Product Quality Stability Research Articles

Influence of lay‐up sequences on cylindrical parts drawability of CFRP laminates

AbstractCarbon Fiber Reinforced Polymers (CFRP) is one of the ideal materials in the field of automotive lightweight. As one of the traditional manufacturing processes of CFRP, autoclave process has long production time, high cost and complicated operation. To overcome these shortcomings, deep drawing can be used for CFRP forming. It has the advantages of stable product quality, good repeatability, low cost, simple operation and can produce parts with complex shapes. In this paper, the influence of lay‐up sequences on the drawability of cylindrical parts was studied by using self‐made deep drawing device. The results showed that among the four lay‐up sequences [0]°12, [0/90]°6, [0/90/45/‐45/90/0]°S and [15/30/……/180]°, the [15/30/……/180]° lay‐up parts had excellent deformation coordination ability due to less angular variation in the fiber direction, and the maximum depth of 21.99 mm could be obtained. Due to the influence of wrinkles, the thickness of [15/30/……/180]° parts increased at the die corner and side wall. The thickness decreased at the flange, punch corner and bottom due to the compressive pressure. In addition, [15/30/……/180]° lay‐up parts benefited from the high level of in‐plane isotropy of the helicoidal structure, it had the fewest forming defects. The resin poor areas and resin rich areas were less and no fiber cracks were found.Highlights Investigated the influence of lay‐up sequences on the drawability of CFRP. The forming depth of [15/30/……/180]° parts was 21.99 mm. [15/30/……/180]° lay‐up parts had the best drawability. No cracks were found on the [15/30/……/180]° part.

Selection of Official and Unofficial Lemon Raw Material Suppliers (Case Study of Tasikmalaya City Branch Mixue)

Mixue is one of the most popular contemporary drink brands in Indonesia. One of the important raw materials for Mixue is lemon. In Indonesia, there are two types of lemon raw material suppliers, namely imported suppliers and local suppliers. This research aims to examine and compare the use of lemon raw materials from official suppliers of PT. Zhisheng Pacific Trading (MIXUE) imports raw materials from local suppliers in the context of use by Mixue retail stores operating in the food and beverage sector. This research uses mixed methods between qualitative methods and quantitative methods. Data collection techniques in this research were observation, interviews and questionnaires. Mixue is one of the most popular contemporary drink brands in Indonesia. Based on the results of this research, it can be concluded that local suppliers are superior to most of the factors considered than official suppliers. There is positive interest from experts involved in research regarding the use of lemon raw materials from local suppliers. From this research, recommendations and suggestions were obtained to consider updating policies related to regulations on the use of raw materials from third parties by considering all the advantages possessed by local suppliers in various factors. The results of this research state that local suppliers are superior in price and logistics efficiency. Official suppliers offer stable and reliable product quality.

Potential of Powder Rheology for Detecting Unforeseen Cross-Contamination of Foreign Active Pharmaceutical Ingredients.

Unexpected cross-contamination by foreign components during the manufacturing and quality control of pharmaceutical products poses a serious threat to the stable supply of drugs and the safety of customers. In Japan, in 2020, a mix-up containing a sleeping drug went undetected by liquid chromatography during the final quality test because the test focused only on the main active pharmaceutical ingredient (API) and known impurities. In this study, we assessed the ability of a powder rheometer to analyze powder characteristics in detail to determine whether it can detect the influence of foreign APIs on powder flow. Aspirin, which was used as the host API, was combined with the guest APIs (acetaminophen from two manufacturers and albumin tannate) and subsequently subjected to shear and stability tests. The influence of known lubricants (magnesium stearate and leucine) on powder flow was also evaluated for standardized comparison. Using microscopic morphological analysis, the surface of the powder was observed to confirm physical interactions between the host and guest APIs. In most cases, the guest APIs were statistically detected due to characteristics such as their powder diameter, pre-milling, and cohesion properties. Furthermore, we evaluated the flowability of a formulation incorporating guest APIs for direct compression method along with additives such as microcrystalline cellulose, potato starch, and lactose. Even in the presence of several additives, the influence of the added guest APIs was successfully detected. In conclusion, powder rheometry is a promising method for ensuring stable product quality and reducing the risk of unforeseen cross-contamination by foreign APIs.

