Batch Production Research Articles

Research on Workshop Layout Optimization Based on SLP Method

With the rapid development of the market economy and the increasing personalized needs of customers, the production mode of the production workshop is gradually shifting towards small batch and multi batch production mode, which has a significant impact on the production efficiency, logistics costs, and other aspects of the production workshop, posing huge challenges to the layout of production workshop facilities. A reasonable workshop layout can not only reduce the distance and cost of material handling, but also save product production time and shorten the production cycle. This article takes DY Company's machining workshop as the research object, and through investigation, it is found that there are problems such as logistics distribution routes crossing and overlapping in the workshop. In response to the above issues, data was collected from the workshop and analyzed using the SLP method, ultimately obtaining the optimal solution for optimizing the layout of the machining workshop, achieving a dual improvement in the production capacity and efficiency of the machining workshop.

Searching by Topological Complexity: Lightweight Neural Architecture Search for Coal and Gangue Classification

Searching by Topological Complexity: Lightweight Neural Architecture Search for Coal and Gangue Classification

Calculation of product service systems in single and small batch production

AbstractSingle and small batch production is characterized by complex value-added processes and products. The transparent calculation of new offers and change requests is therefore a particular challenge. At the same time, the rising spread of product service systems (PSS) increases the complexity of costing, as additional intangible services have to be calculated precisely. In addition to the challenges posed by such precise calculation of intangible services, companies have to master another complexity driver in the form of PSS. Innovative information and communication technologies (ICT) offer new potential for effective and efficient design of the costing process for the entire life cycle. The rising availability of data along the entire product life cycle significantly increases transparency and, thanks to intelligent analysis algorithms, allows the identification of clear cause-and-effect relationships and forecasting options. The aim of the presented paper is thus to develop a model for calculation of PPS in single and small batch production that exploits the new potential of ICT.

Efficient conversion of cane molasses into Tremella fuciformis polysaccharides with enhanced bioactivity through repeated batch culture

Tremella fuciformis polysaccharide (TFPS) is a natural mushroom mucopolysaccharide widely used in health foods, medical care, cosmetic and surgical materials. In this study, we developed an efficient strategy for the repeated batch production of highly bioactive TFPS from the agro-industrial residue cane molasses. Cane molasses contained 39.92 % sucrose (w/w), 6.36 % fructose and 3.53 % glucose, all of which could be utilized by T. fuciformis spores, whereas, the TFPS production efficiency only reached 0.74 g/L/d. Corn cobs proved to be the best immobilized carrier that could tightly absorb spores and significantly shorten the fermentation lag period. The average yield of TFPS in eight repeated batch culture was 5.52 g/L with a production efficiency of 2.04 g/L/d. The average fermentation cycle after optimization was reduced by 61.61 % compared with the initial conditions. Compared to glucose as a carbon source, cane molasses significantly increased the proportion of low-molecular-weight TFPS (TFPS-2) in total polysaccharides from 3.54 % to 17.25 % (w/w). Moreover, TFPS-2 exhibited potent antioxidant capacity against four free radicals (O2−, ABTS+, OH, and DPPH). In conclusion, this study lays the foundation for the efficient conversion of cane molasses and production of TFPS with high bioactivity.

Study of SVOM/ECLAIRs inhomogeneities in the detection plane below 8 keV and their mitigation for the trigger performances

