Manufacturing Production Research Articles

Heat causes large earnings losses for informal-sector workers in India

Abstract Heat reduces labor productivity and output in formal manufacturing but little is known about its impacts on the earnings and welfare of workers in the informal sector that comprise 82\% of the labor force in low-income and lower-middle-income countries. This study reports the results from daily surveys of nearly 400 workers in two slums in Delhi for a month in the summer of 2019. Every degree Celsius increase in wet bulb temperature was associated with a fall in gross earnings of 13(± 3.5) percentage points, a fall in earnings net of work-related expenditure of 19(± 4.5) percentage points, an increase in the self-reported probability of sickness of the worker or a family member of 6(± 0.5) percentage points, and a decrease in the probability that a worker went to work of 2(± 0.5) percentage points. Net earnings were 40\% lower during the two heatwaves that occurred during the study period.Over 320 million informal-sector workers in low-income and lower-middle-income countries are currently exposed to temperatures similar to those observed in this study.

Research on Topology Optimization of Building Digital Supply Chain Based on Genetic Neural Network

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

National brands versus store brands: retailer fight against national brands to improve store brands via a dynamic discount pricing strategy

Today, there is a lot of competition between competitors in the market of different products. The level of complexity of relationships and behavior of active players in the market has also increased. So that a manufacturer and retailer work together to increase the profit of the supply chain through the sale of products. On the other hand, the retailer and the manufacturer as competitors are competing with each other to sell more of their products. This issue has increased the complexity of the relationship between retailers and manufacturers. This study aims to explore how retailers can strengthen the self-introduced store brand (SB) and resist the pressures of manufacturers by using dynamic discount pricing (DDP) strategy. Numerical examples are proposed to clearly explain the validity and applicability of the model. Based on the managerial insights developed by analyzing the model's results, we evaluated the influences of DDP strategy on the retail prices and demands of national brands (NB) and SB and the profit of the retailer and the manufacturer. We found that discounts on the manufacturer's product by the retailer increase the demand for both NB and SBs. An increase in demand leads to an increase in the profits of manufacturers and retailers. However, the increase in the manufacturer's profit does not compensate for the decrease in his market share. In other words, the introduction of an SB always causes damage to the manufacturer, but the amount of damage depends on various factors. These findings offer manufacturers and retailers useful insights and help them adopt effective marketing strategies.

A Review of Wear in Additive Manufacturing: Wear Mechanism, Materials, and Process

In fields such as industrial engineering and healthcare, additive manufacturing technology is a focal point for researchers. Wear represents a significant challenge for additive manufacturing technology, increasingly emerging as a research hotspot in recent years. This review categorizes and summarizes wear issues in additive manufacturing technology, providing a comprehensive overview of wear mechanisms, materials, and the effects of additive manufacturing processes on wear. Research indicates that different wear mechanisms result in varying wear characteristics. The inherent properties of the materials significantly influence wear during the manufacturing process. Modifying material compositions and optimizing microstructures can enhance the wear properties of additive manufacturing products. Additionally, the study of additive manufacturing technology in repair and maintenance is a current and anticipated research hotspot for the coming decades. In the research of additive manufacturing processes, the effective regulation of process parameters and their post-processing play a positive role in enhancing the wear characteristics of products produced via additive manufacturing. Lastly, the challenges and recent advancements concerning wear issues in the field of additive manufacturing technology research are summarized.

Heat treatment of FDM and SLS delicate additive manufacturing products: mechanical properties enhancement and dimensional accuracy

Heat treatment of FDM and SLS delicate additive manufacturing products: mechanical properties enhancement and dimensional accuracy

Investigations on laser-assisted turning of IN625 alloy with hot hardness approach using uncoated and CrAlSiN coated WC tools

This paper presents an investigation into the effect of laser-assisted turning of the IN625 superalloy using uncoated and CrAlSiN nanocomposite-coated tungsten carbide tools. A hot hardness test was conducted for IN625 material, which showed there was a pronounced softening of the material above 850 °C. This exercise guided the choice of laser power for the subsequent laser-assisted turning experiments. The cutting forces (radial/thrust, Fx; axial/feed, Fy; and tangential/cutting, Fz), maximum flank wear (VBBmax), and surface roughness ( Ra) were measured and analyzed for the planned experiments. The results demonstrated that at 2500 W laser power, a 9%, 70%, and 59% reduction of cutting forces for uncoated tools, and a 31%, 77%, and 69% reduction for CrAlSiN coated tools were observed in the Fx, Fy, and Fz directions respectively. At 2250 W laser power, the uncoated tools exhibited a 33% (433–289 µm) reduction in VBBmax and a 28% (1.8–1.3 µm) reduction in Ra. The CrAlSiN-coated tools, at 2500 W laser power, showed even more significant improvements, with reductions of 46% (365–232 µm) in VBBmax and 56% (1.4–0.8 µm) in Ra. The results underline the improved performance of laser-assisted turning for cutting-force and tool-wear reduction and improved surface finish with CrAlSiN-coated tools. This paper presents the potential of laser-assisted machining as a viable method for machining difficult-to-machine materials like IN625, which offers enormous manufacturing productivity and tool life benefits.

