 0.4 mm/rev for conventional turning, and 0.5; 0.75; 0.82 mm/rev for high-feed turning). Analysis involved assessing tool wear rates by observing flank wear and analyzing it using scanning electron microscopy and energy-dispersive spectroscopy techniques. Machined surface quality was quantified by roughness measurements. Metallographic characterization of bushes post-cutting indicated thermal cutting effects on the cutting edge's microstructure, microhardness, and residual stress. Machinability test results showed superior wear resistance in the high-feed turning tool (wear rate about 13 % lower). Surface finish evaluation demonstrated that, when isolating the effects of tool geometry and feed rate, the average surface roughness increased at a similar rate with tool wear for both conventional turning and high-feed turning. Identical wear mechanisms were observed: abrasive on the flank and adhesive on the rake face. Thus, the superiority of high-feed turning over conventional turning is established under the evaluated machining conditions.

Rubber-Tracked Forwarders—Productivity and Cost Efficiency Potentials

The extraction of timber is expensive, energy intensive, and potentially damaging to the forest soil. Machine development aims to mitigate risks for environmental impact and decrease energy consumption while maintaining or increasing cost efficiency. The development of rubber-tracked forwarders has gained renewed interest, not least due to climate change leading to unreliable weather in combination with low tolerance for soil damage. The increased cost of rubber tracks compared to wheels is believed to be compensated by higher driving speed enabled by semi-active suspension. Thus, the aim of this study was to theoretically investigate how the productivity and cost efficiency of rubber-tracked forwarders are affected by variations in driving speed and machine costs. The calculations were made with fixed stand parameters, to evaluate performance in well-defined working conditions, and with parameters from 2500 final felling stands in central Sweden, to evaluate performance in varied working conditions. Scenarios were compared to a baseline corresponding to mid-sized wheeled forwarders. The results show higher productivity with the increased driving speed enabled by rubber tracks and suspension at all extraction distances, with larger differences at long extraction distances. Assuming a 15% higher machine price for the rubber-tracked forwarder and a variable cost increase proportional to speed increase, extraction costs break even with the baseline at 400 m and 700 m extraction distance for moderate and fast driving speed, respectively. Furthermore, a rubber-tracked forwarder is likely to enable access to a larger part of the harvest area during longer seasons. For the studied set of stands, the year-round accessible volumes are estimated to increase from 9% to 92% with a rubber-tracked forwarder. With rubber tracks instead of wheels, good accessibility has the potential to be combined with low soil impact and cost efficiency in a favourable way for both industry and ecosystem.