milling production line optimization

a green method for production of nanobiochar by ball milling- optimization and characterization - sciencedirect

a green method for production of nanobiochar by ball milling- optimization and characterization - sciencedirect

Biochar with particle size of 212nm was obtained at optimized milling conditions.Keeping biochar at80C before milling reduced the size to around 60nm.The surface area of nanobiochar increased 15 times compared to the raw biochar.The produced nanobiochar can be used for removal of carbamazepine from wastewater.

Environmental considerations along with the technological challenges have led to search for green and energy-efficient processes for advanced nanostructured materials. In this study, nanobiochar was produced from pine wood biochar using a planetary ball mill. A central composite experimental design and response surface methodology was employed to optimize the ball milling parameters including time, rotational speed and ball to powder mass ratio to obtain nanoparticles in short time and at lower energy consumption. ANOVA results showed that the linear and quadratic effect estimates of time and the interaction effect of time and rotational speed were significant contributors to the size of particles during milling (p<0.05). Based on the developed statistical model, the optimum conditions for obtaining the smallest particles, around 60nm, were found to be 1.6h, 575rpm and 4.5g/g. However, the size measurements indicated that particles had a great tendency to agglomerate. Further study showed that the conditioning of biochar at cryogenic temperatures prior to milling inhibits the agglomeration of nanoparticles which is essential in industrial processes. The adsorption test proved that the nanobiochar produced using green method is promising in the removal of micropollutants from aqueous media by removing up to 95% of carbamazepine from water. At the optimum milling parameters and conditioning for 24hat80C, nanobiochar with the average size of around 60nm was obtained. The produced nanobiochar was characterized by Brunauer-Emmett-Teller (BET) gas porosimetry, scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Also, physical and chemical properties, such as water holding capacity, organic matter, oxidation-reduction potential (ORP), elemental composition, polycyclic aromatic hydrocarbons (PAHs) and heavy metals were analyzed.

intelligent sequence optimization method for hole making operations in 2m production line | springerlink

intelligent sequence optimization method for hole making operations in 2m production line | springerlink

This paper is concerned with the optimization of the tool path in a production line consisting of two machine tools. Existing computer numerical control (CNC) time estimation methods are based either for a single machine or a single operation. As current methods dont illustrate the necessity of the multiple operations with more than one machine, this paper presents a new method for CNC machining time estimation which predicts the optimal tool path sequence with minimal time for a 2M production line. The optimized sequence is determined by employing the most reliable hybrid method i.e., Genetic Algorithm (GA). Attention was focusing on the hole making operations where a hole may need multiple cutting tools to get the process finished. Each of the machines can do certain set of operations. So the non-productive time between two machines should be minimized and it is obtained by this intelligent sequence optimizer. This proposed technique is developed on a modified travel salesman problem algorithm with preceding constraints. The work also introduces a computational program based on this methodology. The numerical simulation conducted in this research shows that the proposed approach is feasible and practical. It is beneficial especially in real-time manufacturing process outlining and scheduling multiple systems.

an effective and automatic approach for parameters optimization of complex end milling process based on virtual machining | springerlink

an effective and automatic approach for parameters optimization of complex end milling process based on virtual machining | springerlink

The demand for optimization of manufacturing processes rises as a reflection of the highly competitive market environment that requires shorter lead time and lower production costs. Although some approaches to milling process optimization have been developed based on analytical model using average cutting parameters, they are not available for complex workpieces when cutting parameters are time-varying and instantaneous cutting conditions need to be considered. In order to automate the optimization process and avoid costly machining tests, in this paper, an effective approach for parameters optimization of complex end milling process based on virtual machining is proposed. A computer-aided design (CAD)/computer-aided manufacturing (CAM) application is integrated for actual tool path generation and feedrate scheduling based on material removal rate. Then, a machining simulator based on octree and instantaneous force model is developed to evaluate feasibility of the given numerical control (NC) program, and the correctness of this simulator is verified by machining tests. The optimization process is controlled by the efficient global optimization method to find global optimal solution with fewer simulations and less computation time. During each iteration of the optimization process, NC programs are generated and evaluated automatically by the CAD/CAM application and the simulator, respectively. The effectiveness and efficiency of the proposed approach are proved by comparing the generated optimal solution (has reduced machining time and production cost) with the recommended cutting parameters from machining experts when machining an impeller.

Aggarwal, S., & Xirouchakis, P. (2013). Selection of optimal cutting conditions for pocket milling using genetic algorithm. The International Journal of Advanced Manufacturing Technology,66, 19431958.