Multi-stage manufacturing process parameter optimization method based on improved marine predator algorithm

Product quality is a critical factor in manufacturing industry competition, and mechanical processing technology has been widely applied in manufacturing, directly affecting product quality. Therefore, it is very important to find the appropriate optimal parameters to improve the impact of processing on product quality. However, modern production processes are characterized by complex mechanisms and the mutual influence of multiple processes, which poses higher challenges for optimizing processing technology parameters. In this regard, the thesis proposes a method for optimizing process parameters in multi-process manufacturing based on an improved marine predator algorithm, aiming to optimize and improve process parameters in multi-process manufacturing processes. Firstly, a multi-process modeling strategy is adopted to explore the nonlinear relationship between process parameters and quality indicators based on multi-gene genetic planning, establishing a multi-process parameter optimization objective model. This effectively solves the problem of modeling difficulty caused by severe coupling of multiple processes. Then, to improve the efficiency of solving the optimization objective model, an improved marine predator algorithm is proposed, utilizing reverse learning strategies and mixed control parameters to enhance optimization capability, thereby obtaining the global optimal solution. Finally, using production process data from a certain factory as an example, the feasibility of the proposed method is verified, achieving the goal of multi-process process parameter optimization and ensuring the stability of product quality.

An improved skip-lot sampling scheme with resampling mechanism using an advanced capability index

In today’s competitive market, product quality holds significant importance for consumers, motivating companies to prioritise high-quality product development and enhance their competitiveness. Acceptance sampling plans have proven to be indispensable tools in quality control practices, enabling producers and consumers to protect their interests by establishing evaluation rules for product batches. As defective rates decrease in modern manufacturing, skip-lot sampling plans (SkSP) have emerged as a suitable way for lots exhibiting stable and excellent product quality. Additionally, repetitive group sampling plan (RGSP) offers a cost-effective way to maintain desired protection for both parties, especially in costly or destructive inspection scenarios. Therefore, this study integrates the benefits of process capability indices, SkSP, and RGSP to develop a new variables SkSP with RGSP as its reference plan based on an advanced capability index Cpmk (called Cpmk -based SkSP-RGSP). The proposed plan achieves comparable discriminatory power to traditional sampling plans while significantly reducing the required sample size. Detailed tables of plan parameters are given to facilitate effective implementation for practitioners.

Replacement of raw materials in the formulation of industrial products based on near-infrared spectroscopy and particle swarm optimization

The replacement of raw materials in formulations is a crucial but challenging issue in industrial production. The goal is to substitute the inadequate or costly raw materials or their combinations with the adequate or potentially adequate inventory but while still ensuring the consistency and stability of product quality. In the present study, near-infrared (NIR) technology is first used for rapid and non-destructive analysis and further comprehensive characterization of the chemical characteristics of different raw materials. Afterwards, the particle swarm optimization (PSO) method is modified for the processing of NIR data and formulation replacement. Under the conditions of optimal constraints, an integrated strategy for replacement and optimization of raw materials in formulation is fully established. This yields optimized results for replacements that meet real demands in formulation design. In addition, the simulation of NIR spectra is initially realized by combining multiple Gaussian functions, which constructs simulated datasets that are suitable for verifying the accuracy and reliability of the proposed method. The real example of the replacement of raw materials is applied to comprehensively demonstrate the effectiveness and capability of the strategies proposed in this study. Among the 3,000 combinations used for formulation replacement, the recommended results obtained through the PSO method exhibit a high targeted proportion, attaining 81.82%, in which the difference between the optimal and original formulation is less than 0.0005. The results of expert validation indicate that 83% of the evaluations are consistent with spectral similarity assessments, which shows that the replaced results effectively meet the requirements for objective, scientific and digitized requirements in product manufacturing. Furthermore, this proposed method is applicable to various analysis and application scenarios on the basis of NIR analysis and global optimization.

