Milling Operations Research Articles

Numerical assessment of tool geometry for improving productivity in milling stainless steel 316 L

Abstract Improving the material removal rate (MRR) can significantly enhance the efficiency of the milling operations during machining. However, increasing MRR develops a larger degree of stress and eventual wear at the cutting edge, reducing the tool’s lifetime, in particular for hard metals like stainless steel. Therefore, it is important to optimize the tool geometry to enhance the stress-carrying capacity under extreme cutting conditions. Considering a four-fluted tungsten carbide milling tool for cutting stainless steel, we propose in this study a procedure for reducing tool stresses by modifying the tool geometry. Using a systematic set of finite element simulations, we showed that the degree of stresses on the cutting edge can be reduced by optimizing three geometrical parameters, i.e., helix angle, rake angle, and cutting edge radius. To validate the simulation results, we manufactured 18 four-fluted milling tools with varying geometries and tested them by milling stainless steel 316 L under identical cutting conditions. The performance of each tool was ranked based on microscopic inspections of their cutting edges, showing a close agreement with the numerical simulation predictions. This study presents a procedure for modifying milling tool geometry to enhance performance under extreme machining conditions.

Milling of geosynthetic-reinforced asphalt layers: field operations and milling byproducts

With the increased use of geosynthetic interlayers, including geogrids, paving geocomposites and paving mats in asphalt rehabilitation works, an increase is also expected in projects involving the milling of asphalt layers that incorporate these geosynthetics. Although significant experience exists in milling conventional asphalt layers, such experience is limited when the milled material contains polymeric or fiberglass interlayers. Consequently, better understanding is needed on the differences in the milling process in asphalt layers with and without geosynthetics. In this study, an experimental field track was constructed at Salvador International Airport (Brazil), featuring five different geosynthetic-reinforced asphalt test sections that were milled after their construction. Evaluation of the field campaign results indicates that all geosynthetic interlayers proved to be “millable”. However, the milling operations exhibited varying milling efficiencies and resulted in milling byproducts (or Reclaimed Asphalt Pavement with Geosynthetics, G-RAP) with different physical characteristics. An average reduction of 18% in milling efficiency of reinforced asphalt layers was observed compared to that of unreinforced pavements. The G-RAP byproducts were found to include geosynthetic fragments of different sizes but maintained a particle size distribution similar to that of the control RAP. Furthermore, the collected G-RAP showed beneficial impact on mechanical characteristics of asphalt mixtures with 20% G-RAP, particularly in Marshall stability, flow, and indirect tensile strength, confirming their suitability for reuse in transportation applications.

Validation of a rheology-dependent PBM-DEM-CFD simulation model of a continuous vertical stirred mill operating under different conditions

Validation of a rheology-dependent PBM-DEM-CFD simulation model of a continuous vertical stirred mill operating under different conditions

Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques

Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction.

An Exploratory Study of the Surface Integrity of Various FRP Composites in Face Milling Operation

The use of glass, carbon and hybrid /glass/carbon epoxy polymer composites is widely used in the automotive, manufacturing and aerospace sectors. Milling is a secondary production process in which the final shape of the product is produced with the desired shape and high dimensional accuracy. The objective of this study is to enhance our knowledge of the machinability properties of composite materials in the secondary manufacturing industry. In this research study, performing face milling operations with different tools on bidirectional (BD) glass, unidirectional (UD) glass, carbon/epoxy, and glass/carbon/epoxy (hybrid epoxy) polymer composites. This experimental work derives conclusions that contribute to the improvement of the milling process for better surface quality. Taguchi’s L16 Design of Experiments (DOE) and Analysis of Variance (ANOVA) are used to determine the effect of tool type, speed of spindle, rate of feed, cut depth on surface roughness and delamination factor. And it was concluded that glass/carbon hybrid epoxy polymer laminates showed improved surface quality when milled with a Poly Crystalline Diamond (PCD) tool at parametric combinations of rate of feed is 300 mm/min, speed of spindle is 1000 rpm, and cutting depth is 0.5 mm. In addition, the experimental results of the scanning electron microscope examination were also verified. And found excellent machined surface quality and least damage with the above optimal process parameter combinations.