Alajmi, M. S., Alfares, F. S., & Alfares, M. S. (2019). Selection of optimal conditions in the surface grinding process using the quantum based optimisation method. Journal of Intelligent Manufacturing,30, 14691481.

Bharathi Raja, S., & Baskar, N. (2012). Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Systems with Applications,39, 59825989.

iek, A., Kvak, T., & Ekici, E. (2015). Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills. Journal of Intelligent Manufacturing,26, 295305.

Corso, L. L., Zeilmann, R. P., Nicola, G. L., Missell, F. P., & Gomes, H. M. (2013). Using optimization procedures to minimize machining time while maintaining surface quality. The International Journal of Advanced Manufacturing Technology,65, 16591667.

El-Mounayri, H., & Deng, H. (2010). A generic and innovative approach for integrated simulation and optimisation of end milling using solid modelling and neural network. International Journal of Computer Integrated Manufacturing,23, 4060.

Ferry, W., & Yip-Hoi, D. (2008). Cutter-workpiece engagement calculations by parallel slicing for five-axis flank milling of jet engine impellers. Journal of Manufacturing Science and Engineering,130, 51011.

Fountas, N. A., Benhadj-Djilali, R., Stergiou, C. I., & Vaxevanidis, N. M. (2017). An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence. Journal of Intelligent Manufacturing,46, 811.

Fountas, N. A., Vaxevanidis, N. M., Stergiou, C. I., & Benhadj-Djilali, R. (2014). Development of a software-automated intelligent sculptured surface machining optimization environment. The International Journal of Advanced Manufacturing Technology,75, 909931.

Ginta, T. L., Amin, A., Radzi, H., & Lajis, M. A. (2009). Tool life prediction by response surface methodology in end milling titanium alloy Ti6Al4V using uncoated WCCo inserts. European Journal of Scientific Research,28(4), 533541.

Karunakaran, K. P., Shringi, R., Ramamurthi, D., & Hariharan, C. (2010). Octree-based NC simulation system for optimization of feed rate in milling using instantaneous force model. The International Journal of Advanced Manufacturing Technology,46, 465490.

Kondayya, D., & Krishna, A. G. (2012). An integrated evolutionary approach for modelling and optimisation of CNC end milling process. International Journal of Computer Integrated Manufacturing,25, 10691084.

Kurt, M., & Bagci, E. (2011). Feedrate optimisation/scheduling on sculptured surface machining: A comprehensive review, applications and future directions. The International Journal of Advanced Manufacturing Technology,55, 10371067.

Li, L., Liu, F., Chen, B., & Li, C. B. (2015). Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network. Journal of Intelligent Manufacturing,26, 891898.

Palanisamy, P., Rajendran, I., & Shanmugasundaram, S. (2007). Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. The International Journal of Advanced Manufacturing Technology,32, 644655.

Silva, J. A., Abelln-Nebot, J. V., Siller, H. R., & Guedea-Elizalde, F. (2014). Adaptive control optimisation system for minimising production cost in hard milling operations. International Journal of Computer Integrated Manufacturing,27, 348360.

Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2014). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing,25, 14631472.

Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Evolutionary techniques in optimizing machining parameters: Review and recent applications (20072011). Expert Systems with Applications,39, 99099927.

Ma, H., Liu, W., Zhou, X. et al. An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining. J Intell Manuf 31, 967984 (2020).

increasing performance of the milling operation | | miller magazine

increasing performance of the milling operation | | miller magazine

The increasing performance of the mills can be classified into two groups. One of them is increasing performance by organization and the other is increasing performance by milling technique. Both are important and different from each other. If both are supplied, maximum performance can be obtained.

Abstract Milling is used by different industries such as food, chemistry, mining, pharmacology, construction and materials etc. About 60-70% of the food industry is related to solid processing. The solid processing industry is also used the milling operation. Milling is known as size reduction or comminution, which is made by using different principles such as compression, impact, attrition/rubbing and cutting. During milling, as depend on the principles, the different forces are used like shear and compression etc. In this article, the milling principles and milling performance were evaluated and the increasing performance of milling was analyzed.

1. Introduction The main objective in the milling operation is the decreasing size of materials by creating new surface. Therefore, the creation of a new surface means that the requirement of energy. The ideal size reduction, milling or comminution should have a high capacity, small power requirement, high yield and producing uniform size distribution. The optimum values for each requirement can be determined theoretically, however actual values may be different due to processing conditions, design, parameters, raw materials, operators and handling.

