Affect Production Efficiency Research Articles

Development and Design of an Online Quality Inspection System for Electric Car Seats

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats.

RANCANG BANGUN MESIN PEMURNIAN VIRGIN COCONUT OIL (VCO) DENGAN METODE SENTRIFUGAL BERBASIS INTERNET OF THGINGS (IoT)

Virgin Coconut Oil (VCO) is a virgin coconut oil produced from the extraction process of fresh coconut meat without the use of chemicals or high temperatures. VCO has high economic value with various health benefits. The process of making VCO involves a centrifugal machine to separate the oil from coconut milk. However, the control of the VCO Purification machine still relies on manual intervention by the operator, which can lead to human error and affect production efficiency. In order to improve efficiency and reduce the risk of human error, Internet of Things (IoT) and Mobile Application technologies can be applied in the control of VCO Purification machines. The programming language used is PHP because it can be accessed on various platforms, especially Mobile. The sensor used is an IR (Infrared) speed sensor for measuring rotational speed. The Mobile Application can be a convenient interface for operators to control and record the operation history of the VCO purification machine. This research has succeeded in developing an IoT and Mobile Application-based control system that is effective in controlling VCO purification machines, increasing production efficiency, and reducing the risk of human error.

Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

Dynamic matching of energy and heat conduction in production workshops and financing for manufacturing enterprises based on the Internet of Things

With the advancement of Industry 4.0, the application of Internet of Things technology in manufacturing enterprises is increasingly extensive, and energy heat transfer, as an important factor affecting production efficiency and cost, needs to be optimized by new technical means. This study explores the energy heat conduction characteristics of production workshops based on the Internet of Things and its impact on the dynamic matching of financing for manufacturing enterprises, providing an innovative energy management model to promote the sustainable development of enterprises. The research uses the Internet of Things technology to monitor the energy flow and heat transfer in the production shop in real time through sensors and data acquisition systems. Combined with big data analytics and machine learning algorithms, in-depth analysis of energy consumption data is performed to identify bottlenecks and optimization points for heat transfer. At the same time, the financing dynamic matching model is constructed to analyze the financing needs and costs of enterprises under different energy management levels. The study found that iot technology can significantly improve the thermal efficiency of production plants and reduce energy waste. On the basis of the optimization of energy heat transfer, the financing demand and financing cost of enterprises have also undergone dynamic changes. The optimization of energy heat transfer based on the Internet of Things not only improves the energy efficiency of the production floor, but also provides a new impetus for the financing of manufacturing enterprises. Implementing an effective energy management strategy will help companies stay ahead of the curve in an increasingly competitive market.

Analisis Persediaan Bahan Baku Susu pada Produksi Keju British Cheddar dengan Metode Economic Order Quantity ( EOQ ) di PT. Mazaraat Lokanatura Indonesia Pasuruan

To achieve profit, companies usually process raw materials into finished or semi-finished goods. Therefore, in this company, controlling and managing raw materials becomes an important factor influencing the smooth production process to achieve desired goals. Cow's milk is one of the popular agricultural products, and cheese is a dairy product consumed globally. PT Mazaraat Lokanatura Indonesia is a company that produces local Yogyakarta cheese and creamery products. The instability of organic milk quality from local farmers often negatively impacts the production process and the final quality of the company's British Cheddar Cheese products. This instability also affects raw material inventory planning, so the company must be more careful in maintaining the quality and quantity of organic milk stock to ensure optimal production. To address this issue, an analysis of raw milk inventory using the Economic Order Quantity (EOQ) method was conducted. This research began with observations and identification of problems related to raw material inventory management that could affect production efficiency and operational costs. Then, data collection was conducted regarding the demand amount, order time, ordering cost, and milk storage cost. The research results show that the application of EOQ is proven effective in optimizing inventory costs. Using EOQ allows the company to manage the optimal order quantity, significantly reducing total ordering costs by decreasing the order frequency.

Building information modeling-based production process optimization model

In the current manufacturing process of enterprises, there are some problems such as poor predictability and low level of intelligence, which lead to high product error rate and affect production efficiency. Therefore, this paper introduces the building information model in the field of engineering construction, and proposes a big data predictive manufacturing model based on the building information model, which divides the production process into production service system, resource planning system, production control system and after-sales service system, and realizes the overall process optimization of planning-production-sales on the basis of the close combination of each system and virtual model system. Finally, the application of error correction process in production line is verified from an empirical point of view, which provides a reference method and path for reducing production error rate and improving work efficiency.