Assessment of reliability and availability of a system by using a bivariate stochastic model

AbstractIn the present age of industrial revolution where the smart manufacturing and smart production are being used in the developed countries, the production systems are still vulnerable to disruptions and outages that affect the reliability and availability of the systems. Certain scientific and probabilistic approaches are required to study the inter‐failure times and repair times and find the possible solutions for maximum production and minimum losses. In this article, we propose a bivariate Power Pareto distribution to model the inter‐failure times and repair times of a system along with some of its characteristics. The model parameters are estimated by employing the maximum likelihood estimation, Bayesian estimation and ant colony optimization algorithm. A simulation study is conducted for different sample sizes to assess the stability of the model parameters. Moreover, we derive the distribution of convolution from our proposed bivariate stochastic distribution and compute its quantiles that help to determine the total time of availability and recovery of a certain system. To demonstrate the efficacy of our model, we use real data set of inter‐failure times and repair times of a certain system.

Sustainable manufacturing production synergy and circular economy model under low carbon energy based on thermal energy cycle and blockchain technology

With the intensification of global climate change and resource depletion, the use of low-carbon energy and the exploration of circular economy model become more and more important. As an efficient way to use energy, thermal energy cycle has wide application potential in manufacturing industry. This study aims to build a sustainable manufacturing production synergy and circular economy model under low-carbon energy based on thermal energy recycling and blockchain technology, in order to improve the resource utilization efficiency of manufacturing industry, reduce carbon emissions, and achieve double benefits of economy and environment. Through literature analysis and case studies, key elements of thermal energy recycling and blockchain technology are integrated to design and simulate a new collaborative manufacturing system. The system uses thermal energy recycling technology to convert waste heat recovery into reusable energy, and combines blockchain technology to achieve real-time monitoring of energy flows and production processes to improve transparency and efficiency. The model simulation results show that after the introduction of heat recovery and blockchain management, the energy utilization efficiency in the manufacturing process is greatly improved, and the overall carbon emissions are significantly reduced. The transparent data sharing mechanism helps all participants optimize production decisions and improve the flexibility and response speed of collaborative production.

Enhancing Public Health: Research on Occupational Health Measures and Employee Productivity in Manufacturing

Occupational health measures are rules and regulations designed to protect workers' physical and mental health while they are at work. These precautions consist of health exams, safety procedures, environmental modification, and stress reduction plans. This research’s goal is to investigate the impact of occupational health measure (OHM), work environment, and employee productivity, to investigate the significant variable in manufacturing. A total of 54 respondents were provided. Questions were used to get the data. SPSS software is used. ANOVA, chi-square test, regression analysis test, Pearson correlation coefficient (PCC) test, and Cronbach’s alpha (α) test are employed in the analysis of the outcomes. The findings indicate that OHM, employee productivity, and work environment in enhancing public health are simultaneously significant to manufacturing. OHM and employee productivity are the significant factor that affects manufacturing. By mitigating health hazards and advancing employee welfare, businesses establish more secure work environments that promote increased productivity and effectiveness. In the end, successful public health programs support both overall corporate performance and employee happiness.

AR/VR assisted integrated framework of autonomous disassembly system for industrial products