Context. The Space-based multi-band astronomical Variable Objects Monitor is a Chinese-French mission dedicated to the study of the transient sky. It is scheduled to start operations in 2024. ECLAIRs is a coded-mask telescope with a large field of view. It is designed to detect and localize gamma-ray bursts in the energy range from 4 keV up to 120 keV. In 2021, the ECLAIRs telescope underwent various calibration campaigns in vacuum test-chambers to evaluate its performance. Between 4 and 8 keV, the counting response of the detection plane shows inhomogeneities between pixels from different production batches. The efficiency inhomogeneity is caused by low-efficiency pixels (LEPs) from one of the two batches, together with high-threshold pixels (HTPs) whose threshold was raised to avoid cross-talk effects. In addition, some unexpected noise was found in the detection plane regions close to the heat pipes. Aims. We study the impact of these inhomogeneities and of the heat-pipe noise at low energies on the ECLAIRs onboard triggers. We propose different strategies in order to mitigate these impacts and to improve the onboard trigger performance. Methods. We analyzed the data from the calibration campaigns and performed simulations with the ground model of the ECLAIRs trigger software in order to design and evaluate the different strategies. Most of the impact of HTPs can be corrected for by excluding HTPs from the trigger processing. To correct for the impact of LEPs, an efficiency correction in the shadowgram seems to be a good solution. An effective solution for the heat-pipe noise is selecting the noisy pixels and ignoring their data in the 4–8 keV band during the data analysis. Results. The trigger threshold is the minimum value of the signal-to-noise ratio that is required to claim that ECLAIRs has detected a candidate event that is not related to a background fluctuation. After introducing the efficiency inhomogeneity in the imaging simulation, the trigger threshold in the 4–8 keV band increased by a factor of 5.75 times and 1.43 times due to the impact of HTPs and LEPs, respectively, in the worst case (on a timescale of about 20 min). The trigger threshold value was restored to its normal value after we applied an efficiency-correction method. Introducing the heat-pipe noise in our simulations in the worst case (timescale of about 20 min) resulted in an increase in the trigger threshold of approximately 100% in the 4–8 keV band compared to observations without heat-pipe noise. Moreover, even with this increased threshold, we estimated a false-trigger rate of 99.26% in the 4–8 keV band and 4.44% in the 4-120 keV band. By accepting a loss of 2.5-5% noisy pixels in the 4–8 keV energy band, we can prevent false triggers caused by heat-pipe noise and reduce the threshold increment to about 20% for the longest timescale (about 20 min) of the ECLAIRs trigger in the 4–8 keV range.

Tool Wear Monitoring System Using Seq2Seq

The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and GRU models to compare performance by using R2, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an R2 of approximately 0.03~0.037 in step drill data, 0.540.57 in top metal data, and 0.16~0.45 in low metal data. Confirming that Seq2Seq exhibited the best performance, we established a real-time monitoring system to verify the prediction results obtained using the Seq2Seq model. It is anticipated that this monitoring system will help prevent accidents in advance.

Kinetic and artificial neural network modeling of dried black beauty eggplant (Solanum melongenaL.) slices during rehydration

AbstractThis paper targeted to investigate rehydration characteristics of dehydrated black beauty eggplant (which was dried under sun, by hot air, microwave, and infrared) at 25, 50, and 75°C, and to analyze rehydration ratio (RR) by artificial neural network (ANN) approach. RRs of both microwave‐processed specimens (between 5.66 and 7.91) and sun‐dried ones (between 5.38 and 6.37) were always greater than the others, at all rehydration temperatures. Rehydration temperature had an enhancing effect on RRs, and the image that closely resembled the fresh sample was captured in the microwave‐dried rehydrated samples. Nearly 180–240 min was adequate for rehydration of all slices. Zeroth‐ and first‐order kinetic models, Peleg, Peppas, and two‐term exponential decay models, as well as a new rational model were tested to describe rehydration kinetics. Two‐term equation was almost superior with high R2 (between 0.9734 and 0.9994) and the lowest root mean square error (RMSE) and χ2. Novel recommended mathematical expression was also successful (R2: 0.9679–0.9989, χ2: 0.0051–0.0975, RMSE: 0.0191–0.0866). Regarding relationship between actual and predicted RRs and performance indices of ANN equation, overall R and R2 were recorded as follows: 0.9975–0.9950 (sun‐dried) > 0.99642–0.9929 (hot air‐dried) > 0.9955–0.9911 (infrared‐dried) > 0.9907–0.9815 (microwave‐dried), respectively. The proposed ANN model and novel mathematical formula not only offer a considerable potential in predicting rehydration patterns and developing rehydration protocols in food production sector, but also contribute to save energy by completely understanding the process and optimizing conditions.Practical applicationsThe eggplant is a cherished vegetable often dried, providing a velvety texture and rich taste in Anatolian recipes. In Anatolia, colorful vegetables are strung together on strings, adorning traditional houses, and serving as a testament to the region's deep‐rooted cultural heritage. The Turkic people in Central Asia demonstrated remarkable skill in preserving plants by carefully drying leaves, stems, or roots. The process of rehydration entails the addition of water to dehydrated eggplant slices in order to restore their initial dimensions and attributes for being consumed. This investigation integrates the utilization of ANNs to simulate the rehydration procedures of eggplant. This simulation can be employed for the purpose of quality control and standardization within the food sector. Hence, food manufacturers can anticipate the rehydration characteristics of their products across various production batches and conditions, thereby guaranteeing adherence to quality benchmarks.