Fault diagnosis of complex industrial equipment based on chunked statistical global-local feature fusion

Abstract Industrial process equipment is bulky and complex in structure, which is easy to produce faults during operation and affect production efficiency, cause huge economic losses, and even threaten the safety of workers. To achieve sustainable operation of large-scale industrial processes, timely and accurate monitoring and handling of abnormal situations are essential. However, fault monitoring of large equipment requires the collection of abundant data, which includes many complex related variables, resulting in excessive redundant data generated during the fault monitoring process. Moreover, the existing principal component analysis (PCA) method can only retain the global characteristic of variance information, and cannot obtain the local characteristic that can characterize the topological relationship between the data points, which affects its monitoring reliability and intelligence level. In response to these issues, a fault diagnosis model for complex industrial processes based on chunked statistical global-local feature fusion (CSGLFF) is proposed in this paper. First, considering the correlation characteristics between industrial process variables, a correlation variable chunking method mutual information-based is designed to merge the variables with small correlation to obtain the optimal chunking of variables. Second, PCA and locality preserving projections (LPP) are combined to construct a global-local feature fusion (GLFF) model that can extract global and local features simultaneously. The chunked data are imported into the GLFF for the extraction of its features respectively, and the corresponding CSGLFF is established. In addition, Bayesian inference is used to fuse the statistics of each sub-chunk to establish an overall fault monitoring statistical indicators, and the reason for failure is found through the variable contribution graph. Finally, two cases of Tennessee Eastman process (TEP) and laboratory emulsion pump were used to conduct experimental research on the performance of CSGLFF. The results show that compared with the chunked statistical PCA, chunked statistical LPP, and GLFF algorithms, The accuracy of fault monitoring for TEP mean, flow pulsation impact, and pressure anomaly of this method reached 92.91%, 97%, and 90.30%, respectively. It has good monitoring effect in processing data with large variables, reducing the generation of redundant data, improving the accuracy of industrial monitoring, and accurately identifying the relevant variables of fault occurrence. This provides a theoretical basis for determining the fault location and points out the direction for maintenance by staff.

Problems and Countermeasures in the Water Injection Development Process of Strong Water Sensitive Sand Conglomerate Reservoir

Due to the unique physicochemical property of strong water sensitive glutenite reservoirs, a series of problems may occur during water injection development, seriously affecting production efficiency and economic benefits. This study analyzed the problems and their causes in the water injection development process, constructed a water injection optimization model based on machine learning algorithms, and rigorously verified and evaluated the performance of the model. The results show that the algorithm proposed in this article has achieved significant advantages in prediction accuracy, reducing prediction error by 28.26%, and maintaining a high recall. In addition, as the sample size increases, the algorithm in this article performs more stably. When facing complex reservoir environments and a large amount of data, this algorithm can provide more accurate and reliable prediction results, providing strong support for the optimization of reservoir water injection development process. The research results can provide an effective decision support tool for the field of petroleum engineering, which helps to improve the efficiency and economy of oil extraction, and promote the sustainable growth of the petroleum industry.

A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data

Before leaving the factory, lithium-ion battery (LIB) cells are screened to exclude voltage-abnormal cells, which can increase the fault rate, troubleshooting difficulty, and degrade pack performance. However, the time interval to obtain the detection results through the existing voltage-abnormal cell method is too long, which can seriously affect production efficiency and delay shipment, especially in the mass production of LIBs when facing a large number of time-critical orders. In this paper, we propose a data-driven voltage-abnormal cell detection method, using a fast model with simple architecture, which can detect voltage-abnormal cells based on the multi-source time series data of the LIB without a time interval. Firstly, our method transforms the different source data of a cell into a multi-source time series data representation and utilizes a recurrent-based data embedding to model the relation within it. Then, a simplified MobileNet is used to extract hidden feature from the embedded data. Finally, we detect the voltage-abnormal cells according to the hidden feature with a cell classification head. The experiment results show that the accuracy and average running time of our model on the voltage-abnormal cell detection task is 95.42% and 0.0509 ms per sample, which is a considerable improvement over existing methods.

Integrative analysis of the ovarian metabolome and transcriptome of the Yaoshan chicken and its improved hybrids.