Influence of different indica rice (Oryza sativa L. subsp. indica Kato) flours on the quality and flavor characteristics of fermented rice cakes

AbstractBackgrounds and ObjectivesFermented rice cake is a common traditional fermented food in China with a crystal clear color, unique flavor, dense honeycomb structure, and soft and delicate texture. As its raw materials are not uniform, different batches often have poor product quality stability and different volatile flavor components. Therefore, this study investigates the effects of different indica rice flours on processing adaptability and their impacts on the flavor characteristics.FindingsThe results show that the seven indica rice flours have significantly different basic compositions and physicochemical properties (p < .05). Specifically, the fermented rice cake prepared with TG2 showed a large specific volume, good elasticity, bright color, high volatile flavor compound contents, and the maximum comprehensive sensory evaluation score. Additionally, partial least squares discriminant analysis was conducted, and three volatile compounds were identified with variable importance for the projection >1 and odor activity value >1 as the criteria, namely, ethanol, styrene, and phenethyl alcohol, which played major roles in forming the overall aroma of fermented rice cakes.ConclusionsThe texture, color, and sensory score of fermented rice cakes are closely related to the basic components of the indica rice flour, and the most popular fermented rice cakes have a specific volume of no less than 2 mL/g, an L* of above 78, and a TPA‐tested elasticity of about 3 mm.Significance and NoveltyThis study may help improve the processing of fermented rice cakes and provide a theoretical basis for their production and flavor control.

ENERGY TECHNOLOGY OF DAIRY PRODUCTS QUALITY ASSESSMENT USING IMPEDANCEMETRY

The article examines the peculiarities of milk quality research using the impedance method. Impedance is a complex parameter that characterises the interaction of the electric current with milk. The authors have developed a methodology based on the specified method to assess the quality of dairy products and have created a hardware and software complex that makes it possible to analyse the quality of dairy products. The research, conducted using the developed hardware and software complex, revealed several regularities characterising different types of milk. These regularities demonstrate the relationship between the impedance signal and the milk fat index. Analysis of the obtained data allowed us to identify differences in impedance between samples with varying fat content and origin from different manufacturers. The developed experimental setup prototype enabled the following research stages: checking the change in voltage during the passage of an alternating electric current through milk. This work used the AFS Sensor DAQ data acquisition system, designed for sensor control projects and which runs with NI LabVIEW software, for data output. The authors tested the installation while investigating the impedance of dairy products with different fat content and from multiple manufacturers. During the experiment, we measured the impedance and capacitance of the samples, and the obtained data became available on a PC for further analysis. The aim was to study the electrical properties of dairy products and their dependence on the composition and manufacturer. The obtained results represent valuable information for dairy product production and quality control. It is possible to use them to optimise production processes, ensure stable product quality, and improve consumer characteristics of dairy products. The obtained data contribute to a deeper understanding of the relationship between the impedance signal and the composition of dairy products, which can lead to the development of new methods of analysis and evaluation of the quality of food products. In addition, these studies have implications for food science and nutrition. Keywords: dairy products, milk quality, impedancemetry, electrical measurements, hardware and software complex.

Production of Galacto‐Oligosaccharides by Enzymatic Membrane Reactors Using Free β‐Galactosidase

AbstractPrebiotic galacto‐oligosaccharides (GOSs) are synthesized by transgalactosylation of lactose using β‐galactosidases. Well‐established, large‐scale production of GOS is performed by stirred‐tank reactors (STR) operating in batch fashion. Main manufacturing expenses are attributed to enzyme purchase costs and the downstream operations required for subsequent enzyme inactivation and removal. In the last decades, several studies and patents have appeared on free enzyme membrane reactor (FEMR) as an economic alternative to STR due to enhanced enzyme recovery and reuse. In this review, we attempt to summarize the key design aspects, the catalytic performance and efficiency of FEMR compared to STR technology, and the main concerns related to stable product quality over long‐term operation.

Design and Implementation of an RFID System for Order Tracking in Smart Manufacturing: A Case Study of a Mechanical Factory

Six Sigma is a well-liked technique for raising productivity and quality of output in industrial processes and process improvement initiatives. In the Six Sigma methodology, abnormalities in the operational process are reviewed and examined using the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle. Corrective action is then taken to reduce bottlenecks to enhance productivity, ensure stable product quality, and create a tightly connected production cycle. This study proposes the use of integrating statistical hypotheses into the DMAIC cycle of the Six Sigma method and proposes the use of Enterprise Resource Planning (ERP) modules to monitor production processes at a major mechanical processing company. Lean tools are also suggested by the author to be used in the analysis phase. The research results show a reduction in the rate of late product delivery to users from 6.28% to 0.02% per month. The lean tool application model, statistical hypotheses, and industry 4.0 system are integrated into the EPR system at the company's improvement department and are considered a model for manufacturing companies to refer to in implementing improvements for their products. Production processes in different companies.

