Machining Cost Research Articles

Acceptance Ordering Scheduling Problem: The impact of an order-portfolio on a make-to-order firm’s profitability

Firms’ growth, the darling measure of investors, comes from higher revenues. Thus, sales and marketing departments make extreme efforts to accept as many customer orders as possible. Unfortunately, not all orders contribute equally to profits, and some orders may even reduce net profits. Thus, saying no (i.e., not accepting an order) may be a necessary condition for net profits growth. For understanding the impact of rejecting orders on profitability, we propose an order acceptance and scheduling problem (OAS). Although the OAS has extensively been studied in the literature, there is still some gap between these papers and real-life problems in industry. In an attempt to close that gap, the OAS we propose considers orders revenues, machines costs, holding costs and tardiness costs. We develop a mixed integer linear programming (MILP) model for solving this problem. Since the complexity of the problem makes it impossible for the MILP to solver large-scale instances, we also propose a metaheuristic algorithm. Numerical experiments show that the metaheuristic finds good quality solutions in short computational times. In the last part of the paper we confirm some managerial insights: higher holding and tardiness costs imply a lower acceptance of orders, forcing production has a concave negative impact on net profits, and accurately estimating costs is essential for good planning.

Performance Evaluation of Graphene Nanofluid to Mitigate the Wear of a Diamond Tool in Micro-Machining of Ti6Al4V Alloy

Diamond tools are extensively used in ultra-precision machining due to their exceptional performance. However, when machining challenging materials like Ti6Al4V, diamond tools experience significant wear due to poor machining properties and catalytic effects. Tool wear not only impacts machining quality but also escalates machining costs and energy consumption. Cutting fluids are commonly employed to mitigate interfacial reactions and suppress tool wear. However, traditional cutting fluids are difficult to penetrate the cutting area and have limited lubrication and cooling capabilities. Therefore, in this paper, a technique combining graphene nanofluid and minimum-quantity lubrication (MQL) is used to suppress diamond tool wear. Firstly, micro-milling experiments for Ti6Al4V alloy are conducted using diamond tools in the graphene nanofluid MQL and under a dry environment. The experimental results show that tool wear is effectively suppressed by graphene nanofluids. Subsequently, the cutting process in both environments (graphene nanofluid MQL, dry) is simulated. The suppression mechanism of graphene nanofluid MQL for diamond tool wear is evaluated from phase transition, atomic transfer process, and amorphous behavior of diamond structure. The simulation results show that the contact characteristics, cutting force, and cutting temperature are improved by graphene nanofluids. Tool wear is effectively reduced. In addition, the removal efficiency of workpiece materials has also been improved. This work provides a technical basis for exploring the application of graphene nanofluids in diamond tool damage suppression and micro-milling.

Machining Accuracy Allocation of Shaft System Considering Vibration and Cost

In some important equipment such as vessels, the requirements for vibration reduction and cost-effectiveness of centrifugal pumps are becoming more and more stringent. It becomes really essential to present a method to assign manufacturing accuracy for shaft system assembly considering vibration and cost. A numerical model of the pump shaft system is developed to analyze the residual unbalance of the pump shaft system. The finite element model was verified through prototype tests. A machining accuracy allocation scheme is proposed by using an orthogonal design, and the cost and vibration intensity of the shaft system generated by vibration are evaluated. Based on this, a method to distribute shaft system machining accuracy is proposed to control the cost as much as possible while ensuring the vibration. Through orthogonal design, multiple sets of key parameter combinations are obtained, the vibration response of the shaft system under the parameter combination is calculated, and the contribution of each shaft system error to the shaft frequency vibration is analyzed. Combining the relative machining cost of the shaft system under different parameter combinations, the optimal allocation of the key parameters of the shaft system is completed based on vibration evaluation and relative cost.

Design, Optimization and Analysis of Milling Fixture

Abstract: The manufacturing industry of small scale provides wide range of products to fulfill the market needs. To face many challenges of market these industries should increase their production rate with good quality and accuracy. The manual production is of low production rate and long throughput time. Moreover, standardization of manual processes is difficult and also its difficulty in maintenance, thus fixtures is used on the machines. Therefore, this study aims to design a Fixture. Basically, Fixture is a work holding device to guide the tool. The main purpose of making this fixture is to perform the milling operation without any need of shifting the job regularly. This results in reduction of production time and increase in production rate. This will lead to decreasing manufacturing time and also the machining cost. In this work the Milling fixture is designed and analyzed for the stresses and deformation that occur during the machining process. The CAD modeling Software is used to model the Milling fixture and analysis work will be carried out by using ANSYS Workbench Software.

