Improve Production Efficiency Research Articles

Proactive Manufacturing Strategy for Managing the Multiproduct Production Plan

The case study focuses on a multi-product company in Indonesia that aims to develop a proactive manufacturing strategy to tackle production disruptions caused by process uncertainty. Data from 30 periods are analyzed to evaluate the company's weekly production plan performance, revealing consistent quantity fulfillment but significant discrepancies in the types of products produced compared to the plan. Process uncertainty, especially regarding quality, leads to disruptions such as uneven and inadequate material supplies. Statistical Process Control (SPC) tools, like Pareto analysis, are used to identify priority quality issues, forming the basis for Fault Tree Analysis (FTA) to trace root causes and determine intervention points. Each machine's limited set of valves and pipes poses challenges for thorough cleaning during downtime and quality management outside working hours. Introducing backup valves and pipes per machine can mitigate these issues and improve production efficiency. Companies can implement procurement strategies for pipes and valves to prevent dirty prints in the primary process, ensuring smooth material supply and minimizing disruptions. Although actual production plan achievement hasn't reached 100%, this strategy has the potential to increase product sales by 3.31% by reducing the rejection ratio of dirty prints. Ultimately, proactive procurement of spare pipes and valves is expected to increase production efficiency by 4%, indicating enhanced production schedule stability.

Identification of the FGB gene polymorphism and analysis of its association with fat deposition traits in Hu sheep

As a crucial economic trait, fat deposition is directly related to carcass quality and feed efficiency in sheep. The purpose of this study was to investigate the polymorphisms of the FGB gene related to fat deposition and detect the expression features of the FGB gene in different adipose tissues of sheep by using Sanger sequencing, MassARRAY® SNP technique, and quantitative real-time PCR (qRT-PCR). Results showed that in the intron region of the FGB gene, a SNP g. 3378953 A > T has been identified, and significant association was found between perirenal fat weight, perirenal fat relative weight, mesenteric fat weight, and mesenteric fat relative weight (P < 0.05). Moreover, qRT-PCR analysis showed that FGB was expressed in all three adipose tissues, and FGB gene expression level in the AA genotype was significantly lower than that in the AT or TT genotypes (P < 0.05). Therefore, the FGB gene can be used as a candidate gene to reduce fat deposition in Hu sheep breeding, and the selection of the AA genotype in Hu sheep in production practice is more conducive to improving production efficiency.

Effect of amylose partial extraction on citrate esterification of potato starch and its role in structure and physicochemical modification

This study examines the impact and influence of amylose on the starch esterification reaction through partial extraction of amylose. Citric acid was added for the esterification reaction, and then the esterified starches' multiscale structure, physicochemical, and functional properties were evaluated. As the extraction time of amylose increased, the amylose content in the starch decreased. Higher concentrations of citric acid will lead to samples with a higher degree of substitution, with DS rising from 0.203 % (0 h) to 0.231 % (3.5 h) at CA3 treatment. While removing amylose had minimal effects on the crystal structure of starch granules, it did decrease the ratio of A and B1 chains and the molecular weight of amylose. Acid hydrolysis exacerbated these changes upon the addition of citric acid. Furthermore, removing amylose followed by citrate esterification resulted in lower pasting viscosity, enthalpy of gelatinization (from 13.37 J to 2.83 J), and degree of short-range ordering. Also, digestion shows a decrease caused by the increasing content of slow-digesting starch. The presence of amylose in starch granules does affect the formation of starch esters, and removing it before esterification modification may improve production efficiency and reduce costs to some extent.

The fluency of human-robot collaboration based on human-robot fit

As an important working mode of current manufacturing enterprises, human-robot collaboration (HRC) is of great significance for enterprises to improve production efficiency and quality. However, unfortunately, there are still some problems in the practice of HRC, such as lack of fluency and low quality of HRC, which limit the positive role of HRC in manufacturing enterprises. Therefore, it is of great significance to study and reveal the influencing factors and formation mechanism of HRC fluency for further exerting the positive role of HRC and managing HRC in a targeted way. Building upon a review and analysis of existing literature, this study innovatively explores the formation process of human-robot collaboration fluence based on the theory of person-environment fit.

