Workshop Production Research Articles

Adaptive Reuse Suggestions in Interior Architecture Education: A Registered Workshop in Ankara

This study aims to determine both the recommendations for the conservation and reuse of historical buildings in interior architecture education and the learning outcomes of the restoration course. A registered building in Ankara, which formerly served as a production workshop for a vocational high school, has been identified as the study's main issue as it is no longer in use after having served its purpose. As part of the restoration course in this case, senior students in interior architecture department of Selcuk University were asked to submit a variety of function concepts for the registered building. The study focused on both qualitative and quantitative research methods, which included four stages: preparation, analysis, design, and evaluation. Through examples of applications from our nation and around the world, students were given the information they needed to understand the restoration during the first phase, preparation. The second stage, analysis, entails taking measurements of the building's interior and exterior as well as photographing and collecting written and visual records of it. The third step, the design phase, begins with the choice of a new function appropriate for the building's location and style in light of all available data and documentation. The students were asked to assess their level of knowledge regarding conservation and adaptive reuse as well as the learning outcomes of the restoration course before and after taking it in the final stage of the evaluation process, which took place after the fourteen-week restoration course. This study helped interior architecture students gain experience with a holistic design process through an adaptive reuse proposal project. In conclusion, increasing the number of courses on conservation and adaptive reuse in interior architecture education is important in training interior architects who are experts and competent in the field of reuse.

Great Moravian luxury jewellery (‘Veligrad jewellery’): The current state of research

The aim of the study is to evaluate the current state of research of luxury female jewellery in Moravia (Czech Republic) in the Middle Hillfort period (9th–10th century), which is also referred to by the name ‘Veligrad jewellery’. First, terminology issues and the state of the source base are summarised, after which the entire spectrum of ornaments (earrings, buttons, finger rings, etc.) and their typology is presented. A large section of the text is devoted to the results of archaeometric research and the contribution of experimental jewellery production. In terms of the provenance and method of creation of Veligrad jewellery, several aspects should be distinguished: where the raw materials for its production came from, where this production took place, whether the products of different workshops can be identified, to what extent the manufacturers were inspired by foreign models, etc. Subsequently, more recent research on chronology is summarised and the informative value of luxury jewellery for an understanding of Great Moravian society is discussed. In the final part, overlaps with research of luxury early medieval jewellery in the regions neighbouring Moravia are discussed.

НЕКОТОРЫЕ АСПЕКТЫ ПОДГОТОВКИ И ЗАЩИТЫ ДИССЕРТАЦИОННЫХ РАБОТ (ИСТОРИКО-ПРАВОВОЙ АНАЛИЗ)

The article analyzes the process of formation of university education, which began in Europe in the XII century in connection with the growth of cities, the development of workshop production and trade and industrial relations. It is noted that universities were created as free corporations, they paid great attention to the teaching of legal sciences. Simultaneously with the formation of universities, a system of defending dissertations for academic degrees is being formed. In Russia the process of formation of university education began later – in the XVIII century. Scientists who were awarded academic degrees, in accordance with the Table of Ranks, had the right to hold certain positions. The process of awarding academic degrees was under state control, which was reflected in decrees, government regulations and university charters. In Imperial Russia, dissertations on legal sciences represented deep scientific research, which has not lost its significance to this day.

Developing Talented Children’s Computational Thinking Through Multimodal Literacies in Pop-Up Storybooks: A Case Study in Hong Kong

Computational thinking (CT) currently has been mainly explored using programming robots and conducted in K12 education. In early childhood education, arts have a significant place in children’s learning, expression and cognitive development. Specifically, creating pop-up storybooks is a child-friendly activity. Our study aimed to explore the combination of CT and art, and to develop talented children’s CT abilities through a multimodal literacies educational approach, which referred to using different skills (i.e., writing, drawing, making, and storytelling) in the art activity. A total of 12 talented children were selected to participate in a pop-up storybook production workshop using a convenience sampling method. We adopted an observation method to capture talented children’s CT behaviors, generating a total of 180 min of activity videos and collecting fieldnotes and the children’s worksheets and artworks for the data analysis. Based on a content analysis, we found that talented children enhanced their CT development in multiple modes and practiced seven CT skills. In conclusion, our study emphasizes the importance of art in children’s education and provides new insights for subsequent research on children’s CT education.