An immersive Augmented Reality (AR) technology enhances productivity and flexibility in manufacturing by providing real-time instructions within workers’ field of view, aiding in complex tasks. According to cognitive load and attention-based theories, AR guidance can reduce cognitive strain, enhancing task performance. The affordance of AR in industrial processes like assembly or disassembly for repair/maintenance has been exploited broadly. However, the prevailing studies on automated user interfaces for AR instruction development, specifically virtual scene creation and target generation for object mapping rendering, still need to be explored. Notably, production industries currently rely on outsourcing skilled professionals to create AR overlaying instructions using manufacturer input. However, the exactness of the disassembly repository is substantial to avoid defects in AR guidance particulars. This underscores the significance of generalized automated AR instruction development and emphasizes the demands for further research. In this work, the autonomous AR-integrated framework is developed for the disassembly context, empowering manufacturers to acquire desired outcomes across various modules. These modules include the validation of the manual user disassembly sequence repository during the prefatory phase of AR instruction creation and the provision of Disassembly Sequence Planning (DSP) input. Additionally, the framework supports the simulation/rendering of disassembly efforts in a virtual platform and the generation of rendering virtual display scenes for appropriate AR-guided object mapping. Furthermore, it enables the generation of essential model targets for registering the AR scenes. Subsequently, the extracted repositories (virtual scenes and model targets) are input into the Unity AR tool to create AR mapping guidance for disassembly operations. The performance of the autonomous disassembly module is evaluated through three case studies with different AR visualizations. Furthermore, the potential of the proposed framework is exploited by extracting feedback from experts regarding flexibility, compatibility, completion time, and cognitive level.

Digital twin-enabled quality control through deep learning in industry 4.0: a framework for enhancing manufacturing performance

ABSTRACT In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0.

Dynamic Analysis of the Multichannel Supply Chain Under Carbon Subsidy Policy

For achieving the goals of carbon peaking and carbon neutrality, the government will further strengthen subsidies to enterprises for carbon emission reduction. This paper investigates the long-term repeated game behavior of manufacturer and retailer in multichannel supply chains under three different forms of government subsidies. The long-term repeated game behavior of manufacturer and retailer is studied under the manufacturer green innovation subsidy model (IS model), manufacturer production subsidy model (MS model) and retailer sales subsidy model (RS model). Under three situations, excessive price adjustment can easily lead to system instability and sharp fluctuations in profits. Two methods are used for chaos control and management, and the parameter adjustment control method is found having more advantages for system re-stabilization.

Microscale Humidity Sensor Based on Iron-Coated Elaters of Equisetum Spores.

Humidity sensors deeply influence human manufacturing production and daily life, while researchers generally focus on developing humidity sensors with higher stability, higher linearity, rapid response time, etc. Yet, few people discuss measuring humidity in the microenvironment by miniaturizing sensor size into a microscale, in which the existing humidity sensors are difficult to reach. Accordingly, this study proposes a methodology for measuring relative humidity in the microscale by utilizing the distinctive morphologies of Equisetum spores across a range of relative humidities between 50% and 90%. Equisetum spores are responsive to changes in ambient relative humidity and remain in their original activities even after iron sputtering, which aims to endow the sensor with magnetic properties. The test performed in this study demonstrated a response time of 3.3 s and a recovery time of 3.6 s. In the first application, we employed such microscale sensors to work in the channel of the microfluidic chip or the cell migration microchip, as an example of working in the microenvironment. COMSOL Multiphysics 6.2 software was also used to simulate the change in relative humidity in such microchannels. Secondly, such microscale sensors are combined with smartphone-based microscopy to measure the humidity of the skin. These microscale sensors pave the new way to sensing humidity in microenvironments.

Food Manufacturing Workers and Structural Causality among Work Environment, Work–Family Conflict, Musculoskeletal Pain, Sleep-Related Problems, and Burnout

This study analyzes the association and structural causality among work environment, work–family conflict, musculoskeletal pain, sleep-related problems, and burnout in the food manufacturing industry. This study used the 6th Korean Working Environment Survey data, and 523 food production workers were selected as research subjects. Structural equation modeling showed that work environment and work–family conflict significantly affected musculoskeletal pain. In addition, work–family conflict and musculoskeletal pain affected sleep-related problems, and musculoskeletal pain and sleep-related problems impacted burnout. Furthermore, this research provides practical strategies to deal with musculoskeletal pain, sleep-related issues, and burnout. Burnout is more affected by sleep-related problems than by musculoskeletal pain. Additionally, sleep-related problems seem to be more affected by musculoskeletal pain than by work–family conflict. Meanwhile, musculoskeletal pain is influenced by the work environment rather than by work–family conflict. This result can be used to establish preventive policies for the safety and health of food manufacturing production workers.