Image Recognition System for Mechanical Parts Based on SSD and YOLOv5

With the development of industrial automation, the importance of mechanical parts in the production process is becoming increasingly important. In recent years, deep learning technology has made breakthrough progress in fields such as image recognition and object detection. Deep learning models can learn a large amount of data through multiple epochs, automatically extract features from it, and achieve image recognition and object detection. This provides potent technical assistance for the design of mechanical component recognition systems. In industrial production, there are a wide variety of mechanical parts with complex shapes, and the differences between different models are often very small, making it difficult to identify them manually. Therefore, it is necessary to develop a system that can automatically identify mechanical parts to improve production efficiency and quality. This article takes mechanical parts as the research object and studies the recognition technology of mechanical parts based on deep learning and image recognition. The main content of the article includes the following aspects. Research was conducted on image quality enhancement methods for images with different characteristics, and different image preprocessing methods were applied to different images. YOLOv5 and SSD (Single Shot MultiBox Detector) algorithms are used to perform deep learning and recognition detection on the processed images, respectively. By comprehensively comparing the differences between the two algorithms, YOLOv5 was selected as the focus of this design, and based on this, deep training was conducted, and the code was imported into the embedded system.

Non-invasive rotor temperature detection of magnetically levitated turbo molecular pump based on high-frequency inductance

The rotor temperature has a significant effect on the reliable operation of the magnetically levitated turbo molecular pump (MLTMP). Therefore, it is crucial to monitor the rotor temperature during the operation of the MLTMP to prevent irreversible demagnetization of the permanent magnet and improve production efficiency. However, direct measurement of rotor temperature is a challenge due to the complexity of installing a temperature sensor on the high speed spindle. This paper presents a non-intrusive method for detecting rotor temperature based on high-frequency(HF) inductance estimation. On this basis, a novel method is proposed that injects a HF rotating voltage into the stationary coordinate frame and introduces synchronous demodulation to achieve accurate estimation of the HF inductance. Finally, the experimental results conducted on the MLTMP test bench indicate that the proposed method can effectively detect the rotor temperature, and the temperature error is within 3 ∘C.

PKG-DTSFLN: Process Knowledge-guided Deep Temporal–spatial Feature Learning Network for anode effects identification

In the aluminum electrolysis process, the accurate identification of anode effect (AE) can improve production efficiency. However, the existing methods fail to effectively capture the features of the anode current signal (ACS) due to its complex dynamic characteristics and temporal–spatial dependence. To address this issue, we propose a Process Knowledge-guided Deep Temporal–spatial Feature Learning Network (PKG-DTSFLN). We believe that knowledge and production data are complementary. Knowledge has potential to deduce beyond observational conditions. Data can be used to detect unexpected patterns. The combination of data and knowledge is potential to improve the performance. Specifically, knowledge is utilized to construct the adjacency matrix to represent the spatial structure of ACS. Then, a deep learning model is constructed by integrating the 1D-CNN and GAT, which is used to capture the temporal–spatial features of ACS. The experimental results on ACS dataset show that the accuracy is more than 99% with low computational cost.

Research on the Teaching Resource Construction System of Computer VR Online Interactive Experiment Platform for Mechanical Engineering Graphics

The introduction of three-dimensional design experiment teaching in engineering drawing teaching. The experimental platform of "network engineering drawing" is established, and the corresponding multimedia teaching software is developed using WebVR, ASP, database and other technologies. The 3D modeling method of the reducer is established by using UGNX and optimized accordingly. In order to solve the problem that UGNX mode introduces packet loss in the simulation system, the model and data are reconstructed separately. Using VisualStudio2019 as the development platform, the 3D modeling of the reducer is realized. The establishment of the engineering graphics design virtual simulation system helps enterprises to simulate operations before actual production and processing. It greatly improves production efficiency and reduces production costs.