Lean Manufacturing Analysis to Minimize Waste on The Production Process

Improvements to eliminate or reduce non-value added activities that cause waste with Lean Manufacturing analysis on CV ABC. Lean Manufacturing is a continuous effort to eliminate (waste) and increase value add (value added) products (goods/services) to provide value to customers (customer value). Objective This research is to determine the factors that cause waste in the production process and to give proposal on company method minimize waste which has been identified. Data in this research are quantitative and qualitative data. Primary data processing in the form of standard time calculations took 30 data, which means 30 batches of fish roulade production. Lean Manufacturing Approach carried out by analyzing the flow of information and the flow of goods using the Value Stream Mapping method. Waste Which dominant happen that is time waiting, unnecessary movements and processes it is not in accordance with. Waste of time waiting occurs in process steaming. Waste time wait on process production can be minimized by adding production facilities which can reduce the dough waiting outside the machine steaming. Waste movement which no need can minimized by increased supervision from superiors as well as providing firm action to workers. Waste for non-compliant processes can be reduced by clarify standard operation Which applies in company and stick it in every production process, as well do maintenance machine in a way regular so that performance machine always deep conditions which good. Reduced time to reduce lead time ie amounting to 37.78 minutes. This time is obtained from subtraction the process of taking spices, weighing flour and vegetables as well as preparation and maximization activities process draining with addition facility. Matter this result change time leads time production from 674.44 minutes to 636.66 minutes.

Solvent-free parallel artificial liquid membrane extraction for drugs of abuse in plasma samples using LC-MS/MS

BackgroundParallel artificial liquid membrane extraction (PALME) is a 96-well plate setup variant of liquid-phase microextraction. Basic or acidic analytes are extracted in neutral form from the sample, through a supported liquid membrane (SLM), and into aqueous acceptor. PALME is already considered a green extraction technique, but in the current conceptual work, we sought to make it even greener by replacing the use of organic solvents with essential oils (EO). PALME was combined with LC-MS/MS for analysis of plasma samples and multiple drugs of abuse with toxicological relevance (amphetamines, phenethylamines, synthetic cathinones, designer benzodiazepines, ayahuasca alkaloids, lysergic acid diethylamide, and ketamine). ResultsFourteen EO were compared to organic solvents frequently used in PALME. The EO termed smart & sassy yielded the best analyte recovery for all drugs studied and was thus selected as SLM. Then, factorial screening and Box-Behnken were employed to optimize the technique. The extraction time, concentration of base, sample volume, and percentage of trioctylamine significantly impacted analyte recovery. The optimum values were defined as 120 min, 10 mmol/L of NaOH, 150 μL, and 0%, respectively. Once optimized, validation parameters were 1–100 ng mL-1 as linear range, accuracy ±16.4%, precision >83%, 1 ng mL-1 as limit of quantitation, 0.1–0.75 ng mL-1 as limit of detection, matrix effect <20%, and recovery 20–106%. Additionally, EO purchased from different production batches were tested and achieved acceptable reproducibility. Data were in compliance with requirements set by internationally accepted validation guidelines and the applicability of the technique was proven using authentic samples. SignificanceIn this study, the use of an EO provided a solvent-free sample preparation technique suited to extract different classes of drugs of abuse from plasma samples, dismissing the use of hazardous organic solvents. The method also provided excellent sample clean-up, thus being a simple and efficient tool for toxicological applications that is in agreement with the principles of sustainable chemistry.

Anticipated Wait and Its Effects on Consumer Choice, Pricing, and Assortment Management