Nonlinear Dynamic Granger Causality Analysis Framework for Root-Cause Diagnosis of Quality-Related Faults in Manufacturing Processes

Root-cause diagnosis of quality-related faults plays a crucial role in ensuring stable product quality and high-efficient production for modern manufacturing processes. However, there exists complex nonlinear and dynamic sequential characteristics for process and quality data before and after faults, which contain important fault information. How to fully explore and use this information to locate the root-cause and identify the propagation path is a hot topic. Thus, a new nonlinear dynamic Granger causality analysis method is developed for root-cause diagnosis, which will provide timely and useful reference information to take reasonable measures for field engineers. First, an optimal variable division based on minimal redundancy maximal relevance algorithm is presented to get the quality-related variables. Then, a new attention-based random disturbance gated recurrent unit is designed for nonlinear dynamic Granger causality analysis, aiming at locating the root-cause and identifying the propagation path of quality-related faults. A typical manufacturing process, the hot strip mill process is used for verification. The results show the practicability of the proposed method, and its strong robustness to noise. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Reasonable and feasible root-cause diagnosis technology will provide timely reference information for field operators to take maintenance measures. However, the nonlinear and dynamic characteristics of time-series data before and after quality-related faults have brought great challenges to the traditional root-cause diagnosis techniques. To address this issue, a practical nonlinear dynamic Granger causality analysis method is proposed, which is purely data driven without knowing complex mechanism knowledge. It will provide feasible solutions for safety monitoring of hot strip mill process as the representative of manufacturing processes.

Replacing traditional pretreatment in one-step UF with natural short-distance riverbank filtration: Continuous contaminants removal and TMP increase relief

Scientists have been focusing on applying more natural processes instead of industrial chemicals in drinking water treatment to achieve the purpose of carbon emissions reduction. In this study, we shortened the infiltration range of riverbank filtration, a natural water purification process, to form the short-distance riverbank filtration (sRBF) which retained its ability in water quality improvement and barely influenced the groundwater environment, and integrated it with ultrafiltration (UF) to form a one-step sRBF-UF system. This naturalness-artificiality combination could realize stable contaminants removal and trans-membrane pressure (TMP) increase relief for over 30 days without dosing chemicals. Generally, both sRBF and UF played the important role in river water purification, and the interaction between them made the one-step sRBF-UF superior in long-term operation. The sRBF could efficiently remove contaminants (90 % turbidity, 60 % total nitrogen, 30 % ammonia nitrogen, and 25 % total organic carbon) and reduce the membrane fouling potential of river water under its optimum operation conditions, i.e., a hydraulic retention time of 48 h, an operation temperature of 20 °C, and a synergistic filter material of aquifer and riverbank soil. Synergistic adsorption, interception, and microbial biodegradation were proved to be the mechanisms of contaminants and foulants removal for sRBF. The sequential UF also participated in the reduction of impurities and especially played a role in intercepting microbial metabolism products and possibly leaked microorganisms from sRBF, assuring the safety of product water. To date, the one-step sRBF-UF was a new attempt to combine a natural process with an artificial one, and realized a good and stable product quality in long-term operation without doing industrial chemicals, which made it a promised alternative for water purification for cities alongside the river.

An innovative composite elbow manufacturing method with 6-axis robotic additive manufacturing for fabrication of complex composite structures