Parametric Optimization of Cutting Force and Temperature in Finite Element Milling of AISI P20 Steel

Multiple manufacturing methods are used in the manufacturing industry. The most commonly used method is machining methods. With machining methods, production can be made from both raw materials and finishing processes can be applied to products produced with different production methods. However, the initial cost of machining operations is quite high due to factors such as the machining parameters used during the process, the rigidity of the machine, and the machining conditions. Today, the finite element method (FEM) has been widely used in order to reduce the initial cost of machining. For this reason, in our study, the machining of AISIP20 material was carried out with the FEM. This study, four different cutting speeds, feed rates and two different cutting depths were used and the lateral feed rate was kept constant. As a result of the study, the cutting force values and temperature values that occurred depending on the machining parameters were evaluated by finite element analysis. Consequently, in the study, an increase in the cutting force occurred in general with the increase of feed, cutting depth and cutting speed parameters, and a decrease in temperature values occurred with an increase in cutting speed and feed and constant cutting depth. The lowest cutting force was 36.11 N, while the highest was 1951.42 N. The lowest and highest temperature values that occur during the process are 448.98 and 593.14 °C, respectively. In this regard, for the optimization of the parameters, the proportional change between the parameters and the finite elements as well as the physical processes can be performed for the desired final product.

Wear resistance enhancement of Inconel 718 via high-speed ultrasonic vibration cutting and associated surface integrity evaluation under high-pressure coolant supply

Machining-induced surface integrity evaluation and functional performance are considered as the most representative problems for evaluating the machining quality of Ni-based superalloys (for example, Inconel 718). Although high-speed ultrasonic vibration cutting (HUVC) has been proved to be effective for improving the machinability of Inconel 718, the surface integrity and its effect on functional performance (for example, wear resistance) are still unclear. By applying scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and white light interferometry (WLI), the surface integrity induced by HUVC and the wear resistance of the machined surface were comprehensively evaluated; the grain refinement, hardness increase, and wear mechanism of the machined surface were thoroughly studied, which were compared with the conventional cutting (CC) process. Compared with the CC process, the HUVC process achieves superior grain refinement, thicker surface plastic deformation layer, lower surface roughness Ra and Rz, higher hardness and compressive residual stress, and thicker hardening layer. The wear rate of the machined surface processed by HUVC is reduced by at most 24.01%. This study shows that the HUVC process can be potentially used to improve the wear resistance, which can effectively achieve grain refinement, surface roughness reduction, and wear resistance improvement while reducing the machining cost and process procedure.

An extended cost analysis method for complex lightweight aircraft components manufactured with selective laser melting

Current research has shown that the costs of manufacturing parts by the selective laser melting (SLM) process are higher than production using conventional manufacturing techniques. This paper aims to extend a cost manufacturing analysis method for metallic parts based on CAD data by including hot isostatic pressing as a heat treatment in the SLM technique. The proposed method includes all the pre- and post-processing steps linked to SLM and allows the determination of aggregated costs per part. A case study consisting of a lightweight metallic aircraft wing rib with Internet of Things capabilities whose weight is around 96% less than that shown to be necessary by other studies is presented. The results show that the main cost driver is the build cost, for which the major contributor is the machining cost, followed by the material cost. This analysis can be applied to other components within an aircraft wing box.

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

In pursuit of sustainability in machining thin walled α‑titanium tubes: An industry supported study