Problem definition: We investigate the effects of waiting time, mainly due to production in a make-to-batch-order (MTBO) system, on consumer choice behavior, pricing, assortment, and model estimation. In an MTBO system, the seller/manufacturer first collects orders placed within a certain period of time into a batch and then starts the production process. After the production of all orders in a batch are complete, the products are then shipped and delivered to individual consumers. Because of batch production and shipping, the disutility of the waiting time exhibits negative externality. Methodology/results: We adopt the widely used multinomial logit (MNL) model as a starting point and incorporate the anticipated wait into consumers’ decision making. The derived model, referred to as the MNL with wait model, is a solution of the rational expectation equilibrium and is capable of capturing the effects of negative externality induced by anticipated wait that may change the substitution patterns dramatically. We characterize the multiproduct price optimization problem under the MNL with wait model by establishing a one-to-one mapping between the price vector and the choice probability vector. We find that firms tend to charge higher prices for time-consuming items and charge lower prices for time-saving items compared with the optimal prices under the standard MNL model. In addition to price competition, we also study the Cournot-type competition, in which the decision is the choice probability for each firm and establish the existence of a Nash equilibrium. For assortment optimization, we identify mild conditions under which the optimality of revenue-ordered assortments still holds. However, the assortment problem under the MNL with wait model is generally NP-hard, so we develop approximation algorithms with performance guarantees and provide an easy-to-compute tight upper bound. Moreover, we develop an efficient maximum likelihood-based algorithm for model calibration and further conduct numerical studies to showcase the importance of incorporating disutility due to wait in estimation, pricing, and assortment planning problems. Managerial implications: The MNL with wait model can increase prediction accuracy for consumers’ choice behavior especially when they are aware of the potential wait. Failure to take into account the effects of anticipated wait in firms’ decision making may lead to substantial losses. Funding: The research of C. Ke is supported in part by the National Natural Science Foundation of China [Grant 72101113]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0346 .

Regression Metamodel-Based Digital Twin for an Industrial Dynamic Crossflow Filtration Process.

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid-liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited, because fixed recipes are usually applied to run the processes. In order to take the varying properties of biological feed materials into account, we aim to develop a digital twin of an industrial brownfield DCF plant, allowing to optimize setpoint decisions in almost real time. The core of the digital twin is a mechanistic-empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation in the selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach, and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. We considered a total of 24 industrial production batches from the filtration of grape juice from the years 2022 and 2023 in the study. After implementation of the digital twin on site, the campaign mean productivity increased by 15% over the course of the year 2023. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

Characterization of microstructure and magnetic properties of 3D printed bonded magnets made by fused deposition modeling

Abstract Bonded magnets are composite materials consisting of polymer matrix and magnetic powders, prepared by rapid solidification processes from Nd-Fe-B alloys. They are synthesised by the compounding process on a twin-screw extruder, whereby, the finished products of complex shapes can be made from bonded magnets using injection moulding or 3D printing by fused deposition modelling method (FDM). The main advantages of 3D printing are the possibility to produce parts with complex geometries that are not possible with traditional manufacturing techniques and low-cost production of small batches. The aim of the research work was to identify the optimum processing parameters, which would give 3D printed bonded magnets characteristics similar to those produced by injection moulding. The characterization of the microstructure of bonded magnets was made on cryo-fractured, conventionally mechanically prepared and ion beam polished samples. The microstructures of bonded magnets were analysed by stereo, optical and scanning electron microscopy. Additionally, the influence of the 3D printing parameters on the magnetic properties has been examined. The results of the research work have shown that desired magnetic properties of 3D printed bonded magnets can be obtained by optimizing the thickness of the printed layer, printing speed and flowrate. In addition, it was revealed that selection of the materialographic preparation method plays a crucial step for correct microstructural characterization. Namely, the impropriate sample preparation results in artifacts that are mostly misinterpreted as microstructural defects (pores, cracks, non-adherent layers, etc.) accidently caused during 3d printing.

Emerging Strategies for the Synthesis of Correlated Single Atom Catalysts.

People have been looking for an energy-efficient and sustainable method to produce future chemicals for decades. Heterogeneous single-atom catalysts (SACs) with atomic dispersion of robust, well-characterized active centers are highly desirable. In particular, correlated SACs with cooperative interaction between adjacent single atoms allow the switching of the single-site pathway to the dual or multisite pathway, thus promoting bimolecular or more complex reactions for the synthesis of fine chemicals. Herein, the structural uniqueness of correlated SACs, including the intermetal distance and electronic interaction in homo/heteronuclear metal sites is featured. Recent advances in the production methods of correlated SACs, showcasing the research status and challenges in traditional methods (such as pyrolysis, wet impregnation, and confined synthesis) for building a comprehensive multimetallic SAC library, are summarized. Emerging strategies such as process automation and continuous-flow synthesis are highlighted, minimizing the inconsistency in laboratory batch production and allowing high throughput screening and upscaling toward the next-stage chemical production by correlated SACs.

