Machine Capacity Research Articles

Performance Analysis of Disc Mill Type FFC-15 Grinder for Making Charcoal Husk Flour

This study is motivated by the great potential of rice husk as a waste material that has not yet been optimally utilized. The focus of this research is to evaluate the performance of the FFC-15 disc mill machine in producing flour from rice husk charcoal at three different moisture contents. The variations in the moisture content of rice husk charcoal studied were 10%, 15%, and 20%, with the goal of determining the optimal conditions in the production process of rice husk charcoal flour. The methodology used is experimental with repeated testing to produce data on machine capacity, electrical power consumption, flour yield, flour quality (moisture content, ash content, and mineral content), and the physical parameters of the charcoal. The results show that moisture content significantly affects the milling efficiency and quality of the flour produced, with optimal conditions achieved at a moisture content of 10%. Adjusting the moisture content before the milling process can enhance the operational efficiency of the machine and the quality of the final product. These findings are expected to provide benefits for follow-up by a group or industry that wants to optimize the utilization of rice husk charcoal. Keywords: Disc mill FFC-15, Flour quality, Husk charcoal, Milling efficiency, Moisture content.

Fuzzy flexible job shop dynamic scheduling considering machine remaining processing capacity

Dynamic fuzzy flexible job shop scheduling (DFFJSP) is significant in the medical equipment industry. Generally the dynamic flexible job shop scheduling problem considers a single dynamic factor or ignores resources such as machine itself also plays a huge role in dealing dynamic issues. Thus fuzzy processing time and machine breakdowns are considered in this paper. In order to copy with two problems mentioned above, the model of DFFJSP containing two objectives of makespan and energy consumption is established. To solve this problem, the remaining processing capacity of broken machine is applied to weaken the impacts of machine breakdowns and a multi-objective evolutionary algorithm (NG-WOA) is proposed, which integrating NSGA-II and whale optimal algorithm (WOA). Furthermore, the joining of variable neighborhood search makes NG-WOA skip the local optimum solution in time. Through experiments, the validity of remaining processing capacity is verified in rescheduling scheme. Then NG-WOA algorithm is proven can find better solutions when compared with other two algorithms according to the distribution of pareto forefront solutions in benchmark examples. The results of this paper show that the remaining processing capacity has greater advantages in guaranteeing the stability of dynamic scheduling problems, because the machine’s processing capacity can be utilized fully compared to previous strategies for fault disturbances.

Identical Parallel Machine Scheduling to Minimize Makespan Using Suggested Algorithm Method at XYZ Company

XYZ company produce the various shape of motor spare parts product. The company has threeidentical parallel spot welding machines that use a random method of production scheduling, basedon machine capacity without any sequence of jobs, and only use daily production targets given tooperators. Based on the data, the actual scheduling of the machines has a very large completion timedifference between each machine, or the machine loading is uneven. As a result, the makespanbecomes longer with a value of 440000 seconds (26 days). This research aims to minimize the existingmakespan by giving proposed scheduling, using the suggested algorithm method, which has a smallnumber of iterations and has an optimal result. The method begins with the longest processing timesequence rule which is used as the upper bound for the first iteration, then continued to calculate thelower bound and machine workload. The calculation stops at the 15th iteration because the completiontime value exceeds the lower and upper bound so that the optimal scheduling taken is scheduled inthe 14th iteration with a makespan value of 914412 seconds (16 days). The proposed scheduling canminimize the makespan from the actual schedule by 38%.