SAG’s Overload Forecasting Using a CNN Physical Informed Approach

The overload problem in semi-autogenous grinding (SAG) mills is critical in the mining industry, impacting the extraction of valuable metals and overall productivity. Overloads can lead to severe operational issues, including increased wear, reduced grinding efficiency, and unscheduled shutdowns, which result in financial losses. Various strategies have been employed to address SAG mill overload, from real-time monitoring to predictive modeling and machine learning techniques. However, existing methods often lack the integration of domain-specific knowledge, particularly in handling class imbalance within operational data, leading to limitations in predictive accuracy. This paper presents a novel approach that integrates convolutional neural networks (CNNs) with physics-informed neural networks (PINNs), embedding physical laws directly into the model’s loss function. This hybrid methodology captures the complex interactions and nonlinearities inherent in SAG mill operations and leverages domain expertise to enforce physical consistency, ensuring more robust predictions. Incorporating physics-based constraints allows the model to remain sensitive to critical overload conditions while addressing the challenge of imbalanced data. Our method demonstrates a significant enhancement in prediction accuracy through extensive experiments on real-world SAG mill operational data, achieving an F1-score of 94.5%. The results confirm the importance of integrating physics-based knowledge into machine learning models, improving predictive performance, and offering a more interpretable and reliable tool for mill operators. This work sets a new benchmark in the predictive modeling of SAG mill overloads, paving the way for more advanced, physically informed predictive maintenance strategies in the mining industry.

Evaluating Fiber Losses with NIRS DA1650 and Determination Coefficient

This study focuses on enhancing the yield of crude palm oil (CPO) during the pressing process by thoroughly examining the oil losses that occur throughout production. The primary aim is to evaluate how different pressures and electric currents impact oil losses from palm fiber at a specific palm oil mill in Pantai Cermin, Kec. Tapung, Kampar, Riau. A systematic methodology was employed to achieve this, which involved detailed measurements conducted using the FOSS NIRS DA1650. This advanced technology allowed for precise assessment and quantification of oil losses during the pressing phase. Following the data collection, a rigorous statistical analysis was performed utilizing determination coefficients to interpret the relationship between the variables. The analysis results revealed a coefficient of determination (R²) of 49.96% concerning pressure, suggesting that nearly half of the variability in oil losses can be explained by fluctuations in pressing pressure. Additionally, the examination of current showed a higher coefficient of determination of 60.09%, underscoring a substantial influence of electric current on fiber oil losses. These findings highlight the critical importance of optimizing pressure and current in palm oil extraction. By making informed adjustments to these parameters, mill operators can significantly reduce oil losses, thus enhancing the overall extraction efficiency. The study provides practical recommendations for operators aiming to improve their processes, ultimately contributing to better resource utilization and increased profitability in the palm oil industry.

Improving operation of a drawing mill

The report considers the purpose of drawing mills and possible violations of the technological process associated with the design flaws of drawing mill drive. We analyzed the design of a planetary gearbox with a common carrier used in the drive of the stretching drum of a drawing mill. During the operation of such a transmission, there are disadvantages: due to the imbalance of links of the mechanism relative to the central axis, additional dynamic forces arise. This design transmits movement from the leading link to the carrier only through one satellite, the teeth of which perceive all the force transmitted by the torque, which reduces the reliability of the gearbox and the drive as a whole. The design of a three-satellite balanced self-aligning planetary gearbox, free from these disadvantages, is described.