The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence

Artificial Intelligence (AI) can improve production efficiency and general quality of life through assisting human labor, potentially leading to the conversion of employment types, enhancing industrialization, and upgrading energy structure. This paper enriches the role of AI in improving sustainable growth by curbing hypocritical sustainable and greenwashing behaviors. By accessing the panel data from Chinese listed-firms for the period 2014–2021, we have shown that AI can significantly mitigate the existence of greenwashing behaviors by raising the disclosure quality of ESG rating scores. Moreover, the role of AI in mitigating greenwashing behaviors performs significantly in SOEs, less pollution-intensive industries, high environmental regulation and less developed green finance regions. Furthermore, the potential mechanisms of AI in mitigating greenwashing behaviors are displayed, including alleviating financial constraints, easing management cost, improving green innovations.

Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI+3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI+3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production.

Application of Intelligent Algorithms and Big Data Analysis in Film and Television Creation

With the rapid development of social media, people can access a large amount of data in a short period of time, and big data technology has emerged. With the vigorous development of cloud computing and big data, the method of mining audience interests through a large amount of data to guide film and television creation has attracted more and more attention from experts and scholars. In order to understand the current situation of film and television drama creation in China and provide suggestions for the shortcomings in the industry, this article mainly analyzes the application of intelligent algorithms for traffic prediction models and big data analysis in film and television creation. This intelligent algorithm can predict the potential audience of movies or TV dramas, helping producers and investors make decisions. This system utilizes artificial intelligence technology to select suitable actors for characters based on their matching degree and past work performance. This article applies intelligent algorithms to big data processing to improve the accuracy of data processing. This article explores the application of intelligent algorithms and big data analysis in film and television creation. Using machine learning algorithms to predict the potential audience of a movie or TV series based on historical data, providing decision-making basis for investors and producers. This system utilizes AI technology to select suitable actors for characters based on their matching degree with characters and past work performance, improving the scientific and accurate selection of roles. The application of these technologies helps to improve production efficiency and quality, reduce costs and risks, and inject new impetus into the sustainable development of the film and television industry.

Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels

INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems.&#x0D; OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency.&#x0D; METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms.&#x0D; RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms.&#x0D; CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.

The Second-Round Effects of the Agriculture Value Chain on Farmers’ Land Transfer-In: Evidence from the 2019 Land Economy Survey Data of Eleven Provinces in China

In the context of the separation of three rights of land and agricultural modernization, this paper is based on the land economic survey data from eleven provinces in China in 2019, covering the eastern, middle, and western regions of China. Based on the value chain theory and its “second-round effect”, which pertains to the multi-round effects of value chain distribution theory, various research methods such as Probit, Tobit, the two-part model, SFA, PSM, and the intermediary effect model are employed to analyze the direct impact of the agriculture value chain (AVC) on farmers’ land factor inputs and the income effects caused by them, which are the “second-round effect” of the AVC on land factor inputs. The research results show the following: Firstly, the AVC has a significant positive impact on the behavior and area of farmers’ land transferring-in, which helps guide farmers towards large-scale land operation. Secondly, the AVC significantly improves farmers’ production efficiency and promotes land transfer through differences in production efficiency, representing the “second-round effect” mechanism of the AVC on land factor inputs. Moreover, the AVC will increase farmers’ net land production income by 48.74%, which is the “second-round effect” of the AVC on farmers’ agricultural income and also the motivation for farmers’ land factor inputs. Finally, the expansion of land area and the improvement of production efficiency jointly increase farmers’ agricultural income, among which production efficiency plays a partial intermediary effect in increasing agricultural income if farmers join the AVC. This paper believes that we should further promote the market-oriented reform of land factors, support the innovation of the benefit linkage mechanism of the AVC, and promote appropriate areas of land operation by farmers, thereby achieving common prosperity and promoting agricultural modernization in China.