Introduction: Laying performance is a key factor affecting production efficiency in poultry, but its molecular mechanism is still indistinct. In this study, Yaoshan chickens, a local breed in Guizhou, China, and merchant chickens (GYR) with higher egg yield after the three-line cross improvement hybridization of Yaoshan chickens were used as animal samples. Methods: To explore the regulatory mechanism of the diversities in laying performance, RNA-seq and ultra-performance liquid chromatographytandem mass spectrometry (UPLC-MS/MS) were used to describe the transcriptional and metabolic profiles of the ovaries of Yaoshan and GYR chickens. Results: At the transcriptional level, 288 differentially expressed genes were upregulated in Yaoshan chickens and 353 differentially expressed genes were upregulated in GYR chickens. In addition, GSEA showed that ECM-receptor interactions and the TGF-β signaling pathway were restrained, resulting in increased egg production in GYR chickens. Furthermore, the upregulation of thiamine and carnitine was identified by metabolomic analysis to facilitate the laying performance of hens. Finally, comprehensive analyses of the transcriptome and metabolome found that thiamine and carnitine were negatively correlated with ECM-receptor interactions and the TGF-β signaling pathway, which jointly regulate the laying performance of Yaoshan chickens and GYR chickens. Discussion: Taken together, our research delineates differences in the transcriptional and metabolic profiles of the ovaries of Yaoshan and GYR chickens during the peak egg production period and provides new hypotheses and clues for further research on poultry egg production performance and the improvement of economic benefits.

Optimisation of steel rolling schedule based on evolutionary multi-tasking transfer algorithm

Strip rolling is an important part of steel processing. The load distribution scheme in the rolling process directly affects production efficiency and product quality. A dynamic rolling process with changing speed is investigated to improve quality and reduce energy use. To balance the iterative calculation of the complex industrial evolution process and the requirements of high real-time, this study examine the rolling process by combining the multivariate, multi-constraint, and strong coupling characteristics of field measured data. On this basis, several conflicting rolling optimisation objectives in the process of rolling schedule optimisation were analysed, and a dimension reduction migration model based on transfer component analysis was established. In case of an inefficient transfer of feasible solutions, a multi-objective multifactorial evolutionary algorithm based on an explicit transfer solution strategy (MOMFEA-ETS) was proposed. The proposed algorithm obtained four average distance optimal values for eight practical rolling programming problems.

The Influence of ERP-SAP Implementation and Inventory Management on Production Efficiency through Inventory Control Performance Using Partial Least Square (PLS) Method

PT XYZ is the largest manufacturing company in Indonesia that produces biscuits with the support of the SAP system, which has integrated data from each division, especially the inventory control division. Due to the accessibility of real-time data, it was observed that there were two late arrivals of raw materials, causing the company to only achieve 75% of the weekly production target. Therefore, this study aims to determine the effect of ERP-SAP implementation and inventory management on smooth production and provide recommendations for improvement through inventory control performance at PT XYZ. The method used is Partial Least Square (PLS) which uses SmartPLS. Based on the research conducted, it was found that the implementation of ERP-SAP does not have a positive impact on production flexibility, but has a positive impact on inventory management performance, and then inventory management directly positively affects production efficiency and inventory control performance

СТАНДАРТИЗАЦІЯ ЛЛЯНОЇ СИРОВИНИ: ПОРІВНЯЛЬНИЙ АНАЛІЗ ДОСВІДУ США ТА УКРАЇНИ

The main problem in the primary processing of bast fiber raw materials is Ukraine's significant lag in the field of standardization compared to the USA, which negatively affects production efficiency, product quality, and competitiveness in the global market. The article aims to provide a comprehensive comparative analysis of flax raw material quality assessment systems in Ukraine and the USA, identify the key reasons for Ukraine's lag, and develop practical solutions to address these issues. The research found that while Ukraine still relies on outdated standards for flax fiber quality evaluation, which are primarily based on organoleptic methods and do not take into account modern requirements, the USA is actively developing and implementing innovative standards that regulate a wide range of quality indicators, such as fiber fineness, strength, color, shive content, and other important parameters. The main reasons for Ukraine's lag in the field of flax standardization include persistent economic difficulties, obsolete infrastructure and equipment, insufficient funding for scientific research, and weak integration with international standardization organizations. The authors propose a set of solutions to overcome these challenges, which involve increasing state support for the flax industry, intensifying research efforts aimed at developing new methods for assessing the quality of flax raw materials, harmonizing national standards with international ones, deepening international cooperation, and fostering the development of industry associations and clusters. The article emphasizes the necessity of introducing modern instrumental methods for assessing the quality of flax raw materials, such as spectrophotometry for color measurement, computer vision systems for analyzing fiber structure, and automated methods for determining fineness and shive content. The importance of developing standards for new and innovative flax products to expand the range, improve the competitiveness of domestic products, and enter promising markets is underlined.