Demand study of freight transportation via railway of four commodity groups: A case study on Costa Rica's Pacific Coast Route

Having railway mobility as an option among transportation modes is of prime importance for the freight logistics of a country and the overall performance of its commerce sector. This work presents a case study in Costa Rica, where freight transportation via railway was formerly available during the last century but has not been in full service since 1995. Specifically, this paper assesses the link connecting the capital city of San Jose to the port of Caldera on the Pacific Ocean coast, henceforth referred to as the “Pacific Coast Route”. Although government institutions and industries alike have stressed the importance of its reactivation in recent years, few efforts have been made to specifically study this route with a level of detail that enables them to understand the composition of freight markets.This study presents a novel demand estimation differentiated by four types of commodities developed to calculate the potential amount of goods that could flow through this corridor, measured in monetary terms and as volumes. Trends in the expected share were analyzed to identify variables that may improve the attractiveness of the railway service and the participation of clients along the route from the country's main economic activities which includes the following: agriculture & livestock, manufacturing, minerals, and forestry.The four-step transportation model was chosen for its suitability towards this case study; where available data sets were limited, the information was aggregated into yearly values, and the network was defined with few transportation zones. Activity-based models were not feasible in this case due to the lack of information on complete routes and volume flows. The model evaluated a first scenario given the present design of the route and a second scenario involving improvements in the alignment and the addition of a new segment.Regarding the opportunities of moving goods according to their type, in terms of volume, minerals (48 %) and agricultural (31 %) have the largest shares of participation in this region, followed by manufacturing (20 %) and forestry products (1 %). However, the manufacturing sector stands out in financial terms, accounting for 71 % of the total value of goods mobilized in the study area.It was found that the demand is most sensible for changes in the service fare, while the benefits of shorter travel times and a larger attraction area in scenario 2 were not significant. A service fee of $USD 2.35/ton in average is recommended to reach a 30 % modal shift towards the railway (following the European Commission's goals to reduce CO2 emissions from freight transportation in 2030).Finally, recommendations for public policies are provided to the entities in the country in the sectors of Agriculture, Health, Energy and Environment, Transportation, and Public Services. The suggestions aim to develop the new railway project with a holistic perspective, considering the inclusion of less profitable economic activities of the surrounding communities that require connectivity, compliance with environmental commitments, and design for resilience while aiming for a financially feasible business model.

Enhancing organic milk sales through cumulative power of product – extrinsic cues: empirical evidence from China

PurposeTo examine the impact of manufacturer reputation, retailer reputation, and product price on consumers’ perceived quality and purchasing behavior regarding organic milk.Design/methodology/approachEmploying a 2 × 2 experiment, data were collected from 1,259 consumers in 32 provinces in China.FindingsWhen a low-reputation manufacturer sells products through a high-reputation retailer, it improves consumers’ perception of quality and positively influences their purchasing behavior. Interestingly, setting higher prices for products manufactured by low-reputation companies and selling them through high-reputation retailers did not significantly enhance consumers’ perceived quality and deter their purchasing behavior.Originality/valueThe analysis expands the framework for cue diagnosis. While the existing framework primarily focuses on the influence of cue-type combinations on perceived quality, it does not integrate purchasing behavior into the conceptual framework. This limitation hinders people understanding of the theoretical mechanisms underlying the use of cues in purchasing decisions. This paper address this by gradually introducing variables, such as retailer reputation and product price, into the baseline model, thereby extending this theory. In addition, this paper advances the marketing research literature within the business-to-business-to-consumer context by examining the additive effects of manufacturer reputation, retailer reputation, and product price on consumers’ perception of quality and purchasing behavior.

Optimising distributed heterogeneous flowshop group scheduling arising from PCB mounting: integrating construction and improvement heuristics

ABSTRACT In real-world manufacturing systems for processing printed circuit boards (PCBs), the workshops integrating various flowline-based cells are common in large-scale enterprises. The scheduling problem within the systems is modelled as the distributed heterogeneous flowshop group scheduling problem (DHFGSP) in this study. Departing from practical requirements and considering the grouping characteristics among PCB components, we account for carryover sequence-dependent setup time (CSDST). Moreover, recognising the critical importance of just-in-time production in semiconductor manufacturing, the total tardiness, a previously unexplored objective within the DHFGSP context, is addressed. To tackle this problem, a mixed-integer linear programming (MILP) model, capable of obtaining optimal solutions for small-scale instances, is proposed. Due to the NP-hard nature of the problem, obtaining high-quality solutions in a reasonable time using the MILP model becomes challenging for large-scale instances. Therefore, a solution algorithm, comprising construction and improvement heuristics, is developed. Capitalising on the problem’s characteristics, the construction heuristic efficiently generates high-quality feasible solutions in a very short time. Built primarily around artificial bee colony (ABC) optimisation, the improvement heuristic can significantly further enhance solutions by integrating the collaborative and restart operators. Comprehensive experiments on instances of varying scales demonstrate the effectiveness of the proposed algorithm for the problem under investigation.