Studies on the Iconography and Interpretation of Combat Scenes of the Late Bronze Age Aegean: A re-appraisal

Abstract The iconographic depictions of combat scenes in the art of the Late Bronze Age Aegean constitute the most fascinating motives of their era. These representations of fighting astonish us by their diversity. They have been immortalised on heterogeneous kinds of material, throughout the Late Bronze Age. The aim of this paper is to present and examine the issues of iconography of these performances. To do so, I use a typological method based on thematic, chronological, material and contextual criteria. Questions, such as whether these representations refer to mythical-fictional or historical-real events, will be addressed. In addition, questions arises as to what extent these combat scenes were a common topic of the Aegean Bronze Age art and during which chronological periods they occurred. Questions about the importance of such motifs in the society of that period, as well as whether they were products of either Minoan or Mycenaean workshops and if it is possible to recognize different artistic tendencies, are also examined. The analysis of the data shows that it is possible, at least for the early phase of the Late Bronze Age, to distinguish between artistic styles. It also shows that every combat scene of the Late Bronze Age Aegean could have a manifold significance for the society of that era, above all because the symbolic meaning of the struggle was integrated into the practical occurrence of war.

Examining capture velocity in local exhaust ventilation utilizing a purpose-built workstation

Abstract HVAC systems play a pivotal role in determining the overall energy consumption of industrial production halls and workshops, thereby emphasizing the necessity to optimize ventilation levels. Consequently, there is a paramount need to devise systems that strike a balance between energy efficiency and functional efficacy. This article elucidates a comprehensive investigation into the capture velocity of local exhaust ventilation (LEV) through the utilization of a purpose-built workstation within a controlled laboratory environment. The workstation, characterized by a 900 mm width and 450 mm depth worktop, was strategically outfitted with an exhaust duct proximal to the pollutant source, aligning with the operational objectives of the LEV system. The worktop was partitioned into square sections to facilitate precise documentation and subsequent reevaluation. Controlled fresh airflow was supplied through a duct positioned above the worktop. The study primarily centered on evaluating air velocity and turbulence intensity. Diverse configurations were experimented with at specified measurement nodes, and to avoid air extraction from behind the exhaust duct, a back sheet panel was installed. The results unequivocally demonstrated that the incorporation of a back sheet augmented capture velocities, underscoring its efficacy in enhancing LEV performance. These findings significantly contribute to advancing our comprehension of LEV efficiency and underscore the imperative of thoughtful design considerations and operational adjustments to ensure effective containment of contaminants within the operation environment.

Penerapan Modern Farm untuk Menunjang Higienitas dan Kemadirian Pangan di Pondok Pesantren Darul Mutaqin Kota Batu

One of the important contributions from the academic community of Brawijaya University is the empowerment of students in the boarding school setting. A range of empowerment programs have been adopted, encompassing the enhancement of traditional beverage production tools and workshop tools, as well as the introduction of handicraft innovations to augment income. The economy of the students and members of the Darul Mutaqin Islamic boarding school in Dau Village, Batu City, primarily relies on donations, supplemented by revenue generated from the sale of herbal goods. The school is strategically located next to rice fields, which offer ample natural resources. Food self-sufficiency can be achieved by utilizing animal husbandry techniques that involve feeding animals with nutritious green feed derived from agricultural waste, such as water hyacinth and maize stalks. This approach holds significant promise. Presently, the livestock industry at Darul Mutaqin Islamic Boarding School, namely in poultry farming, remains underdeveloped despite the ample availability of natural resources. Poultry farming, a generally manageable approach, might suffer a decrease in economic profitability if not adequately planned. The implementation of suitable technology via the Doktor Mengabdi initiative is anticipated to enhance students' proficiency in animal husbandry, augment livestock yield, and uphold environmental well-being. Darul Mutaqin's future goals include attaining financial self-sufficiency. This effort offers solutions by implementing a cutting-edge pilot project for a contemporary livestock system that integrates diverse mechanization and automation technology. The purpose of these suitable technologies is to generate sustainable poultry products, hence benefiting the pesantren community.