Toroidal mean flow and complex temperature distribution in a thermoacoustic core

Increasing the capacity of thermoacoustic machines (engines or refrigerators) requires increasing the diameter of the regenerator, which may result in non-uniform temperature distributions inside the regenerator. In the current study, the temperature is measured at different radial and axial locations inside the regenerator of a thermoacoustic refrigerator with a diameter of 148 mm. The working gas is a mixture of helium and Argon at a static pressure of 40 bar. Non-uniform radial temperature distributions are observed at different axial positions. Also, the predicted axial temperature distributions by a 1-D linear model (DeltaEC) do not agree with the measured temperature distributions. We suspect that the non-uniform radial temperature distribution generates a steady flow through the regenerator which increases the radial temperature non-uniformity and results in non-linear axial temperature distribution. Given the measured temperature distribution, the presence of a toroidal steady flow inside the regenerator is suspected. To confirm this hypothesis, the regenerator is divided into three segments connected in parallel in the DeltaEC model. Mean flows are added in each segment of the regenerator to simulate the presence of toroidal flow in the regenerator. The predicted temperature distributions from this modified DeltaEC model agree with the measured temperature distributions.

Dyeing scheduling optimization in a multi-machine system with resource constraints

Abstract Water consumption is one of the most important concerns for a textile dyeing company in the era of global energy revolution. Different from the traditional production scheduling which focuses on the production efficiency improvement, the dyeing scheduling emphasizes the constraints of resource consumption. Besides, different from the traditional production scheduling where the task relevance is seldomly considered, the colour depth heavily impacts the water consumption and the dyeing scheduling results, which aggravates the uncertainty and complexity when modelling the dyeing scheduling optimization. Therefore, how to select proper dyeing orders among a large number of orders and schedule them for the purpose of generating more profit is challenging. To address the above issue, an optimization model with objective of maximizing profit and consideration of water consumption, delivery due time, and machine capacity, is built, and genetic algorithm is adopted to solve the problem. A numeric experiment is conducted to validate the effectiveness of the proposed method.

Battery Analysis in Off Grid Solar Power Plant as A Power Source for AC Motor Automatic Coffee Roaster Machine Capacity 20 Kg

Solar power plants that utilize solar energy to be converted into electrical energy have several advantages compared to other power plants, namely that they do not produce air pollution, are available continuously and are available everywhere. Coffee is a commodity in the world that is cultivated in more than 50 countries. Two species of coffee trees that are generally known are Robusta Coffee and Arabica Coffee. Processing coffee before consumption goes through a long process, namely from harvesting coffee beans that are ready to harvest. Then proceed with drying before roasting. Coffee roasting machine that uses an AC motor. The motor gets a DC power source from a solar power plant then the inverter changes the DC voltage to AC. This coffee roasting machine is equipped with IoT to control the motor rotation remotely and monitor the temperature. The battery has the function of storing electrical energy produced by solar panels in the form of direct current energy. The energy stored in the battery functions as a backup, which is usually used when the solar panels do not produce electrical energy. The greater the capacity of the battery used, the longer the battery can back up the load used.

Fast and accurate modelling of twin-screw compressors: A generalised low-order approach

Twin-screw compressors (TSC) are commonly used in heat pump processes due to their robustness and flexibility. They exhibit two core properties, i.e. the swept volume and the built-in volume ratio (BVR), which heavily influence their capacity limits and off-design efficiency. This work presents a new low-order model (i.e. a polynomial model), which can accurately predict a TSC’s behaviour. The model uses the external pressure ratio and volumetric compressor inlet flow rate to calculate isentropic efficiency and compressor speed. The input parameters are normalised with a reference flow rate (calculated from the swept volume) and the BVR, respectively. This results in a generalised model of low numerical cost, which can be used for explorative studies independent of the specific machine size and BVR. A gain in computational speed by a factor of 375 is achieved compared to a semi-empiric reference model. The model displays very good predictive accuracy when used to predict the performance of machines with similar BVRs, but different sizes. A mean deviation from the manufacturer data of 4.29%, 0.88°C and 1.38% for the shaft power, the outlet temperature and the compressor speed can be observed, respectively. When there is a difference in size and BVR, the prediction accuracy is still reasonable but significantly declines for small and very large pressure ratios. Nevertheless, the proposed new approach extends the state-of-the-art by introducing a low-order model, which combines the advantages of low computational cost, high accuracy, physically correct predictions over a wide operational range and scalability to different machine capacities and BVRs. The validation for different fluids indicates a good general prediction accuracy relatively independent of the used fluid.