Substrate Moisture and Temperature Effects on Limestone Reaction Rate in a Peat-Based Substrate

ABSTRACT Lime reaction rate in a container substrate is influenced by temperature and moisture, which are factors that vary between batches of substrate during manufacturing, storage, and crop production. The effects of temperature and moisture on the duration required to achieve a stable substrate-pH are useful information for substrate companies and growers, as well as a key component when modeling lime reaction over time. The pH of a 70 peat : 30 perlite (by volume) substrate was quantified over time under a range of different storage temperature (1.9 to 33.3°C) and substrate volumetric water content (VWC, 0.168 to 0.568 L of H2O/L of substrate). The lime source was a horticultural dolomitic carbonate limestone screened to the fraction that passed through a 100 US mesh but was retained on a 200 US mesh (0.075–0.15 mm) incorporated at 2.67 g·L−1 of substrate. Experiments provided two data sets for calibration and validation. Lime reaction rate increased with increasing substrate temperature and substrate moisture level. The duration required to reach a target substrate-pH value of 6.0 was used to indicate 90% of maximum pH effect from lime. Duration varied from 4 days with the combined high temperature (20.6 and 33.3 °C) and high VWC (0.468 and 0.568 L H2O/L of substrate) to 53 days with low temperature (1.9 °C) and low VWC (0.168 L H2O/L of substrate) for the calibration data set. At an example low VWC of 0.168 L of H2O/L of substrate, the duration required to reach substrate-pH 6.0 at 1.9, 7.8, 10, 20.6, and 33.3°C was 53, 38, 34, 23, and 17 days, respectively. Similarly, if the temperature was held constant at 33.3°C, reducing VWC from 0.568 L H2O/L to 0.128 L H2O/L would decrease lime reaction rate from 100% to 21%, requiring five times the duration to reach an equilibrium pH. Results can be used to compare the relative effects of moisture and temperature on lime reaction rate for substrate manufacturers and growers.

Scale effects on core design, fuel costs, and spent fuel volume of pressurized water reactors

The desire to improve the economic competitiveness and deployment pace of nuclear energy through modularization, manufacturing, and series production had led to the development of smaller size reactors. As the standard 17x17 fuel technology is mainly maintained in the pressurized water reactors (PWRs) category, this translates into a lower number of fuel assemblies in the core and sometimes a reduced fuel height. To assess the impact of such scale change in core design on fuel cycle cost and spent fuel volume, a scoping analysis tool is developed based on infinite lattice calculations, leakage, fuel management reduced models, and levelized unit cost of electricity (LCOE) estimate. As such, cost dynamics driven by fuel specific power, burnup, core leakage, feed, cycle length, fuel assembly height as well as uranium market data are captured with consistent set of assumptions and analysis methods. A selection of 5 reactor designs representative of leading PWR developers is assessed and compared. Pursuing higher specific powers and optimal burnups are highlighted as the main fuel cost reduction drivers, nevertheless, practical limitations and opportunities must be evaluated to establish the feasibility of such enhanced fuel operation. In consequence, a detailed core design is performed using SIMULATE3 code for 5 PWR variations including natural and forced coolant circulation modes, two reactor scales, power densities of 73, 112, and 123 kW/l and higher discharge burnups. Design and optimization are performed at the lattice level, for the reflector, and at the core loading level. Satisfactory steady-state operation including power distribution, coolant operating limits, and reactivity requirements are analyzed and reported in this paper. The fuel economics of the detailed core designs confirm the scoping analysis findings. Despite the unlocked power uprates in small PWRs, the achievable burnup for a given fuel specific power requires more enrichment and shorter fuel height results in higher fabrication costs per mass of fuel, which makes scaling down core size a more expensive endeavor on the fuel cycle front. Spent fuel volumes are reported for the PWRs designed in this paper. These volumes are driven by the core average discharge burnup regardless of the scale in consideration. Additional cost and core performance aspects related to heavy reflector gains, fuel-reflector substitution, and disposal cost policy in the U.S. are examined.

Accelerated Development of Pharmaceuticals Past, Present, and Future.

This paper reviews the accelerated development of pharmaceuticals, exploring past, present, and future perspectives. It provides a historical overview of early strategies used to expedite development, beginning with initiatives from the 1990s. The work of Gardner and Byrn in accelerated development analysis during this era is highlighted. The narrative progresses to the 2000s, discussing the emergence of PK/PD in accelerating pharmaceutical development. The paper further examines case studies in the accelerated development field, including the INDIGO and Chorus programs. It concludes with an analysis of the current state of the field, referencing the NIPTE conference, which focused on the industrial perspective of accelerated development. Additionally, the paper outlines strategies for the rapid development of Solid Lipid Nanoparticle manufacturing and vaccine production.