The main difficulty for the milling operation is to the lack of the perfect scale-up procedure. The scale-up procedure means transferring laboratory or bench-type study results to industrial application. The scale-up is very useful method to make good optimization. In scientific studies, distillation, evaporation, agitation and reaction systems are easily scaled-up. However, in the literature, the scale-up methodology for the solid system or milling has still been completed. Due to the lack of perfect scale-up methodology in the milling industry, research and development, industrial application, monitoring and diagnosis are problem. Therefore, the measurement of performance of the milling operation is not perfectly parallel to the studies in the industrial scale.

The increasing performance of the mills can be classified into two groups. One of them is increasing performance by organization and the other is increasing performance by milling technique. Both are important and different from each other. If both are supplied, maximum performance can be obtained.

1.1. Increasing performance by organization Today, we are talking about Industry 4.0. The start of industry (Industry 1.0) is also accepted as the discovering of the steam engine. But, this is not true. The mills are the start of the industry by water and/over wind mills. Because there were available before steam engines and they were the first mechanical industry. During developments in the milling techniques, it revolved to a business or industry. Today, the milling industry especially in the food sector is one of the biggest industry such as wheat flour, semolina, pulses, rice, bulgur, ready-to-eat-soup, etc. In industry, size reduction or milling are widely used processing steps. The major milling industry or locomotive of this branch is flour, semolina, corn and starch industries.

By the growing industry and capacities, the milling industry is also turning to a big business and organization. However, the development of organization in the milling industry is weaker than the other food industry. Therefore, the increasing performance of the milling industry should be starting with the development of organization and structure of this sector.

In a regular mill, the organization depends on a family structure. Additionally, the main departments in these companies are accounting, buying-selling, security and process. However, in the developing sectors, these departments are only a part of the main structure. When the structures and organizations are analyzed, it can be seen that 90% of the mills do not have the following departments such as research and development, finance, process control, quality control, logistic, import-export, ERP and IT etc. If these departments cannot be adapted to the main organization and also not activated or supported, the mill cannot be developed and it will lose its sustainability.

Firstly, the milling companies should start to new organization and restoration. Especially, these new departments would be established in the organization. According to the knowledge obtained from the experience and other industries, these departments are key-factor for the future, value-added product obtaining, economics, performance and sustainability.

1.1.a. Research, Development and Innovation The growing every time depends on the innovation. Especially, in the food industry due to the changing of the living-style, the consumer prefers new products. Therefore, product-developments with innovation is critical issue for the future of the milling industry. Additionally, research is a key to generate new gate. Either product development or technology-process development can be achieved by this department and the result every time will be value-added production.

1.1.b. Finance, Import-Export, Logistic The economic management and power of the milling industry depend on the management of the finance. In general, the companies confuse the accounting department with finance. However, both are exactly different from each other. In general, milling plants use international raw materials and their products are almost not domestic i.e. international. Therefore, the management of goods, money, cash position, revenue, asset and risk, the finance will be critical. Most of the milling companies lost due to lack of the not enough finance knowledge. Similarly, the milling industry is not a domestic industry, it is a part of the international/global economy. Therefore, export-import and logistics are other required depts.

1.1.c. Process Control By Industry 4.0, all industries started to change. Today, instead of classical or manual processing; artificial neural networks, simulation, internet of things and data management are preferred for the industry. The studies show that there is no chance of manual systems against to process control system.

When classical or manual milling control analyzed, it can be obtained that there is a big fluctuation in quality and capacity in the production (Figure 1a). Additionally, due to the limitation of manual control, the production is significantly under the maximum limit or available/possible capacity (Figure 1b). When the ideal process control systems are preferred (Figure 2), process fluctuation will be lower and production capacity will be higher than the manual systems.

1.1.d. Quality Control By increasing the consumer demands, quality standards are increased day-by-day by the authorities and consumers. In old times, some basic quality parameters related with the process (physical, chemical, rheological properties) were measured and monitored. However, today and in the future, the most important quality parameters will be related to food safety. Therefore, microbiological, toxicological and other safety parameters should be supplied by the producers. A new powered dept. will be quality-control depts.

1.1.e. ERP and IT Growing industries and companies needs a new management system such as ERP. The big companies completed their transfer to ERP and IT systems. Therefore, the milling industry by growing to increase their performances, ERP and IT will be their new big steps to the future.

1.2. Increasing performance by technology Increasing performance by the technology of the milling system basically depends on raw materials, processing conditions, process control, processing parameters, monitoring and diagnosis, energy management, correct equipment, trained person, operator, engineering, quality control, correct design, technology level and yield (Figure 2).