Bounded mixed batch scheduling with job release dates and rejection

This paper investigates a bounded mixed batch scheduling problem with job release dates and rejection. The machine processes a batch containing several jobs that their number does not exceed the machine capacity. For the case with a certain release date, we present a polynomial-time exact algorithm. For the case with a constant number of release dates, a pseudo-polynomial-time exact algorithm is proposed. For the general problem, we provide a 2-approximation algorithm and a polynomial-time approximation scheme.

Case Study Manufacture of Cotton Polo Shirts and Jumpers

Objective: Explore the manufacturing process of tangüis cotton polo shirts and jumpers from a Peruvian textile company to optimize the process and obtain quality products that can compete in the international market. Theoretical Framework: The forecast demand and forecast control, the required and available capacity of workers and machines for the production process, the aggregate planning of the required workforce, the master production program, and the disaggregation of material requirements with the calculation of the corresponding lead-times. Method: Basic and applied hypothetical case study, transversal and exploratory, non-experimental, where the production process of cotton polo shirts and jumpers is simulated in one year considering their seasonality. Results and Discussion: Proposal for the demand estimation, forecast control, the required and available capacity of workers and machines necessary for the production process, the aggregate planning of the required workforce, the master production program, and the disaggregation of materials with the calculation of the corresponding lead-times. Research Implications: Be able to replicate the case in real situations of companies focused on textile manufacturing to determine their real estimations and propose proposals for continuous improvement. Originality/Value: The contribution to the literature of a case study that can be replicated to the reality of companies oriented to textile manufacturing to optimize their processes and contribute with their proposals for continuous improvement.

Application of an inertial vacuum trap for separation of the solid phase of carbon dioxide from the flow of fuel combustion products of boiler units

The technological process of low-temperature purification of fuel combustion products of boiler units from carbon dioxide by freezing it in a turboexpander and separating from the fuel combustion products flow in a separator is considered.The issue of separation of solid carbon dioxide from the flow of fuel combustion products of boiler units using a bag filter, an electric precipitator, a cyclone separator and an inertia-type separator is considered. Based on the analysis, the use of an inertial vacuum separator for separating the solid phase of carbon dioxide from the flow of fuel combustion products is proposed.Solidworks and ANSYS CFX modules were used for numerical modeling. To solve, the equations of continuity, motion, energy, and the k-ε model equations are used. The closing equation is for the effective and turbulent viscosity. There is also a description of the forces affecting the movement of the dispersed phase. The stream is considered lightly dusty.Numerical studies of the dispersed flow in the channel of an inertial-vacuum separator and in the previously used cyclone TsN-11 were carried out. The initial data for the calculation were taken based on the known mass flow rate and particle concentration, as well as from the continuity equation. From the results obtained, we can draw conclusions about a directly proportional relationship between the growth of the flow rate characteristics of the flow, the speeds along the flow path of the apparatus at the inlet and the resistance of the apparatus.Besides, based on the obtained calculation results, it was concluded that it is necessary to additionally install a rotary industrial air blower in order to create the necessary pressure drop in the inertial vacuum separator. The economic and environmental feasibility of installing a rotary air blower is consideredThe range of consumption of fuel combustion products has been determined at which the most effective purification of fuel combustion products from carbon dioxide in an inertial vacuum separator is possible within the technical characteristics of the refrigeration capacity of turborefrigeration machines.

Mean makespan task scheduling approach for the edge computing environment

Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics.

The F plasmid conjutome: the repertoire of E. coli proteins translocated through an F-encoded type IV secretion system.

Conjugation systems comprise a major subfamily of the type IV secretion systems (T4SSs) and are the progenitors of a second large T4SS subfamily dedicated to translocation of protein effectors. This study examined the capacity of conjugation machines to function as protein translocators. Using a high-throughput reporter screen, we determined that 32 chromosomally encoded proteins are delivered through an F plasmid conjugation system. The translocated proteins potentially enhance the establishment of the co-transferred F plasmid or mitigate mating-induced stresses. Translocation signals located C-terminally or internally conferred substrate recognition by the F system and, remarkably, many substrates also were translocated through heterologous conjugation systems. Our findings highlight the plasticity of conjugation systems in their capacities to co-translocate DNA and many protein substrates.