Study on Characteristics of Steam Chamber and Factors Influencing Nitrogen-Assisted Vertical–Horizontal Steam Drainage Development

With the notable achievements attained through the implementation of steam-assisted gravity drainage (SAGD), the vertical–horizontal steam drive (VHSD) emerges as a pivotal technological advancement aimed at significantly enhancing the efficiency of thin reservoir heavy oil recovery subsequent to steam cyclic stimulation. The inclusion of nitrogen assistance has proven effective in enhancing the efficacy of gravity drainage techniques in reservoir development. However, it is noteworthy that this method has only led to improvements in approximately 50% of the well groups within the observed field. The comprehensive evaluation index of VHSD was proposed, and as the objective function, it was determined that the greatest contribution to the VHSD technique lies in oil saturation, accounting for 40% of the overall evaluations. This differs from conventional SAGD operations, where reservoir thickness serves as the primary determinant. Building upon an enhanced physical simulation similarity criterion, two comparative injection scheme experiments were conducted to explore the impact of nitrogen injection on the performance of VHSD and the characteristics of the steam chamber. Nitrogen is distributed in the vicinity of the steam chamber, leading to the formation of a dual mechanism characterized by ‘top heat insulation and lateral traction’ on the steam chamber. The lateral traction accounts for approximately 25% of the team chamber volume. Additionally, the inducement of nitrogen causes a downward displacement of crude oil, resulting in its accumulation within the high-temperature region of the steam chamber. This, in turn, enhances the contact area between the high-temperature steam and the crude oil, ultimately leading to improvement in production efficiency. Further validation of the impact of nitrogen on steam lateral traction and interlayer steam drainage within the reservoir was confirmed using Xinjiang oilfield testing. The well temperature increased from 75 °C to 130 °C.

Does applying financial engineering methods have an impact on improving production efficiency? Does applying financial engineering methods have an impact on improving production efficiency?

The study aims to identify the application of financial engineering methods and production efficiency in the General Fertilizer Manufacturing Company in Basra. To obtain the required information, a sample of (250) workers working at various administrative and production levels in the company was selected as a study population.The questionnaire was used as a main tool for collecting data, and to test the study hypotheses: There is a significant effect of the processes of applying financial engineering methods in improving production efficiency. To measure the effect, analyze correlations, and then arrive at the research results, the two statistical programs (SMART PLS) were used. The research reached a number of conclusions, most notably the existence of an impact of the processes of applying financial engineering methods on the factors influencing the improvement of production efficiency. The study concluded with a number of recommendations, including the necessity of applying financial engineering methods in the company in the study population to improve production efficiency.

Research on the Application of AGV Scheduling Strategy in Improving the Efficiency of Intelligent Manufacturing of Vehicle Parts

&lt;p&gt;With the development of intelligent manufacturing and the upgrading of industrial structure, all industries are facing the transformation and upgrading towards refined and efficient production models. This article takes engine piston and connecting rod as the research object. Firstly, the production process of piston and connecting rod is analyzed, and then the corresponding processing equipment is listed. Then, the layout design of the piston and connecting rod workshop is completed according to the layout standards of the intelligent workshop; Secondly, the transportation problem of engine piston connecting rods between storage units and various processing units during the production process was further analyzed. The scheduling strategy under multiple AGV modes was studied, and an optimization model was established with the lowest comprehensive cost of time and distance. Finally, an improved genetic algorithm was used to solve the model parameters. The simulation experiment results show that the proposed method can improve production efficiency by 7.5%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Balance of mixed flow assembly line based on industrial engineering mathematics and simulated annealing improved algorithm

The frequent changes in customer orders have led to the need for production line rebalancing and design. The increasing complexity of mixed assembly of multiple products makes it an urgent issue to perfect the balance of mixed flow assembly lines. This study proposes an improved simulated annealing algorithm by combining industrial engineering mathematical methods with intelligent optimization algorithms. The purpose is to achieve balanced optimization of mixed flow assembly lines by minimizing production rhythm, balancing load between workstations, and minimizing loss time within workstations. Firstly, industrial engineering methods are used to analyze the mixed flow assembly line, and then a mathematical optimization model is established by combining simulated annealing algorithm and genetic algorithm. Finally, through example simulation, the effectiveness of the algorithm is verified, and the superiority of its performance is verified through algorithm comparison. The experiment shows that the balance rate of the optimized assembly line for a total of 60 processes in 12 workstations has increased by about 6%. The smoothness index decreases by about 4. The loss coefficient has decreased by approximately 0.06. The overall efficiency has increased by about 12%. The smoothness index decreases by 0.3 in comparison with the solution results of Jackson's law in a case of 11 processes in 5 workstations. This fully demonstrates that the hybrid model has higher optimization efficiency, achieves balanced optimization of the mixed flow assembly line, improves production efficiency, and reduces equipment idle rate.

