Machining Cost Research Articles

Perhitungan Ongkos Pemesinan Konvensional Menggunakan Macro Vba Excel pada Studi Kasus (Produk Roda Gigi Payung Ø65mm × Z24)

In the conventional machining process, production costs are essential to know to determine the cost of making a product in any industrial manufacturing such as the SMK N 2 PEKANBARU machining workshop. Machining production costs are calculated based on machining time per product and machining operation costs. In calculating conventional machining costs, the VBA Excel macro is an application that can be used to calculate production costs. The Ø65mm × z24 umbrella gear product which is the research case study is a product that is machined by two conventional machining processes, namely lathe machining and milling machining. From the results of the research, the cost of conventional lathe machining, namely making gear wheels, is IDR 47,841/product, and the conventional milling process, namely making Z24 gears, is IDR 40,002/product, while the machining cost for lathe mastercam simulation is IDR 33,187/product and the machining cost for milling mastercam simulation is IDR 20,340/product. Machining costs are the product of operating costs per minute (cm) and machining time (tm). So, from the results of calculating machining and production costs, it is known that the product manufacturing costs or production costs for Ø65mm × Z24 umbrella gears are in the range of IDR 111,827 - IDR 146,143/product.

Comprehensive Review of Machine Learning Techniques for Condition-Based Maintenance

While most industrial maintenance strategies are centered on optimizing machine runtime and cost reduction, the condition-based maintenance (CBM) strategy distinguishes itself from others in its use of real-time operational data from machines to help engineers make informed decisions. The introduction of machine learning (ML) into a CBM strategy can increase its effectiveness, enabling more accurate predictions and making the decision-making process more efficient. In this review paper, we seek to provide a comprehensive overview of the role ML plays in modern CBM systems, beginning by outlining the core concepts and historical development of CBM and briefly introducing various ML techniques being employed in industry today. We then review numerous real-world cases where ML-based CBM systems have been implemented and discuss some of the technological, human, and ethical challenges faced by organizations seeking to integrate sophisticated ML models into existing CBM systems. We end by highlighting some of the current limitations of ML-based CBM systems, paving the way for a discussion on emerging trends and future research directions in this area.

Optimizing cutting speeds in a machining bottleneck stage in order to reach the takt time with the minimum machining cost

Optimizing cutting speeds in a machining bottleneck stage in order to reach the takt time with the minimum machining cost

Optimizing the Electrical Discharge Machining Process Parameters of the Nimonic C263 Superalloy: A Sustainable Approach

Engineers continue to be concerned about electrical discharge-machined components’ high energy consumption, machining debris, and poor dimensional precision. The aim of this research is to propose a hybrid neuro-genetic approach to improve the machinability of the electrical discharge machining (EDM) of the Nimonic C263 superalloy. This approach focuses on reducing the energy consumption and negative environmental impacts. The material removal rate (MRR), electrode wear ratio (EWR), specific energy consumption (SEC), surface roughness (Ra), machining debris (db), and circularity (C) are examined as a function of machining parameters such as the voltage (V), pulse on time (Ton), current (I), duty factor (τ), and electrode type. By employing the VIKOR method, all the responses are transformed into a distinctive VIKOR index (VI). Neuro-genetic methods (a hybrid VIKOR-based ANN-GA) can further enhance the best possible result from the VIKOR index. During this step, the hybrid technique (VIKOR-based ANN-GA) is used to estimate an overall improvement of 9.87% in the response, and an experiment is conducted to confirm this condition of optimal machining. This work is competent enough to provide aeroengineers with an energy-efficient, satisfying workplace by lowering the machining costs and increasing productivity.

A Novel Modified Discrete Differential Evolution Algorithm to Solve the Operations Sequencing Problem in CAPP Systems

Operation Sequencing (OS) is one of the most critical tasks in a CAPP system. This process could be modelled as a combinatorial problem where finding a suitable solution within a reasonable time interval is difficult. This work implements a novel Discrete Differential Evolution Algorithm (DDEA) to solve the OS problem, focusing on parts of up to 76 machining operations; the relationships among operations are represented as a directed graph; the contributions of the DDEA are as follows: (1) operates with a discrete representation in the space of feasible solutions; (2) employs mutation and crossover operators to update solutions and to reduce machining and setup costs, (3) possess a local search strategy to achieve better solutions, and (4) integrates a statistical method based on quantiles to measure the quality and likelihood for an achieving a solution. To demonstrate the efficiency and robustness of the DDEA, five prismatic parts with different numbers of machining operations as benchmarks to address the OS problem were selected. The results generated the same OS for parts with a few machining operations (up to 23 machining operations). Conversely, for parts with more machining operations, the DDEA needs more runs to achieve the best solution.