Filament winding method is the most commonly used method to produce profiles with different cross sections as composite product manufacturing. In this method, fiber material is wound with resin at different angles on a mold that has a suitable cross section shape. As a winding strategy, angled and helical winding can be done. Motion planning for this process is done with geodesic and nongeodesic theories. Requirement to use mold in the filament winding method increases the cost. Also, there is an obligation to helical windings. In winding of different layers, 90° angle cannot be given between the layers. To overcome all these constraints, UV curing can be achieved using photopolymer resin and continuous fiber glass fiber with the help of robotic additive manufacturing technology. Toolpath strategies for production has a key role in this work. As a tool path strategy, nonplanar slicing can be done and manufactured composite elbow in angular layers without mold. Then, under favour of 6-axis mobility of the industrial robot arm, layers can be obtained at exactly 90° angle. In addition, in this method, unlike other winding methods, internal voids, i.e. a filling rate, can be given within the cylindrical encircled layers. In order to verify whether the elbows produced with this method meet the requirements of the desired applications in the industry in terms of mechanical properties, at different filling rates (50%, 75%, 100%), winding turns (0 and 1/8), and different fiber densities (45%, 55% and 65%) 90° curved composite elbows were produced and their internal pressure strength tests were tested. Afterwards, an optimization study was carried out with the Taguchi method for the production parameters that will maximize the internal pressure strength. According to the results of the optimization study, it is seen that it is appropriate to choose the printing parameters that will obtain the highest internal pressure strength values for production with this method, 100% fill rate, 65% fiber density and 0° winding angle. The products made of this process have the advantage of easy-shaping, reasonable ratio of axial strength and encircled strength, specification easy-unifying, stable product quality.

A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process

The stable control of product quality when abnormal working conditions occur in the industrial production process is essential to improve product quality and economic efficiency. However, the process industry suffers from multivariate, nonlinear, uncertainty, long time delays and frequent failures, making its modeling and control difficult. In this paper, a novel method of neural network model predictive control integrated process monitoring (PM-NNMPC) is proposed. First, the combination of gated recurrent unit and convolutional neural network (GRU-CNN) is used to extract the features from the time and the space separately to build prediction models for different working conditions of the process. Then, a process monitoring and fault diagnosis method of principal component analysis combined with deep neural network and XGBoost (PCA-DNN-XGBoost) is designed to monitor the working conditions in real-time and diagnose accurately when faults occur. Finally, a new operation control framework integrating process monitoring and fault diagnosis into the model prediction control is designed to solve the problem that equipment operation failure, which makes the product quality fluctuation because of the model mismatch and the system runaway. The experimental results of hot rolling process show that PM-NNMPC can effectively control the system under normal working conditions and in case of the system failure to ensure the stable product quality.

A reduced order model based on adaptive proper orthogonal decomposition incorporated with modal coefficient learning for digital twin in process industry

The digital twin (DT) technology provides a viable and promising direction for improving the level of the production status monitoring and the overall product quality in various fields. However, the accuracy of working condition identification, the timeliness of process adjustment, and the stability of product quality are put forward higher requirements in the process industry, which is characterized by nonlinear, large-scale, and dynamic complex systems. Therefore, it still remains a tricky challenge to construct and maintain an effective and accurate DT model in the process industry. A reduced order model (ROM) with the adaptive updating ability is proposed. The adaptive proper orthogonal decomposition (APOD) is adopted to achieve the continuous iteration and the adaptive optimization of the reduced basis set. Correspondingly, an adaptive learning algorithm based on the least squares support vector regression (LS-SVR) is developed to quickly obtain the modal coefficients and effectively circumvent the prohibitively high computational cost. In this way, the physical field of interest is expressed in a low-dimensional approximation with a high accuracy. The effectiveness of the method is verified by a case study in the process industry. Results show that the proposed model displays a high-precision fitting and a significant time saving for the full order model (FOM).

Dynamic scheduling of multi-memory process flexible job shop problem based on digital twin

Digital twin is one of the newly-emerged enabling technologies for achieving intelligent manufacturing. Based on the physical–digital convergence, digital twin provides manufacturing systems with a new model of collaboration between the workforce and industrial processes. With the characteristics of real-time communication and data-driven enablers, the digital twin scheduling strategy requires close cooperation between workers, systems and processes. However, in the process of digitization and intelligentization, industry will need to face the challenge of supporting new technologies and worker skills development. To this end, the paper considers workers’ multi-memory process (learning and forgetting) in the flexible job shop scheduling problem (MPFJSP). Meanwhile, the dynamic scheduling strategy of the digital twin-driven MPFJSP is proposed under machine breakdowns aiming at simultaneously minimizing the makespan, total carbon emissions, total production cost and product quality stability. A virtual workshop is adopted to simulate and optimize the dynamic scheduling scheme to realize intelligent workshop scheduling. Finally, a computational experiment is carried out to verify the effectiveness and advantages of the proposed intelligent scheduling strategy.