The-Application-of-Servo-Control-Technology-in-Robot-Positioning-and-Tracking-System-via-Heuristic-Algorithm

Based on the visual servo technology, this paper focuses on the visual tracking algorithm of moving objects and the dynamic grasping control method of robots, and realizes the automatic loading and unloading of moving workpieces to improve production efficiency. Firstly, aiming at the difficulties in the selection of high-dimensional features extracted by visual servo, this paper proposes a training method of generation countermeasure network based on heuristic algorithm by using the efficient search ability of heuristic algorithm. Secondly, we use image processing technology to realize real-time recognition and location of workpieces under complex background. According to the positioning results, an adaptive dual rate unscented Kalman filter visual tracking algorithm is proposed to solve the problem of delay and multi sampling rate in visual servo, and realize visual tracking of moving objects. The experimental results show that the proposed visual tracking algorithm has better stability and real-time performance.

Analisis Kinerja Mesin CNC Milling 3 Axis pada Pembuatan Pipa Jet Oil

The study evaluated the performance of a 3-axis CNC milling machine in the production of jet oil pipes with a focus on achieving a high level of precision. Through variation of cutting parameters and feeding speed, the machine succeeded in reaching the industry standard for jet oil pipe production. Optimization of parameters improved production efficiency, demonstrated by results that achieved the desired precision tolerance. Cutting tool wear analysis provides insight into machine performance and possible repair actions. This research confirms the potential of 3 axis CNC milling machines in addressing the challenge of high-precision jet oil pipe production, contributing to the development of CNC Milling technology in the context of the oil and gas industry.

Design for product upgradability considering remanufacturing outsourcing: A three-level joint optimization approach

Product design and business models play a pivotal role in the transition to a circular economy, emphasizing waste reduction and the preservation of valuable materials. Upgrading products by selectively replacing components that devalue over time proves to be an effective strategy for achieving competitive reutilization. This approach, known as product design for upgradability (DfU), not only enhances product value but also avoids whole product replacement, contributing to the realization of a sustainable society. The prevailing practice of DfU is often examined as a life-cycle characteristic, focusing on configurations of functional, physical, and architectural features to achieve competitive reutilization and extend service life. However, the upgradability of a product is strongly influenced by the practices of remanufacturing enterprises, which commonly adopt outsourcing strategies. Coordinating DfU decisions with remanufacturing and outsourcing presents a critical challenge for the effective operation of product upgrades.To address this challenge, this paper proposes a nonlinear mixed-integer decision model emphasizing a three-level joint optimization (TJO) based on a Stackelberg game. This model aims to determine the optimal solution that maximizes profit for the manufacturer, multiple remanufacturers, and outsourcers. To solve for the optimal decisions, a solution approach combining analytical methods with a nested genetic algorithm (NGA) is developed. The feasibility and effectiveness of the proposed TJO model are illustrated through a practical case study focusing on engine DfU considering remanufacturing outsourcing. The study identifies optimal DfU, remanufacturing, and outsourcing schemes simultaneously and conducts a sensitivity analysis of key parameters. The results demonstrate that our proposed DfU considering remanufacturing outsourcing strategy effectively improves production efficiency and the competitive advantage of enterprises. Moreover, the proposed TJO model successfully addresses coordination and conflicts between different stakeholders. Overall, this research provides valuable insights into the integration of DfU, remanufacturing, and outsourcing, offering a practical approach to optimizing decisions and improving the overall performance of enterprises in the context of remanufactured products.