Cryogenic machining has emerged as a major sustainable machining techniques especially for difficult-to-cut alloys, including titanium, Inconel alloys in terms of its impact on cost savings for high-value manufacturing projects reduced energy consumption, enhanced worker safety, and eliminating the infrastructure and disposal needed for flood coolants. However, further study is needed to quantify the sustainable elements of machining processes to have a clearer vision for industries to transition from traditional techniques to such advanced techniques. In the currently known literature, the topic of high-speed machining of titanium tubes with thin walls is yet to be explored under advanced cooling technique. The studies on the machinability indicators including tool wear, power consumption etc. are the backbone to the assessment of sustainability aspect of machining. The quantification of sustainability indicators in terms of machining costs and carbon emissions will provide better insights to the industries to maximise their profits and have least negative impacts on the environment. Thus, this study focuses on analyzing sustainability of the machining processes in terms of economic and environmental factors while turning thin walled α‑titanium tubes under different cutting environments which include dry, wet and cryogenic machining under two different high cutting speeds of 150 m/min and 200 m/min. The economical aspect comprises the assessment of the different machining costs occurring during the machining. The environmental aspect includes the assessment of carbon emissions occurring during the machining operation. The backbone of the sustainability analysis is the machinability analysis which involves the investigation of power consumption and cutting tool-wear. A comprehensive study of tool wear and total power consumption has been presented to get a better perspective in the machinability of thin walled α‑titanium tubes while working in the cryogenic environment in comparison with dry and wet environments. The total machining cost is relatively less (approximately 27%) under cryogenic environment in comparison with a wet environment making it a more economical process. The total emissions of carbon are also found to be lowered (up to 9%) while machining under cryogenic environment in comparison with a wet environment making it a more environmentally friendly process. The cutting tool wear is found to be relatively low (up to 38.68% and 72.24%) under cryogenic environment in comparison with wet and dry environments respectively. Power consumption was compared for the three different environments to have detailed analysis in terms of productivity and sustainability. A significant reduction in total power consumption (up to 19.25%) was observed under cryogenic environment in comparison with wet environment.

Optimization of the machining of metallic additive manufacturing supports: first methodological approach

Metal additive manufacturing is an active field of innovation. However, for laser power bed fusion (LPBF), supports removal is a major constraint. In this technology, supports are strongly welded to the part to tightly maintain it, avoid distortion, and evacuate thermal load. Although supports are usually optimized for manual removal, machining is often necessary, which can affect post-processing productivity. This paper proposes a comprehensive methodological approach to optimize the selection of cutting parameters, cutting tools, and support structure for LPBF. The aim is to help additive manufacturers find supports that reduce machining costs in terms of time and cutting tool degradation, from among the numerous support designs available. This approach can also optimize the design of lattice structures used inside parts. Our results show that among the 11 designs tested, honeycomb and squared pattern grid supports are the most efficiently machined using the 8-teeth tangential milling of the 3 tools tested, with a good post-machined surface roughness and tools’ health. The method considers low magnification optical analysis and an accelerometer sensor, which is easy to use even for small- and medium-sized enterprises. This paper also proposes and analyzes a new kind of porous support using this method.

Orthogonal turning of AISI 310S austenitic stainless steel under hybrid nanofluid-assisted MQL and a sustainability optimization using NSGA-II and TOPSIS

AISI 310S austenitic stainless steels are widely used in many industries, such as oil, gas, and petrochemical industries containing hot concentrated acids, due to their superior properties like corrosion resistance, high-temperature service, and creep resistance. However, a study on the machining of AISI 310 stainless steel has not been published in the scientific literature. It is known that the machinability of the austenitic stainless steel alloys, in which the AISI 310S alloy is included, is poor due to its low thermal conductivity, high work hardening, and built-up edge (BUE) formation. This situation causes sustainability problems by increasing the energy consumptions, the total machining cost and the amount of carbon emissions. The presented study investigated for the first time machining response and sustainability assessment in the orthogonal turning of AISI 310S stainless steel using three different cutting speeds and feed values, and five different cutting conditions such as dry, vegetable-based fluid minimum quantity lubrication (MQL), nano molybdenum disulfide (nMoS2) reinforced nanofluid MQL, nanographene (nGP) reinforced nanofluid MQL, and nMoS2/nGP reinforced hybrid nanofluid MQL, and a sustainability optimization was performed. Sustainability optimization was conducted to minimize the resultant force, surface roughness, carbon emission, and total machining cost using multi-objective optimization with NSGA-II (Non-dominated Sorting Genetic Algorithm II) and multi-criteria decision-making with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The resultant force, surface roughness, total carbon emission, and total machining cost were reduced by a significant, 56.8%, 86.8%, 30.4%, and 2.9%, respectively, under the nGP reinforced nanofluid MQL cutting condition compared to the dry cutting condition. As a result of sustainability optimization, the use of high cutting speed (220 m/min), low feed values (between 0.11 mm/rev and 0.14 mm/rev), and nGP reinforced nanofluid MQL method (N2-MQL) in the turning of AISI 310S stainless steel material provides the optimum surface roughness values, cutting forces, carbon emissions, and total machining costs.

Developing a model for managing sustainable regional forest biomass resources: System dynamics-based optimization

In Japan, the number of plantation forests that must be harvested is increasing. Therefore, it is desirable to establish an operational system with a high efficiency. When using efficient forestry machinery, it is necessary to secure a sufficient amount of operation commensurate with the cost of machines, while simultaneously considering the feasibility of the labor force. However, no previous research has presented a quantitative forestry operation management method from the viewpoint of optimizing the amount of operation on a regional forestry scale. First, a basic model that mathematically quantifies the amount of operation and changes in forest biomass by applying system dynamics (SD) was developed, and a regional forestry scenario with the labor force as a constraint was optimized. As a result of the optimization, it is suggested that the scenario will be sustainable if regional forests are managed in an area of approximately 800 ha for 10 workers. The amount of operation in the scenario was generally feasible with a limited labor force during the simulation period and was also validated against reported domestic forestry data. We conclude that the SD model can contribute to the development of a regional forest management plan tailored to local conditions. In addition, it will greatly contribute to the assessment of carbon reduction in the national forestry sector through sustainable management of local forest biomass resources.

OPTIMIZATION OF LASER CUTTING PROCESS PARAMETERS ON SS347 USING GRA AND TOPSIS

Laser cutting is a one of the efficient manufacturing processes in industry to cut the hard materials by vaporizing. Stainless steel (SS347) is the most popular material for many applications due its unique characteristics such as efficiency to retain good strength with no inter-granular corrosion even at elevated temperatures. However, the cutting or machining of this material is very difficult. On the other side, the machining cost of laser process is high when compared with other processes. In this work, GRA and TOPSIS techniques are used to study the laser cutting process parameters of SS347. The obtained results were compared with the data mining approach. The input parameters are power, speed, pressure and stand-off distance (SOD) and the output responses of surface roughness, machining time and HAZ are considered. The set of experiments were constructed by using the Taguchi’s L9 method. The predicted closeness value of TOPSIS is greater than the GRA technique and the predominant factor observed is SOD followed by pressure, speed and power. In this work, C4.5-decision tree algorithm is applied to find the most influential parameter. It also represents the low-level knowledge of data set into high level knowledge (If-Then rules form). This investigation reveals that both TOPSIS and data mining suggested the SOD as predominant factor. This result of the optimized process parameters supports the laser assisted manufacturing industries by providing optimized output. Better results were obtained using the optimized set of parameters with the machining time, HAZ and surface roughness being 7.83 s, 0.09 mm and 0.86 [Formula: see text]m, respectively. The results of this work would be very useful for automobiles and aircrafts industries where SS347 is highly employed.

Ultrasonic Assisted Machining Overview: Accessing Feasibility and Overcoming Challenges for Milling Applications

Machinability, along with its associated facets, is a critical parameter that ultimately determines the cost of machining. Its optimization, however, is inherently limited by the current technology. To surmount such limitations, novel alternative machining technologies, such as Ultrasonic Assisted Machining (UAM), have emerged. The present study introduces UAM, the technology’s underlying principles, and general considerations for vibration application (harmonic waves, eigenfrequencies, resonance). The influence of ultrasonic application on the key parameters of conventional machining processes is studied and relevant research data are presented to support UAM benefits. Following, a comprehensive kinematic examination of vibration application to the milling process is conducted, accounting for various possible vibration modes. A detailed analysis of the requisite system components and their technical specifications is presented, followed by identifying common issues within such systems. Solutions for the identified limitations are proposed, acting as design guidelines for future technological advancements. Finally, based on the conducted research, conclusions are drawn and future directions for UAM are suggested.

On Sensor Data Clustering for Machine Status Monitoring and Its Application to Predictive Maintenance

Predictive maintenance is one of the main approaches which Industry 4.0 is based on, since it aims at reducing unplanned downtime and maintenance costs of industrial machines. In this work, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-aware clustering-based approach to the analysis of sensor data</i> is presented for the purpose of monitoring the time evolution of the health status of an industrial machine. A possible application of the proposed framework to predictive maintenance is then proposed. As a relevant representative application scenario, the focus is on one of the key machines in a pharmaceutical plant: a freeze-dryer. The illustrated procedure allows to carry out a time segmentation of the properly sensed data. More precisely, the corresponding operational points (associated with features of the sensed data) are clustered using various algorithms, among which Density-Based Spatial Clustering of Applications with Noise (DBSCAN) turns out to be the best. The benefits of the proposed approach are (i) its general nature and (ii) the limited amount of needed features that have to be extracted from a single sensor signal. The proposed procedure is attractive when the collected data (e.g., from a single sensor) are not sufficient to build an accurate physical model of the monitored component.

Fabrication of Solar Operated Multipurpose Floor Cleaning Machine

With the advancement of technology, automated floor cleaning machines are getting more attention of researchers to make life of mankind comfortable. The concept is developing in economic countries but the reasons for non-popularity is the design complexity, cost of machines, and operational charges in terms of power tariff. In this paper, a manual floor cleaning machine is proposed. In early day a floor is clean by using a broom which is operated by human hand, in this a continuous movement of human hand is required which create fatigue and time consuming. The aim of this work is to develop and modernized process for cleaning the floor with wet and dry. This machine is capable of performing cleaning of floor in dry as well as wet condition, and it also have storage box to store a dust. This floor cleaning machine is designed by keeping the basic considerations for machine and efforts reduction, environment friendly and easy handling. The machine will work on solar power and there is no need of training to operate it. This work can be very useful to improve the life style of mankind.

The Socioeconomic attributes and Risk behaviour of Small–Scale Oil Palm Processors (Millers) in Akwa Ibom State, Nigeria

This study examines the socioeconomic and risk behaviour of small–scale oil palm processors in Akwa Ibom State, the southern region of Nigeria. The multi-stage sampling technique was used to collect cross-sectional data from one hundred and twenty-five (125) micro-oil palm processors. The results revealed that the majority of the processors were married and males were the dominant sex. The average years of processing experience, age, membership in social organization and educational attainment stood at 11.43 years, 48.23 years, 2.78 years and 12.15 years respectively. The processors' average household size and annual income were 5 members and N319, 658.33 (at 1$ = N380.26 in 2020) respectively. The risk factors identified among the micro-oil palm processors include insufficient raw materials, poor public electricity, high cost of tools and machines and increased operation hazards. The result showed that oil palm processors in the region are risk-takers. The empirical results revealed that processors’ age, educational attainment, membership in social organization, household size, processing experience and processors’ income were significant factors influencing the risk behaviour of oil palm processors. To enhance the well-being of micro-oil palm processors in the region, emphasis should be devoted to the development of education attainment and social capital formation among others.

Theoretical aspects of production of sprinkler nozzles with a rotating deflector

In the materials of the article, the theoretical question of creating sprinkler nozzles with a rotating deflector cone is considered. Mathematical dependences describing the design and technological parameters of the sprinkler nozzle are presented. The process of raindrops falling created by a rotating deflector cone is considered. The results of laboratory studies are presented, during which it was proved that the irrigation radius increased by an average of 25.2% with a rain nozzle having a rotating deflector cone compared with a smooth non-rotating deflector cone. This will allow the sprinkler nozzles to be installed on the sprinkler machine pipeline at a greater distance from each other, which will reduce the cost of the sprinkler machine.

Machine learning-aided time and cost overrun prediction in construction projects: application of artificial neural network

Machine learning-aided time and cost overrun prediction in construction projects: application of artificial neural network

Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm

To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop scheduling for aerospace manufacturing enterprises is proposed. This paper considers the process flexibility of aerospace complex components comprehensively, and an integrated optimization model for the process planning and production scheduling of aerospace complex components is established with the optimization objectives of achieving a minimum makespan, machining time and machining cost. A honey-bee mating optimization algorithm (HBMO) combined with the greedy algorithm was proposed to solve the model. Then, it formulated a four-layer encoding method based on a feature-processing sequence, processing method, and machine tool, a tool was designed, and five worker bee cultivation strategies were designed to effectively solve the problems of infeasible solutions and local optimization when a queen bee mated to a drone. Finally, taking the complex component parts of an aerospace enterprise as an example, the integrated optimization of process planning and workshop scheduling is carried out. The results demonstrate that the proposed model and algorithm can effectively shorten the makespan and machining time, and reduce the machining cost.