A review on drug stability

Stability is crucial to the process of developing new drugs. Stability studies are regarded as a must for the acceptance and approval of any pharmaceutical product since they guarantee the stability of product quality, safety, and efficacy during the shelf life. This article will reduce the knowledge gap on the most important aspect for researcher or a developer who is developing a formulation by providing a complete information about drug stability, principle of drug degradation, Force degradation studies, Stability studies and their Classification, Factor Affecting Stability of Drug, Mechanism of Drug Degradation, Stability Testing, and Different ways to increase Drug Stability. As the different sources provided in this article which are mentioned above will resolve any problem regarding the stability of drug and by resolving the problem, it will influence a science oriented or research-oriented person to develop any formulation by providing the complete knowledge about this particulate topic.

Surface Modified CoCrFeNiMo High Entropy Alloys for Oxygen Evolution Reaction in Alkaline Seawater

Electrolysis of seawater is a promising technique to desalinate seawater and produce high-purity hydrogen production for freshwater and renewable energy, respectively. For the application of seawater electrolysis technique on a large scale, simplicity of manufacture method, repeatability of catalyst products, and stable product quality is generally required in the industry. In this work, a facile, one-step, and metal salt-free fabrication method was developed for the seawater-oxygen-evolution-active catalysts composed of CoCrFeNiMo layered double hydroxide array self-supported on CoCrFeNiMo high entropy alloy substrate. The obtained catalysts show improved performance for oxygen evolution reaction in alkaline artificial seawater solution. The best-performing sample delivered the current densities of 10, 50, and 100 mA cm−2 at low overpotentials of 260.1, 294.3, and 308.4 mV, respectively. In addition, high stability is also achieved since no degradation was observed over the chronoamperometry test of 24 h at the overpotential corresponding to 100 mA cm−2. Furthermore, a failure mechanism OER activity of multi-element LDHs catalysts was put forward in order to enhance catalytic performance and design catalysts with long-term durability.

Factors analysis of lean six sigma practices in the Vietnam wood industry

Six sigma is valuable to process control and ensure the stability of product quality. Lean practices or six sigma in isolation cannot remove all types of waste, control the process statistically, and remove process variability. The integrated development of the two approaches, which are known as Lean and six sigma (LSS), may bring out the advantages of both concepts. Although, many enterprises have succeeded with LSS around the world only less than 20% of enterprises achieve and maintain lean activities for the time being. The aim of this research paper is to present the key factors that constitute a successful implementation of LSS in the wood industry in Vietnam through analysis of LSS implementation. A combination of a comprehensive literature review and a site visit to deeply observe by participants observation for 3 LSS projects and interview the key persons involved in the project was employed in this study.

Toward robust process monitoring of complex process industries based on denoising sparse auto-encoder

Robust industrial process monitoring is crucial to ensure production safety and product quality stability. However, the process monitoring of high-dimensional, nonlinear, complex industrial processes is still challenging due to their inherent complexities, such as multi-phase, multi-field, and tight coupling of multiple sub-processes. In this article, a simple yet robust nonlinear process monitoring scheme based on a kind of denoising sparse auto-encoder (DSAE) is proposed. Specifically, a novel hybrid auto-encoder, namely DSAE, is established, to address the strong redundancy, nonlinearity and noise interference in process variables by integrating a sparse auto-encoder (SAE) and a denoising auto-encoder (DAE). Successively, an online process monitoring model is established by introducing two new process monitoring statistics computed on the DSAE-based feature representation space and residual space, respectively. Moreover, the Kernel density estimation (KDE) approach is adopted to determine the corresponding control limits of the monitoring statistics, which can avoid the conventional empirical assumption of F or Chi-square distribution on monitoring statistics. Extensive confirmative and comparative experiments conducted for the fault monitoring of a nonlinear numerical simulation system, the benchmark Tennessee Eastman process (TEP) and ventilation exhaust fans (VEFs) in the continuous casting process (CCP) from a top steel plant in China show that the proposed method is promising. Specifically, the proposed method performs favorably against the representative process monitoring methods, and it has especially a stronger robustness against noise and uncertain interference in nonlinear process systems.