Optimizing Batch Time Allocation in Tobacco Shred Production Through Smart Scheduling

This paper introduces a smart scheduling system aimed at optimizing batch time allocation in cigarette tobacco shred production. Traditional scheduling methods, which rely on fixed schedules based on historical data, often fail to adapt to dynamic production environments, leading to inefficiencies. To address these limitations, the system continuously monitors real-time data from the production line, including machine performance, material flow, and environmental conditions. Utilizing machine learning algorithms and predictive analytics, the system dynamically adjusts production schedules to anticipate needs, identify potential bottlenecks, and optimize resource allocation. The system was validated in a real production workshop, demonstrating significant improvements in flexibility, efficiency, and overall production quality. These advancements underscore the transformative potential of Industry 4.0 technologies in modern manufacturing.

A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

Accurate prediction of multiple orders remaining completion time (MORCT) is crucial in the make-to-order production model. It enables managers to keep track of production status, make timely decisions, and ensure on-time delivery of orders. However, dynamic production environments, characterized by constantly changing order quantities and relationships, as well as the special temporal features of the production process, pose challenges to existing prediction methods. To address these issues, this paper proposes a novel framework based on multi-target regression. First, production data are collected and standardized from various sources using multiple data transfer protocols. The input dataset is then constructed and dynamically adjusted to accommodate changes in order quantities and priorities. Finally, a prediction model named DMTR-LSA is developed to effectively handle the specific temporal relationships in the production data by integrating long short-term memory (LSTM) and self-attention mechanisms. A case study in a real production workshop demonstrates that the proposed method supports simultaneous prediction of multiple orders. It outperforms existing methods on several evaluation metrics, reducing the average prediction error by more than 8.9%. These results highlight the practical value of the proposed method for predicting MORCT in dynamic production environments and its potential impact to enhance the production decision-making process.

Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.

Optimization of carbon emission in an integrated machine-piece scheduling and vehicle routing problem and its solution using MOPSO and NSGAII metaheuristic algorithms

The growth of the global economy is accompanied by significant energy consumption, and greenhouse gas emissions create various problems such as global warming and environmental degradation. To protect the environment, governments are seeking to reduce carbon emissions. Production systems that operate solely based on economic factors in the workshop only consider problems such as production speed, cost, and processing time. Two aspects can be effective in saving energy and reducing emissions at the production planning level: using routing to find the shortest path for collecting workpieces to the workshop, and turning off machines with long idle times and restarting them at the appropriate time. If the workshop production problem is combined with vehicle routing, a new problem arises. According to the research conducted so far, an integrated mathematical model for production routing has not been designed in a situation where the routing is before the production workshop. In this research, this bi-objective model is introduced, and it is solved using the augmented epsilon-constraint (AEC) method. The proposed mixed-integer linear programming model of this research includes three dimensions: environmental, social (customer satisfaction), and economic simultaneously. Given the high complexity of the mathematical model, MATLAB software and MOPSO and NSGA-II algorithms were used to solve it at higher dimensions. Seven evaluation criteria were used to compare the two proposed algorithms, and the results show that the MOPSO algorithm performs better. The findings suggest that minimizing pollution may involve sacrificing on-time delivery to customers. Consequently, decision-makers must carefully weigh the trade-off between reducing environmental impact and maintaining satisfactory delivery performance, ultimately deciding on an acceptable pollution level.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production.

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.

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.

Existence of Synthetic Dyes in Foodstuffs: A Retrospective Study of Food Safety Concerns