Comparative Analysis of the Ultrastructure, Bubble Pores, and Composition of Eggshells of Dwarf Layer-White and Guinea Fowl.

The decrease in eggshell quality seriously affects production efficiency. Guinea fowl (GF) eggs possess strong eggshells because of their unique crystal structure, and few systematic studies have compared laying hen and GF eggs. Sixty eggs were collected from both 40-week-old Dwarf Layer-White (DWL-White) laying hens and GF, and the eggshell quality, ultrastructure, bubble pores, and composition were measured. The results showed that the DWL-White eggs had a higher egg weight and a lower eggshell strength, strength per unit weight, thickness, and ratio than the GF eggs (p < 0.01). There were differences in the mammillary layer thickness ratio, the effective layer thickness ratio, the quantity of bubble pores (QBPs), the ratio of the sum of the area of bubble pores to the area of the eggshell in each image (ARBE), and the average area of bubble pores (AABPs) between the DWL-White and GF eggs (p < 0.01). The composition analysis demonstrated that there were differences in the organic matter, inorganic matter, calcium, and phosphorus between the DWL-White and GF eggs (p < 0.01). There were positive associations between the mammillary knob number in the image and the QBPs and ARBE and a negative correlation with the AABPs in the DWL-White eggs (p < 0.01). This study observed distinctions that offer new insights into enhancing eggshell quality.

Adaptive control method and experimental study of cone crusher based on aggregate online detection

The size of the discharge outlet of a cone crusher directly impacts the size of the aggregate produced. However, the discharge outlet is still adjusted manually, which has a significant error and affects production efficiency. For this reason, this study proposed an adaptive control method for cone crushers based on aggregate online detection. Firstly, the aggregate image was segmented using an instance segmentation model and the anchor and structure of the model were optimised. Then, this study proposed an evaluation method for quickly and accurately assessing the overall segmentation effect of network models. By comparing the results with those before optimisation, the accuracy of the optimised network model was improved from 0.923 to 0.940. Finally, an adaptive control experiment was conducted based on the online aggregate detection results. The experimental results showed that the discharge particle size distribution of the cone crusher becomes more stable after intelligent control is added, with the variance of the proportion of cumulative gradation at 15 mm decreased from 34.3 to 14.4. These results indicated that the developed adaptive control system effectively controls the fine processing of coarse aggregates and significantly improves the quality of aggregate crushing and processing.

Identifying the Quantitative Trait Locus and Candidate Genes of Traits Related to Milling Quality in Rice via a Genome-Wide Association Study.

Milling quality directly affects production efficiency in rice, which is closely related to the brown rice recovery (BRR), the milled rice recovery (MRR) and the head milled rice recovery (HMRR). The present study investigated these three traits in 173 germplasms in two environments, finding abundant phenotypic variation. Three QTLs for BRR, two for MRR, and three for HMRR were identified in a genome-wide association study, five of these were identified in previously reported QTLs and three were newly identified. By combining the linkage disequilibrium (LD) analyses, the candidate gene LOC_Os05g08350 was identified. It had two haplotypes with significant differences and Hap 2 increased the BRR by 4.40%. The results of the qRT-PCR showed that the expression of LOC_Os05g08350 in small-BRR accessions was significantly higher than that in large-BRR accessions at Stages 4-5 of young panicle development, reaching the maximum value at Stage 5. The increase in thickness of the spikelet hulls of the accession carrying LOC_Os05g08350TT occurred due to an increase in the cell width and the cell numbers in cross-sections of spikelet hulls. These results help to further clarify the molecular genetic mechanism of milling-quality-related traits and provide genetic germplasm materials for high-quality breeding in rice.

Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources

Considering increasingly serious environmental issues, sustainable development and green manufacturing have received much attention. Meanwhile, with the development of economic globalization and requirement of customization production, distributed hybrid flowshop scheduling problem (DHFSP) and assembly shop problem (ASP) have widely existed in realistic manufacturing systems. In addition to machine resources, worker resources are a key element affecting production efficiency. However, previous studies have not considered the integration mode of DHFSP, ASP, and worker resources in green manufacturing systems. Therefore, this paper focuses on an energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources (EDAHFSPW) for the first time. To solve this problem, a mixed-integer linear programming (MILP) model and a multi-objective memetic algorithm (MOMA) are proposed with minimization the total tardiness (TTD) and total energy consumption (TEC) objectives. In MOMA, a speed-related decoding method is developed to improve the quality of solutions. To generate excellent initial solutions, an initialization strategy is proposed based on problem characteristics. A local search strategy is presented to improve the exploitation capability. An energy-saving strategy is designed to further optimize TEC. Additionally, to validate the proposed MILP model, we implement CPLEX to solve it on 12 small-sized instances. To verify the effectiveness of the proposed MOMA, extensive experiments are conducted to compare with other 5 comparison algorithms on 90 large-sized instances. Experimental results illustrate that MOMA is superior to its competitors.