There are important factors to obtain maximum milling performance. If all reach to the optimum level, the maximum performance can be achieved in the milling operation. Analyzing each one needs a long explanation.

Grain milling can be divided into two sections. One is cleaning and the other is size reduction or milling line. Technically, the cleaning section can affect the performance and yield of the milling by adjusting the equipment and good design practice (GDP). The cleaning section directly affects the yield and performance. Depends on the cleaning ratio and dirtiness of raw material, the operational conditions and equipment design and parameters also affect the further milling performance.

In the cleaning section; pre-cleaning sieve, magnet, destoner, light particle gravity table, trior (intendent cylinder) were some of the basic machines. First to all, their engineering design and their design parameters directly affect the performance and yield of milling. In the industry, )0% of the machines are copied and there is no engineering design. They may run however their performances are very bad due to the lack of design works. Due to this main problem in the design of the cleaning machine and conveyors, a lot of capacity, quality, performance and energy losses occur. In the cleaning section, there are mechanical, air, pressure, flow rate, capacity, angle, solid handling problems. Each parameter/variable for each equipment should be analyzed and engineering should be applied. For example, table area, angle of table, air pressure and air flow rate in the destoner are critical. However, when these parameters/variables are measured and analyzed, a lot of mistakes can be obtained due to the lack of engineering design principles and studies.

Additionally, the same mistakes related to table area, table angle, air flow rate and pressure can be seen in the gravity table. Trior (intendent cylinder) is another critical step in the cleaning section. Due to wrong whole, whole shape and angle, revolution, centrifugal force, sorting area etc. the loss of product of insufficient cleaning can be obtained. At each cleaning step, the milling performance decreases due to these kinds of problems.

In the milling section, physical and chemical properties and events should be followed to obtain good performance. The milling section in the wheat milling starts with tempering operation. Before the dehulling, grain is tempered. A new technological approaches also started to change the dehulling operation, especially in flour and semolina milling. Recently, vertical emery stone dehullers are started used in the processing. Tempering conditions (moisture content, time, RH%, temperature and raw material properties) are mainly important for milling performance. They should be under control. Especially, tempering will determine the yield and quality of the finished product.

In the milling operation, another step is the mill. Design of mill, corrugation, revolution, temperature, teeth size and shape, diameter of roll, gap, distribution and hardness of roll are the main parameter to get high performance from the mill.

After the mill, the next important equipment is sieve. Its diagram and positioning, vibration ratio or rotational ratio, sieve aperture, sieve material, surface sieving area, the opening of holes, passage between each plate sieve, centrifugal force and gravitational force balance over the sieve are the most important parameters to control the performance of the milling operation.

During the milling operation particle size distribution and energy calculation should be made. Unless they are smoothed by abrasion after milling, comminuted particles resemble polyhedrons with nearly plane faces and sharp edges and corners. The particles may be compact with length, breadth and thickness nearly equal or they may be platelike or needlelike. For compact grains, the largest dimension or apparent diameter is generally taken as the particle size. For particles that are plakelike or needlelike, two dimensions should be given to characterize their sizes.

Energy is an important expense in milling. During milling, the particles of feed material are first distorted and strained. The work necessary to strain them is stored temporarily in the material like mechanical energy of stress. Then, as additional stress added, they are distorted beyond their ultimate strength and suddenly rupture into fragments, and a new surface is generated. So, a unit area of material has a definite amount of surface energy, the creation of a new surface requires work that is supplied by the release of energy, the creation of new surface requires work that is supplied by the release of energy of stress when particle breaks. By conservation of energy, all energy of stress in excess of the new surface energy created must appear as heat. Therefore, the temperature of the mill surface during comminution should be cooled or controlled to prevent overheating. Also, the ratio of the surface energy created by milling to the energy absorbed by the solid is the crushing efficiency. The surface energy created by fracture is small in comparison with the total mechanical energy stored in the material at the time of rupture and most of the latter is converted into heat. Milling efficiencies are therefore low. They should continuously be measured and controlled. The energy absorbed by solid is less than that fed to the machine. Part of the total energy is used to overcome friction in the bearings and other parts of the machine, and the rest is available for milling. The ratio of the energy absorbed to the energy input is mechanical efficiency. In size reduction or milling, Rittinger, Kick and Bond equation can be used to make the calculation.

As a result, the milling performance depends on a lot of parameters. Therefore, a high knowledge is required to fix all parameters to the maximum level. In general, milling systems do not use high knowledge therefore a lot of loss in performance can occur.

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