Comparative study of dry machining performance using hybrid textured cutting insert

ABSTRACT Dry machining is an environmentally friendly process and minimizes machining costs. Also, using cutting fluids in machining affects the operator’s health risks and the surrounding environment. One of the alternatives is surface texture on the coated cutting tool, known as Laser Surface Texturing (LST). In the current research work, Hybrid Textured Tools (HTT) in a combination of dimples and grooves were developed, and the performance of the tools was studied. The experimental findings assert that incorporating HTT during machining operations conclusively leads to a substantial reduction of 47% in the cutting force (Fc), 12% in tool-chip interface temperature (TCT), 26% in surface roughness (Ra), and 30% in flank wear (Vb) in comparison to non-textured tools (NT). HTT implementation can enhance machining performance, improve efficiency, and reduce tool wear, leading to better-quality products.

Swarm Optimisation to Model the Surface Roughness of an AISI 4340 Turning using the Hot Machining Process

Given that surface roughness is used to determine product quality, it is a crucial consideration in turning machining. Moreover, it considerably affects the cost of machining. This study forecasts surface roughness values for AISI 304 stainless-steel hot lathe machining using the particle swarm optimisation (PSO) methodology. The workpiece is heated to 100, 150 or 200 degrees Celsius before being turned. Afterwards, the depth, speed and feeding rate of cutting are adjusted to determine the surface roughness of the workpiece. The feeding rate is determined to be the most considerable influence in raising the surface roughness value, followed by cutting depth, cutting speed and workpiece temperature. In terms of accuracy, empirical modelling performs better. The PSO methodology illustrates an effective and straightforward method that can be applied to calibrate different empirical machining models.

Enhancing cutting tool durability: Exploration of Al2O3-SiCw-graphene composites fabricated by SPS for improved mechanical and scratch resistance

This study focuses on the fabrication and characterisation of Al2O3-SiCw-rGO composites sintered by spark plasma sintering (SPS) technique. The research is mainly focused on understanding the impact of varying graphene content (0.5, 1.0 and 1.5 vol %) on nanomechanical properties and scratch resistance. Among the compositions investigated, the composite containing 0.5 vol% graphene showed superior mechanical properties. Specifically, it exhibited a hardness of ∼30 GPa, a fracture toughness of 10.5 MPa m1/2 and a flexural strength of ∼920 MPa. In addition, this composite showed remarkable scratch resistance compared to composites with a higher or lower graphene content. In addition to mechanical properties, the fabricated composites also exhibited high electrical conductivity. This characteristic is especially advantageous for electrical discharge machining (EDM) processes. The high electrical conductivity enables efficient machining of complex shapes by EDM, thereby reducing machining costs while ensuring accuracy and efficiency.

Elevating Sustainability: The Role of Machining in Modern Eco-Friendly Manufacturing Processes

Sustainable manufacturing has become a critical concern across various industries, including aerospace, automotive, and services. It involves the creation of manufactured products through economically sound processes that minimize negative environmental impacts while conserving energy and natural resources. Additionally, sustainable manufacturing aims to enhance employee, community, and product safety.This paper focuses on the machining process as a key component of sustainable manufacturing. Machining is widely used in manufacturing industries to fabricate components, but it also significantly contributes to environmental pollution. Sustainable machining has been a topic of discussion for over a decade. In sustainable machining processes, every factor acts differently like, the tool life, productivity, and effective utilization of resources must be increased. In contrast, the machining cost, machine cutting power, and adverse effect of cooling and lubrication fluids will be decreased. Important aspects of sustainable machining include dry and near-dry machining, cryogenic machining, and minimum quantity lubricant (MQL) were presented in this article. The effectiveness of these processes is assessed based on cutting tool life and the quality of machined components. In addition, the advancements consideration such as novel process variations, process hybridization, the use of sustainable tools and green transfer mediums, and the optimization of process parameters. Recommendations include increasing efficiency and reducing waste by incorporating recyclable tools in the machine industry implementing either stand alone or hybrid sustainable lubrication machining techniques.

A Review on Traditional Processes and Laser Powder Bed Fusion of Aluminum Alloy Microstructures, Mechanical Properties, Costs, and Applications.

Due to its lightweight, high strength, good machinability, and low cost, aluminum alloy has been widely used in fields such as aerospace, automotive, electronics, and construction. Traditional manufacturing processes for aluminum alloys often suffer from low material utilization, complex procedures, and long manufacturing cycles. Therefore, more and more scholars are turning their attention to the laser powder bed fusion (LPBF) process for aluminum alloys, which has the advantages of high material utilization, good formability for complex structures, and short manufacturing cycles. However, the widespread promotion and application of LPBF aluminum alloys still face challenges. The excellent printable ability, favorable mechanical performance, and low manufacturing cost are the main factors affecting the applicability of the LPBF process for aluminum alloys. This paper reviews the research status of traditional aluminum alloy processing and LPBF aluminum alloy and makes a comparison from various aspects such as microstructures, mechanical properties, application scenarios, and manufacturing costs. At present, the LPBF manufacturing cost for aluminum alloys is 2-120 times higher than that of traditional manufacturing methods, with the discrepancy depending on the complexity of the part. Therefore, it is necessary to promote the further development and application of aluminum alloy 3D printing technology from three aspects: the development of aluminum matrix composite materials reinforced with nanoceramic particles, the development of micro-alloyed aluminum alloy powders specially designed for LPBF, and the development of new technologies and equipment to reduce the manufacturing cost of LPBF aluminum alloy.

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.