Intelligent Sinter Machine Speed Control System Using Optimized Fuzzy Logic Controller: An Experimental Study in Iron and Steel Plant

Intelligent control systems developed for production facilities significantly contribute to production efficiency and quality. Using intelligent control systems has now become a necessity in iron and steel sintering plants that produce millions of tonnes annually. Automatic control of the sinter machine speed, which directly affects production efficiency and quality, is one of the first issues to be addressed. The complexity of the sintering process, being affected by many variables, and the nonlinearity of these variables make it difficult to control the machine speed. This study demonstrates that we have overcome this challenge using a fuzzy logic controller (FLC), which is optimized with an adaptive neuro-fuzzy inference system (ANFIS). The FLC we have designed operates with the characteristic point of the thermal state, the mixture level, the vacuum average, and the current speed parameters. We achieved an average success rate of 95%. The developed system automatically controls the speed of the sinter machine with high accuracy, independent of the operator. The system we have developed is used continuously at the Iskenderun Iron & Steel Co. sinter plant. The results obtained from the production facility show that the developed system captures the thermal change in the sinter pallet and manages the machine accordingly, increases the sintering efficiency by at least 10%, and ensures process safety. These results revealed that the developed system can be used effectively in the iron and steel industry and the use of the system will increase efficiency.

Design and test of an efficient seedling pick-up device with a combination of air jet ejection and mechanical action

Low degree of transplanting automation will affect production efficiency and planting quality in vegetable cultivation. A new seedling pick-up device was designed and constructed to reduce direct grasping damages to seedlings and improve transplanting efficiency. The pick-up device consists of an air jet loosening device, a flexible pick-up manipulator, a parallel feeding device, and a multi-axis motion control system. Its working principle is to use air jet ejection to assist in loosening of seedling roots from the tray cells, grasp their stems for extracting with the pick-up manipulator, and finally transfer them to the delivery device for feeding into the planting device as needed. The mechanical structure and working parameters of each component were designed, and the control system was constructed according to the working requirements of ejecting, extracting, transferring, and discharging operations. A prototype of the new pick-up device was constructed, and its performance evaluation was conducted using an orthogonal experimental design using cucumber, pepper and caluiflower as test objects. The results showed that the test object, the root lump's moisture content and the loosening way (either as a whole or individual loosening of seedlings) had significant effects on the success ratio in picking up seedlings. Overall, the success in picking up seedlings from the cell was found to be influenced by horticultural and mechanical factors. Under the optimal level group, the maximum success ratio for automatic picking up seedlings was up to 94.49% for pepper while that of cucumber and cauliflower recorded 90.75% and 92.62%, respectively. The seedling pick-up performance was satisfactory for efficient transplanting requirements.

Transcriptome analysis of adipose tissue and muscle of Laiwu and Duroc pigs

Fat content is an important trait in pig production. Adipose tissue and muscle are important sites for fat deposition and affect production efficiency and quality. To regulate the fat content in these tissues, we need to understand the mechanisms behind fat deposition. Laiwu pigs, a Chinese indigenous breed, have significantly higher fat content in both adipose tissue and muscle than commercial breeds such as Duroc. In this study, we analyzed the transcriptomes in adipose tissue and muscle of 21-d-old Laiwu and Duroc piglets. Results showed that there were 828 and 671 differentially expressed genes (DEG) in subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), respectively. Functional enrichment analysis showed that these DEG were enriched in metabolic pathways, especially carbohydrate and lipid metabolism. Additionally, in the longissimus muscle (LM) and psoas muscle (PM), 312 and 335 DEG were identified, demonstrating enrichment in the cell cycle and metabolic pathways. The protein–protein interaction (PPI) networks of these DEG were analyzed and potential hub genes were identified, such as FBP1 and SCD in adipose tissues and RRM2 and GADL1 in muscles. Meanwhile, results showed that there were common DEG between adipose tissue and muscle, such as LDHB, THRSP, and DGAT2. These findings showed that there are significant differences in the transcriptomes of the adipose tissue and muscle between Laiwu and Duroc piglets (P < 0.05), especially in metabolic patterns. This insight serves to advance our comprehensive understanding of metabolic regulation in these tissues and provide targets for fat content regulation.