Coupling atomic layer deposition with supercritical fluid processing to provide a highly C3H6‐selective ZIF‐8 membrane

AbstractZeolitic Imidazolate Framework‐8 (ZIF‐8) membrane is well recognized for its great capability in efficient C3H6/C3H8 separation, and their sustainable fabrication is extremely advantageous for batch production and commercial application. In this study, a solvent‐free protocol was developed for sustainable production of ZIF‐8 membranes with exceptional C3H6/C3H8 selectivity through combining atomic layer deposition (ALD) technique with in situ supercritical fluid (SCF) processing. Experimental results indicated that maintaining appropriate deposition number and conducting SCF processing in gas‐like supercritical carbon dioxide region were crucial for obtaining ZIF‐8 membranes with excellent C3H6/C3H8 selectivity (~185), one of the highest among ZIF‐8 membranes reported in the literature. Noteworthy, not only residual 2‐methylimidazole ligands along with vented gaseous CO2 could be fully recycled and repetitively utilized, but also obsoleting ZIF‐8 membranes could be effectively patched upon SCF‐based treatment. The above‐mentioned solvent‐free protocol holds great application prospects for sustainable production of diverse high‐performance metal–organic framework membranes.

Appearance and Chemical Characteristics of Seeds of Radish with Different Skin Colors

Background: Different parts of radish, such as the roots, leaves, and seeds, can be used in culinary and traditional medicine treatments in East Asia. Objective: The aim of this study was to explore the difference of Raphani Semen (RS) which is sourced from radishes (R) with red skin (RRS), green skin (GRS), and white skin (WRS) through their appearance and chemical characteristics. Materials and Methods: A total of 25 batches of RRS, GRS, and WRS products were collected. The length, width, thickness, shape, flavor, and skin color of RS were carried out using vernier caliper and flavor rating assessment, respectively. The HPLC-DAD method was used to establish fingerprint analysis and measure the contents of sinapine thiocyanate and 3,6’-disinapoyl sucrose. Results: It was shown that the appearance and content varied in RRS, GRS, and WRS. In addition, the method of PLS-DA and LDA can differentiate RRS, GRS, and WRS. Conclusion: RS is diverse, and the appearance and chemical characteristics can be used to differentiate RRS, WRS, and GRS.

Preparation of Oriented Superhydrophobic Surface to Reduce Agglomeration in Preparing Melt Marbles.

Numerous innovative granulation techniques utilizing the concept of liquid marbles have been proposed before. However, these processes frequently encounter issues such as collisions, aggregation, and fragmentation of liquid/melt marble during the granulation process. In this study, the oriented superhydrophobic surface (OSS) was successfully prepared by utilizing copper wire to solve the above problem, facilitating efficient batch production and guided transportation of uniform marbles. The parameters and mechanisms of this process were thoroughly studied. The optimized structure is that the copper wire spacing (d) and height (h) are set as 1.0 and 0.1 mm, respectively. This resulted in a surface contact angle (CA) of 156° and anisotropic sliding (ΔSA) of 16.3 ± 1.34°. Using the prepared substrate, high-quality urea products were successfully obtained through the controlled transport of urea melt marbles. The mechanism of guided and directional drag reduction, based on the solid/solid contact on the surface, is proposed. These findings in this study have significant implications for improving granulation processes.

Population dynamics and bidirectional transfer of Listeria monocytogenes and Shiga toxin-producing Escherichia coli during cheese production in wooden vats

Wooden vats are used in the production of some traditional cheeses as the biofilms on wooden vat surfaces are known to transfer large quantities of microbes to cheese. However, the safety of using wooden vats for cheese production remains controversial as the porous structure of wood provides an irregular surface that may protect any attached pathogen cells from cleaning and sanitation processes. On the other hand, the absence of pathogens in wooden vats has been reported in multiple studies and wooden materials have not been associated with foodborne illness outbreaks. The present study determined the survival of Listeria monocytogenes and Shiga toxin-producing Escherichia coli (STEC) during the production of an uncooked pressed cheese in wooden vats as well as their ability to transfer to the wood and then to milk used in subsequent batches of cheese production in the absence of formal cleaning. Results from the study indicate that pathogens inoculated in milk grew during production of the uncooked cheese, but showed limited ability to colonize the wooden vats and contaminate subsequent batches. These results suggest that the risks of using wooden vats to produce cheese is low if the milk is of high microbiological quality.