Background: The use of additives, especially food colors, has attracted the attention of food industries. The purpose of this study is to retrospectively investigate the use of food colors in food products offered in Urmia, Iran. Methods: In this study, 451 samples of different types of food were collected from Urmia (April 2019-February 2022). To identify the color type, the Thin Layer Chromatography method was used. The samples included a confections (dry sweets), saffron noghl, saffron halva (a type of confectionery originating from Persia and widely spread throughout the Middle East), saffron-flavored rice and chicken, fruit ice cream, traditional saffron-flavored ice cream, fruit drinks, and saffron solutions. These products were produced from establishments engaged in the production and distribution of food and confections, restaurants, halva manufacturing workshops, traditional ice cream production workshops, and coffeehouses. Statistical analysis of data was done using SPSS software (IBM SPSS statistic 16, USA). Results: Within the group of synthetic dyes analyzed, quinoline yellow, sunset yellow, carmoisine, and tartarazine were identified as the most commonly used synthetic colorants in the examined food products. Findings also showed that rice and saffron chicken samples had the highest average frequency of tartarazine used among the studied food groups (41.66±1.88%). The highest mean frequency of yellow quinoline consumption was also observed in samples of saffron halva (57.27±3.86%), various types of dry sweets (55.50±0.7%), and saffron noghl (52.50±3.53%). Sunset yellow had a low frequency in all groups except saffron ice cream and fruit drinks (p=0.086). The findings showed that the highest average frequency of carmoisine synthetic color consumption was related to saffron ice cream (25±1.41%) and fruit drinks (23.80±2.54%) among the studied food groups. Conclusion: The findings indicated that approximately 30% of the tested samples, except fruit drinks, contained synthetic colorants.

An energy-saving distributed flexible job shop scheduling with machine breakdowns

Due to the change of consumption pattern, distributed flexible production is now becoming a major manufacturing mode. The machine breakdown seriously affects the production in workshops. How to efficiently adjust the scheduling scheme after machine breakdowns in distributed flexible job shop is an important concern for enterprises. Meanwhile, energy consumption is attracting more and more attention as environmental issues become increasingly serious. Thus, the energy-saving scheduling on distributed flexible job shop considering machine breakdowns is studied, and a mixed-integer programming model is established to optimize makespan, total energy consumption and machine load difference, simultaneously. Based on the problem characteristics, an improved memetic algorithm is designed. To improve the quality of initial solutions, an initialization strategy incorporating multiple rules is designed. An active decoding method with an adjustment strategy is adopted to fully utilize machine idleness. To expand the search range, the idea of dual-population collaborative optimization is used. Multi-level intelligent mutation operation strategies are introduced to further enhance the quality of solutions. In addition, an adaptive parameter rule is designed to adjust to appropriate search capabilities at different evolutionary stages. To verify the effectiveness of the proposed strategies and algorithm, the memetic algorithm, the artificial bee colony algorithm, the differential evolution algorithm and the estimation of distribution algorithm are chosen as comparison algorithms. The same iteration number and same running time are used as the termination criteria of all algorithms, and 60 instances of different scales are solved. The experimental results demonstrate that the effectiveness of the strategies and algorithm designed in this paper. Notably, our algorithm consistently achieved good results even when the same running time is used as the termination criterion.

Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics

Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient a r in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.

A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

Revealing the flavor compositions, microbial diversity, and biological functions of Huangshui from different production workshops

Huangshui (HS) is the main by-product of Baijiu production, which possesses abundant organic matter and microbial resources, rendering it valuable for high-value utilization. This study collected HS samples from three production workshops with distinct environments, aiming to assess the impact of the brewing microenvironment on the quality of HS. The results revealed elevated lactic acid content in HS samples, ranging from 7878.09 to 10531.56 mg/L. Esters, alcohols, and acids were the main flavor compounds, with 32 key volatile compounds identified. Lactobacillus emerged as the predominant bacterial genus in HS, while Cladosporium and Aspergillus dominated the fungi. Redundancy analysis (RDA) indicated that the physicochemical parameters of HS were pivotal factors influencing the microbial community. Spearman correlation analysis exhibited significant positive correlations of Cladosporium and Aspergillus with 13 and 3 flavor compounds, respectively, suggesting they may represent the core functional microbial genus responsible for the characteristic flavor of HS. Moreover, HS demonstrated antioxidant and antibacterial activities, further augmenting post-enzymatic hydrolysis. These findings lay the groundwork for screening functional strains and utilizing flavor compounds in HS, laying a theoretical foundation for the application of HS in the food industry. However, efficient separation strategies for functional microorganisms and bioactive compounds necessitate further exploration.

Research on Scheduling Algorithm of Knitting Production Workshop Based on Deep Reinforcement Learning

Research on Scheduling Algorithm of Knitting Production Workshop Based on Deep Reinforcement Learning