An Interval Fuzzy Programming Approach to Solve a Green Intermodal Routing Problem for Timber Transportation Under Uncertain Information

This study investigates an intermodal routing problem for transporting wood from a storage yard of the timber harvest area to a timber mill, in which the transfer nodes in the intermodal transportation network have multiple service time windows. To improve the environmental sustainability of timber transportation, a carbon tax policy is employed in the routing to reduce the carbon emissions. Uncertain information on the capacities and carbon emission factors of the transportation activities in the intermodal transportation network is modeled using interval fuzzy numbers to enhance the feasibility of the routing optimization in the actual timber transportation. Based on the above consideration, an interval fuzzy nonlinear optimization model is established to handle the specific routing problem. Model defuzzification and linearization are then conducted to obtain an equivalent formulation that is crisp and linear to make the global optimum solution attainable. A numerical experiment is conducted to verify the feasibility of the proposed model, and it reveals the influence of the optimization level and service time windows on the routing optimization, and it confirms that intermodal transportation is suitable for timber transportation. This experiment also analyzes the feasibility of a carbon tax policy in reducing the carbon emissions of timber transportation, and it finds that the performance of this policy is determined by the optimization level given by the timber mill and is not always feasible in all cases. For the case where a carbon tax policy is infeasible, this study proposes a bi-objective optimization that can use Pareto solutions to balance the economic and environmental objectives as an alternative. The bi-objective optimization further shows the relationship between lowering the transportation costs, reducing the carbon emissions, and enhancing the reliability on capacity and budget by improving the optimization level. The conclusions provide managerial insights that can help the timber mill and intermodal transportation operator organize cost-efficient, low-carbon, and reliable intermodal transportation for timber distribution, and support sustainable forest logistics.

Techno-Economic Analysis of Near-Infrared (NIR) Systems at Feed Millsas a Low-Cost, High-Speed Alternative to Feed Ingredient Testing

Improperly balanced diets not only impact the quality of animal production but also the profits of a livestock operation. Typically, the nutrient and chemical content of feed ingredients and forages are determined using well-established wet chemistry tests. However, these tests can be expensive and time-consuming. Moreover, the increasing use of various by-products which are known to have large variations in chemical and nutrient content warrants a real-time on-site feed and forage testing system. Near infrared (NIR) spectroscopy systems are quick and effective on-site testing tools. While NIR systems are being adopted for on-farm or at feed mill ingredient and forage testing, little is known about the economic impacts of such an investment for a livestock or feed mill operator. This study developed a baseline model and an Excel based spreadsheet application for performing Return on Investment (ROI) analysis to determine the feasibility of using an on-farm or at feed mill NIR testing system. ROI was calculated based on the nutrient cost saved or spent determined from the difference of estimated nutrient content and actual calculated value. The findings from this study will promote low-cost alternatives for onfarm or at feed mill ingredient testing, positively impacting quality of animal products and minimizing costs.

Robust Adaptive Control System of Variable Sampling Period for Cement Raw Mix Quality Control

The advanced quality control of the raw mix remains a priority for the cement industry, particularly in recent years, where large quantities of alternative fuels and raw materials are used in clinker production, aiming to reduce the CO2 footprint. This study presents an adaptive control system with a variable sampling period for regulating raw mix quality in the raw mill (RM) output in a process with four independent inputs and four outputs: the lime saturation factor (LSF), silica modulus (SM), alumina modulus (AM), and SO3. The three pillars of the system are (1) mill dynamics calculation using exclusively industrial data, (2) the design of the controllers to meet robustness criteria, and (3) performance enhancement through simulators. Our technique periodically adjusts the gains of the controllers based on the mill’s dynamic parameters, which are computed from raw mix laboratory analyses. The presented results correspond to more than 14,000 h of mill operation. The standard deviation of the LSF at the mill outlet ranged from 1.5 to 3, which is equivalent to 1 to 2 standard deviations of LSF reproducibility. The standard deviation of the other moduli was close to the corresponding reproducibility of each. The presented adaptive gain-scheduling controller for LSF can be applicable to a broad range of raw meal grinding systems.