A bifunctional cobalt catalyst for the synthesis of waxy diesel fuel by the Fischer–Tropsch method – from the development to implementation. Part 3. The experience of industrial implementation of the preparation technology

The work summarizes the results of testing the technology for preparing a bifunctional cobalt catalyst for the synthesis of hydrocarbons from CO and H2, obtained by extruding a mixture of Co-Al2O3 /SiO2 catalyst powders and HZSM-5 zeolite with a binder – boehmite in industrial conditions (2 batches of 50 kg each were prepared). The catalyst technology was implemented on the equipment of Ishimbay Specialized Chemical Catalyst Plant LLC. The obtained industrial samples of the catalyst were characterized by XRF, TPR H2, DTG, and tested in the synthesis of hydrocarbons from CO and H2 at a temperature of 250 °C, a pressure of 2.0 MPa, GHSV 1000 h–1. It has been shown that the implementation of the technology of a bifunctional cobalt catalyst for the production of low pour point diesel fuel in industrial conditions makes it possible to reproduce the characteristics of the catalyst obtained in laboratory conditions. The technology for producing the catalyst can be recommended for the production of industrial batches. It was determined that changes in the heat treatment conditions of the catalyst, as well as the presence/absence of a peptizer and pore former do not lead to a significant decrease in the productivity of C5+ hydrocarbons. The contentof the diesel fraction in C5+ products obtained from industrial samples of the catalyst remains at the level of the value obtained from the laboratory sample of the catalyst. At the same time, the low-temperature properties of diesel fuel obtained using all catalyst samples have similar values. Using an industrial sample synthesized without the use of a peptizing agent and a pore-forming component, the best lowtemperature properties of diesel fuel were achieved – the cloud point and fluid loss point were minus 16 and minus 24, respectively.

Optimization and Validation of a Harmonized Protocol for Generating Therapeutic-Grade Dendritic Cells in a Randomized Phase II Clinical Trial, Using Two Varied Antigenic Sources.

Autologous dendritic cell (DC)-based immunotherapy is a cell-based advanced therapy medicinal product (ATMP) that was first introduced more than three decades ago. In the current study, our objective was to establish a harmonized protocol using two varied antigenic sources and a good manufacturing practice (GMP)-compliant, manual method for generating clinical-grade DCs at a limited-resource academic setting. After obtaining ethical committee-approved informed consent, the recruited patients underwent leukapheresis, and single-batch DC production was carried out. Using responder-independent flow cytometric assays as quality control (QC) criteria, we propose a differentiation and maturation index (DI and MI, respectively), calculated with the QC cut-off and actual scores of each batch for comparison. Changes during cryopreservation and personnel variation were assessed periodically for up to two to three years. Using our harmonized batch production protocol, the average DI was 1.39 and MI was 1.25. Allogenic responder proliferation was observed in all patients, while IFN-gamma secretion, evaluated using flow cytometry, was detected in 10/36 patients and significantly correlated with CD8+ T cell proliferation (p value-0.0002). Tracking the viability and phenotype of cryopreserved MDCs showed a >90% viability for up to three years, while a mature DC phenotype was retained for up to one year. Our results confirm that the manual/semi-automated protocol was simple, consistent, and cost-effective, without the requirement for expensive equipment and without compromising on the quality of the final product.

Association rules combined fuzzy decision quality control technology in intelligent manufacturing

In the context of the rapid development of intelligent manufacturing, effective quality control has become the key to improving manufacturing efficiency and product quality. Traditional quality control methods are often inadequate when faced with complex production data and changing manufacturing environments. Therefore, exploring new intelligent quality control technologies to cope with these challenges in intelligent manufacturing has become an important research direction. In view of this, the study proposed a quality control technology that combines association rules and fuzzy decision-making. Firstly, association rule mining methods are used to analyze production data and extract the relationships between key quality factors. Secondly, based on these association rules, fuzzy decision technology is used to adjust and optimize the production process, ultimately achieving quality control of products in the intelligent manufacturing production process. The data showed that when running on the training set and validation set, the research method reached a stable state at 18 and 46 iterations of the system, respectively, with a minimum cost loss function value. In both batch production lines, the detection efficiency under the operation of the research method remained at 2200 units per minute. During the process of repeating the system for 6 times, the research method consistently achieved maximum control accuracy and minimum time consumption. The satisfaction of the four experts with the operation of the research method in small batch production and large batch production was 9.32 and 9.2, respectively, significantly higher than other algorithms. The above results indicated that the proposed method can effectively improve the efficiency of product quality control, reduce production costs, and ultimately reduce the rate of defective products in the production process.