PENGARUH KAPASITAS MESIN DAN JUMLAH PERSEDIAAN BAHAN BAKU TERHADAP VOLUME PRODUKSI

Background of this research based on data’s observational at a company relating to capacity, amount of raw material inventory and production volume where the phenomena that occur still need to be optimized. This research uses a quantitative approach and uses primary data as a data source obtained from the results of questionnaires distributed to respondents related to activities in accordance with the specified research variables. The analytical methods used are instrument testing, classical assumption testing, multiple regression analysis, hypothesis testing and coefficient of determination (Adjusted R2). The results of this research show that:1. The machine capacity variable partially has a positive and significant effect on production volume at PT. Gema Graha Saran, Tbk Bekasi Regency.2. The raw material inventory variable partially has a positive and significant effect on production volume at PT. Gema Graha Sarana, Tbk Bekasi Regency.3. The variables of machine capacity and raw material inventory simultaneously have a positive and significant effect on production volume at PT. Gema Graha Sarana, Tbk Bekasi Regency Keywords :Raw Material Inventory, Machine Capacity, and Production Volume

Time is NTCP: Should we maximize patient throughput or perform online adaptation on proton therapy systems?

BackgroundCompared to conventional radiotherapy (XT), proton therapy (PT) may improve normal tissue complication probabilities (NTCP). However, PT typically requires higher adaptation rates due to an increased sensitivity to anatomical changes. Systematic online adaptation may address this issue, but it requires additional replanning time, decreasing patient throughput. Therefore, less patients would benefit in such case from PT for a given machine capacity, with results in worse NTCP. AimTo investigate the trade-off between PT patient throughput and NTCP gain as a function of the time needed for adaptation. MethodsA retrospective database of 14 lung patients with two repeated 4DCTs was used to compare NTCP values between XT and PT for NTCP2ym (2-year mortality), NTCPdysphagia and NTCPpneumonitis. Four scenarios were considered for PT: no adaptation using clinical robustness parameters (4D robust optimization, 3 % range error and PTV-equivalent setup errors); systematic online adaptation with clinical robustness parameters; setup errors reduced to 4 mm and to 2 mm. Dose was accumulated on the planning CT. The number of patients treated with PT depended on the extra time needed for adaptation, assuming an 8-hours capacity (assuming 14 patients a day; thus minimum 34.2 min per treatment session if there is no or instantaneous adaptation). ResultsBaseline NTCP gains (PT against XT without adaptation) equaled 6.9 %, 6.1 %, and 7.7 % for NTCP2ym, NTCPdysphagia and NTCPpneumonitis, respectively. Using instantaneous online adaptation and setup errors of 2 mm, the overall gains were then 10.7 %, 13.6 % and 12.4 %. Taking into account loss of capacity, 13.7 min was the maximum extra-time allowed to complete adaptation and maintain an advantage on all three metrics for the 2-mm setup error scenario. ConclusionThis study highlights the critical importance of keeping short online adaptation times when using systems with limited capacity like PT.

Characteristics of CPO Extraction Results from Spinner Machine Test Capacity 15-45 Kg for Home Industrial Scale

Purpose: This research aims to obtain the characteristics of CPO extraction results using a home industry spinner machine and determine the capacity of palm fruit suitable for CPO extraction using the spinner machine. Methodology: The spinner machine tested used a speed of 1800 rpm for 15 minutes, with capacity ratios of 15, 30, and 45 kg. The spinning process produces oil that will come out through the oil drain hole. Testing these three capacities will determine which one is more capable of producing CPO oil and is safe for engine rotation. Observing the characteristics of CPO involved yield tests, free fatty acid (FFA) tests, impurity content tests, and water content tests. Results: The yield test results ranged from 19.66% to 21.6%, the FFA test results ranged from 4.096% to 5.376%, the water content test results ranged from 0.2% to 0.6%, and the dirt content test results ranged from 2% to 5%. Findings: The 30 kg capacity was the most effective and efficient for CPO extraction using the home industry spinner machine. Novelty: The use of spinner machines in the home industry for CPO extraction has not been widely studied, and this research identifies the optimal capacity and efficiency of the spinner machine. Originality: This study provides new insights into the application of spinner machines for CPO extraction, highlighting their potential use in the home industry. Conclusions: The 30 kg capacity was the most effective and efficient for CPO extraction using the home industry spinner machine. Paper Type: Experimental Research Article Keywords: CPO, Extraction, home industry, spinner