Analysis of anthropogenic transformation of the hydrographic network in the northern part of Lviv according to cartographic sources

The emergence of new and densification of existing urban spaces in Lviv leads to anthropogenic transformation of the hydrographic network. The purpose of the study was to analyse the degree of transformation of water objects in the northern part of the city based on cartographic sources. The study covered the northern part of Lviv, the town of Dublyany, the villages of Malekhiv, Velyki Hrybovychi, Zbyranka and Murovane. The study of water bodies was carried out with topographic maps of the scale range from 1:25,000 to 1:100,000, which made it possible to cover a time period of more than 240 years. Clear trends to decrease the length of open channels, the number of tributaries and the water content of the Holoskivskyy, Zboivskyy and Malekhivskyy streams, which is due to both natural and anthropogenic factors had been marked. The length of the natural channel of the Holoskivskyy stream decreased from 4.7 to 0.9 km, the Zboivskyy stream ‒ from 5.2 to 3.2 km, and the Malekhivskyy stream ‒ from 7.9 to 5.3 km. The largest number of ponds (15 units; 10.3 hectares) on streams was found in the second half of the 18th century. Subsequently, a decrease in both the number and area of ponds was observed. The lowest figures fall on the interwar Polish period, when only four ponds with a total area of 2.5 hectares were recorded. During the Soviet period, the number and area of ponds increased slightly (9 units; 4.6 hectares) and reached the level that existed in the second half of the 19th century. There were 14 water mills operating on most of the ponds of that time located in the villages of Holosko Velyke, Zamarstyniv, Zboyishcha and Malekhiv. After that, the number of mills steadily decreased: at the beginning of the 19th century ‒ nine; in the second half of the 19th century ‒ seven; in the interwar period – five. After the World War II, the operation of all water mills was stopped. The conducted analysis of the degree of transformation of the hydrographic network is aimed at correcting the development plan of the northern part of the city and implementing the environmental management system

Расчетно-экспериментальное исследование и идентификация матричной модели струйного измельчения сыпучих материалов

Grinding powders in jet mills makes it possible to obtain materials uncontaminated by wear products of grinding media to prepare pure products in chemical and pharmaceutical industries. It should be noted that for different technologies, the requirements for the granulometric composition of powders vary significantly, and to meet these requirements, specific approaches are often required. Experimental determination of optimal conditions to obtain new powders is often associated with significant costs, which is not always justified from an economic point of view. Mathematical modeling allows us to obtain effective solutions in a shorter time and at lower costs in most cases. Thus, the development of adequate models, methods of calculation and optimization of jet grinding technology is an urgent scientific and practical task. To simulate grinding, a matrix methodology is used to describe technological processes. To identify the model, the results of specially conducted experimental studies are used. During experimental studies, the granulometric compositions of powders after single and multiple grinding in a jet mill have been determined. The results obtained have been used to identify and verify the matrix grinding model. Within the framework of the resulting model, a method for technological calculation of a jet mill operating in an open grinding cycle has been developed. The presented approach allows us to propose the ways to develop and implement new jet grinding technologies to obtain highly pure products in open and closed grinding cycles.

Extent of Locally Sourced Feed Ingredients and Performance of Feed Mill Entreprises in Southwest, Nigeria

Promoting local sourcing of raw materials for firms in Nigeria has become one of the critical areas of government policies. In achieving this, the government introduced a ban on the importation of grains/cereals, which are critical ingredients in feed production for the feed mill industry. Hence, this study examines the extent (share) of locally sourced feed ingredients and its implications on the performance of feed mills in Southwest, Nigeria. Primary data used for this study was analysed using descriptive statistics and fractional probit regression. Firm’s age, quantity of feed produced and location of the feed mills significantly determined the share of locally sourced feed ingredients in feed production of feed mills in the region while level of education of feed mill operators, firm’s age, quantity of feed produced and proportion of locally sourced feed ingredients had significant effect on the performance (profitability) of feed mill entreprises in the area. It was therefore recommended that policies on improving infrastructure, production of feed ingredients, and addressing insecurity should be prioritised rather than promoting a ban on the importation of critical feed ingredients for the feed mill industry in Nigeria.

Material Removal Rate of Face-Milled Bevel Gears Based on a Ring-Dexel Cutting Simulation

Abstract Face milling is not only a primary machining technique for the mass production of bevel gears, but it has also become a standardized process integrated into computer numerical control (CNC) bevel gear-cutting machines in the last two decades. Controlling suitable feed rates for face milling is one of the most direct and pivotal factors influencing processing efficiency for CNC machines. Despite being programmed through numerical codes, the feed rates provided by the gear machines most rely on experiential insights rather than optimization. Therefore, leveraging material removal rate (MRR) directly correlates with machining power and will hold immense potential for optimizing feed rates and enhancing efficiency. Because commercial software solutions cannot accurately predict the MRR for face milling operations, this paper uses a novel ring-dexel-based model for cutting simulation to address this issue. The main aim of this model is to provide a more precise prediction of the MRR across all face milling cuttings. By controlling the cutting depth and generating angle, the ring gear's plunging process and the pinion's single-roll generating process were successfully simulated. Thus, the MRRs through all cutting processes were calculated. Experimental results showed that tool torques are positively correlated with the MRRs. Finally, by appropriately increasing the cutting feed rate based on the MRR, the pinion and ring gear machining times were reduced by 44% and 18%, respectively.

On influence of microstructural anisotropy of additive manufactured structures upon machining dynamics: An example of milling of Ti6Al4V thin-walled parts

Thin-walled structures, as common component specification for aerospace components, easily experience deformation and chatter issues during machining (e.g. milling) operations. With Additively Manufactured (AM) materials being increasingly applied in these areas, it is found that their microstructural anisotropy can lead to varied mechanical properties and machinability in different directions. However, former research on thin-walled parts mainly focused on bulk materials, while the machining performance of these thin-wall structures produced from AM processes remains unclear. In this view, this research aims to reveal the effect of microstructural anisotropy resulting from AM process on the machining (deformation and stability) of thin-walled parts by taking Ti6Al4V as an example. Three kinds of AM Ti6Al4V thin-walled parts were fabricated with different laser scanning strategies, in which the prior columnar β grains were found to grow along the building direction and led to a variation in mechanical properties. Owing to this, the deformation and machining stability varied in the AM thin-walled parts with different β phase crystallization orientations on the same milling directions, which could be attributed to the changes of cutting force and dynamic parameters (e.g., frequency, stiffness (determined by elastic modulus), damping ratio) in different orientations. This investigation could provide a reference for the selection of printing strategies and follow-up machining process of AM thin-walled parts when considering their microstructural anisotropy.

Operational Modal Analysis of Self-excited Vibrations in Milling Considering Periodic Dynamics

Abstract A new method is presented to identify the dynamics of regenerative chatter from measured process vibrations in milling. This method combines the synchronous once-per-revolution sampling of process vibrations with Operational Modal Analysis to estimate the Floquet multipliers of the delayed linear time-periodic dynamics in milling, all from the natural process vibrations without external excitation. The identified multipliers quantify vibration stability, enabling chatter prediction before it occurs. In addition to this, they can be used to calibrate physics-based chatter models based on vibration measurements solely within the stable region. The method's accuracy in identifying Floquet multipliers is validated through extensive numerical simulations and two experimental case studies. The results show that chatter due to both Hopf and period-doubling bifurcations can be predicted from the process vibrations during stable cuts. Moreover, the experimental case studies demonstrate a vibration measurement system for implementing the presented method in standard milling operations and confirm its effectiveness in practice.

An experimental study of the response characteristics of end milling operations and their finite element simulation

The present study investigates the effect of milling parameters on cutting forces and performance characteristics in end mill operations using two different types of end milling tools. The end milling experiments were performed on AA7075-T6 grade aluminium alloy using a vertical milling machine fitted with a Kistler dynamometer to acquire the data of cutting forces during the milling operation. A matrix of process parameters was created by varying spindle rotational speed in the range of 500–1200 rpm while the feed rate was kept between 10 and 30 mm/min, keeping a maximum depth of cut of 4 mm. The data obtained from the experimental results were analysed and validated by simulation using the DEFORM-3D software facility. The experimental results reflect that with the increase in cutting speed, the cutting force first increases and then decreases, while an increase in feed rate enhances the cutting forces. The combination of high cutting speed with low feed rate was found to be suitable as the force required in this case exhibits a minimum to achieve both efficiency and machinability for a tool. Validated experimental results can be useful for finding new combinations of parameters in the defined parameter range for milling applications.

Batch-sizing and machinability data systems for milling operations: An optimal sustainable cost of quality approach

Nowadays, manufacturers make every effort to achieve a higher quality of their products at an attractive cost. With all the introduced legislation and incentives in the developed world to address global warming, machining shops in the West also strive to cut greenhouse emissions. This article offers an optimal approach to the micro Computer-Aided Process Planning (CAPP) problem to optimize the internal quality cost and buffering effect while keeping the environmental impact low. To optimize the machining parameters, the mathematical model is developed for different milling operations, face, side, and peripheral. cutting speed, feed rate, axial depth of cut, radial depth of cut, nose radius, and batch sizing while maximizing profit and meeting customer demand. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated and solved using Classical Constrained Nonlinear Optimization (CCNO) and Genetic Algorithms (GAs). Surface roughness, used as a metric to evaluate the desired quality level of a finished machined part type, is modeled as a Gaussian random variable to model the surface roughness of the machined part utilizing a cumulative normal distribution. The ratio of rework and scrap is calculated in terms of the surface roughness of the machined part shifting away from the target and exceeding upper and lower specification limits. The internal failure cost model, addressing both scrap and rework, is developed based on Taguchi’s quadratic loss function. CCNO is employed to validate the results obtained by GAs, relaxing the lot-sizing integrality constraint and, thus, the convexity of the produced relaxed model. An iterative method employing a developed multi-regression model is used to solve for the expended power consumption (an inherent highly nonlinear environmental criterion of the developed model) within both GAs and CCNO. This study reveals that the machining parameters substantially impact the cost components of the objective function as well as the scrap and rework quantities. A stringent quality cost target can force the model to optimize the feed rate and nose radius to minimize the internal failure quality cost while improving the environmental impact, including direct and indirect power consumption and CO2 emissions considerations.

Numerical investigation on machining of additively manufactured CFRP composite with different build orientations and layer widths

Additive manufacturing (AM) is now a widely researched manufacturing technology in the past two decades to adopt in industry for its added advantage of customization and adopting complex geometries with comparatively low buy-to-fly ratio. Carbon fiber reinforced polymer (CFRP) composite has found applications in different high-performance industries for its greatest benefit of high strength-to-weight ratio. Additive manufacturing of CFRP composite (AM-CFRP) opens up the possibility of enhancing mechanical strength using different printing orientations and enables producing complex shapes maintaining sustainable manufacturing perspective. However, Limitation of AM parts of having surface irregularities and questionable dimensional accuracies. To use AM-CFRP parts in high precision assembly or industrial applications, post processing machining is often required to meet the customers’ specification of geometrical tolerances and acceptable surface finish. In this study, the influence of AM parameters on machinability of AM-CFRP composite has been evaluated using finite element analysis (FEA) based numerical simulation. The slot milling operation was simulated with a tungsten carbide end milling tool and AM-CFRP workpiece with four different printing directions, i.e., 0°-90°, 45°-135°, 0°-90°-45°-135°, two different layer widths, i.e., 50 µm and 100 µm, and two in-fill patterns, i.e., solid and perforated structures. The machinability of the 3D printed CFRP has been analyzed based on cutting forces, stress at first contact and maximum stress generation, and temperature increases at the interface during slot milling of AM-CFRP under different AM parameters. Evolution of failure mechanisms of AM-CFRPs under various machining conditions, such as, delamination, matrix rupture etc., have been discussed and chip and burr formation mechanisms have been analyzed. Finite Element analysis (FEA) package ABAQUS/Explicit was used to model 3D micro slot milling operation of AM-CFRP workpiece with appropriate damage and constitutive models, such as, damage initiation, progression and cohesion in adjacent passes and layers.