Design and development of a detopping mechanism for onion detopping machine

Onion detopping is one of the unit operations involved in the cultivation of onion crop. Manual onion detopping is time consuming and highly drudgery. Therefore, an onion detopping machine was designed and developed for higher efficiency. An efficient detopping mechanism designed to achieve the maximum performance parameters in terms of detopping efficiency, per cent damage of onion bulbs and capacity of the onion detopping machine. By considering the crop parameters and functional requirement, four types of shearing designs of detopping tool namely, lead screw, square shaft with two cutting edges, square shaft with four cutting edges and helical roller were designed, developed and evaluated. Among four different shearing mechanisms, the helical roller type detopping tool was found to be effective in detopping the onion leaves. The helical roller type detopping tool had a detopping efficiency (98.44±1.07%), per cent damage (2.05±0.45%), conveying efficiency (97.60±1.02 %) and capacity (372.60±13.95 kg/h), respectively

Design and development of a detopping mechanism for onion detopping machine

Onion detopping is one of the unit operations involved in the cultivation of onion crop. Manual onion detopping is time consuming and highly drudgery. Therefore, an onion detopping machine was designed and developed for higher efficiency. An efficient detopping mechanism designed to achieve the maximum performance parameters in terms of detopping efficiency, per cent damage of onion bulbs and capacity of the onion detopping machine. By considering the crop parameters and functional requirement, four types of shearing designs of detopping tool namely, lead screw, square shaft with two cutting edges, square shaft with four cutting edges and helical roller were designed, developed and evaluated. Among four different shearing mechanisms, the helical roller type detopping tool was found to be effective in detopping the onion leaves. The helical roller type detopping tool had a detopping efficiency (98.44±1.07%), per cent damage (2.05±0.45%), conveying efficiency (97.60±1.02 %) and capacity (372.60±13.95 kg/h), respectively

Multi Aspect Optimization of Milling Machines : Review

Modern manufacturing supports various aspects of modern life. Modern manufacturing right now is focused on the application of artificial intelligence, acceleration of production, automation production, sustainability and environmental protection. A milling machine is a tool supports in a series of production processes. Its existence supports the production process which is part of the modern manufacturing system. In operating milling machines, several problems that occur, starting from operations, machine capacity, and predictions of machine or tool usage. This study summarizes and reviews previous studies in order to collecting types of milling machine optimization. Multi-aspect optimization obtained includes: optimization with a tool life prediction approach, optimization with a milling process approach, optimization with modification or retrofit, optimization with a production yield approach and finally optimization with operational time. With the results obtained, it is hoped that it can contribute to the optimization of milling machines in the modern manufacturing world. It is hoped that future research will be able to carry out in-depth analysis of each aspect of milling machine optimization has been mentioned.

Bottleneck identification and transfer prediction for automated production lines based on FNN

Abstract In a re-entrant production system, the throughput of the whole system depends on the capacity of the bottleneck machine. In this study, a new definition of bottleneck is proposed for a precision forging blade shop. The reinforcement learning algorithm is used to optimize the production scheduling to determine the most suitable scheduling scheme, which lays the foundation for bottleneck identification. Subsequently, the bottleneck identification index system was established according to the optimization objective, and the bottleneck identification problem was transformed into a multi-attribute decision-making problem. Finally, a fuzzy neural network is used for training, and the basic scheduling examples of each flow shop are utilized for bottleneck identification and prediction to verify their effectiveness.

A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model’s practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost.