Industrial Quality Research Articles

Perencanaan Strategi Untuk Mengurangi Produk Reject Dengan Pendekatan Lean Manufacturing

Company X, a feed manufacturer, faced a significant challenge related to a high product rejection rate of 10%. This study aimed to identify the root causes of the problem and implement lean manufacturing-based solutions to reduce waste. Through fishbone and Pareto analysis, the study revealed that human errors and non-standard measuring instruments were the primary contributors to the issue. The PDCA cycle was utilized as a framework for implementing solutions. The results demonstrated that the company reduced the product rejection rate by improving employee adherence to production procedures and conducting regular calibrations of measuring instruments. This research highlights the importance of lean manufacturing approaches in optimizing production processes and enhancing product quality in the feed industry.

Applications of Generalized Hypergeometric Distribution on Comprehensive Families of Analytic Functions

A sequence of n trials from a finite population with no replacement is described by the hypergeometric distribution as the number of successes. Calculating the likelihood that factory-produced items would be defective is one of the most popular uses of the hypergeometric distribution in industrial quality control. Very recently, several researchers have applied this distribution on certain families of analytic functions. In this study, we provide certain adequate criteria for the generalized hypergeometric distribution series to be in two families of analytic functions defined in the open unit disk. Furthermore, we consider an integral operator for the hypergeometric distribution. Some corollaries will be implied from our main results.

Systematic study on the application of lean six sigma in improving efficiency, quality and safety in the food industry

The proposed scientific article aims to analyze the application of Lean Six Sigma in the food industry. To this end, a detailed methodology has been designed that ranges from the selection of the works to the synthesis and presentation of the results obtained. The methodology is based on rigorous inclusion criteria to ensure the relevance and quality of the selected sources, including books, academic articles, theses, and other relevant documents. Through extensive searches of academic databases and other reliable sources, key works were identified that specifically address the implementation of Lean Six Sigma in the context of food production. Once the relevant papers were collected, a critical analysis was conducted to identify common themes, trends, and key findings. The works were classified according to their main focus, such as process improvement, waste reduction, supply chain optimization and food safety assurance. This categorization allowed the information to be organized in a coherent way and to facilitate the synthesis of the results. The results obtained were presented in a table that included details about each selected work, such as title, author, year of publication, abstract and links to the original source. This structured and rigorous approach provides a clear and comprehensive view of the topic, contributing to the advancement of knowledge in this area and offering practical guidance for practitioners and researchers interested in the application of Lean Six Sigma in the food industry. The literature on Lean Six Sigma in the food industry highlights its importance in improving efficiency, quality, and safety. Key recommendations include gradual implementation, appropriate training, focus on quality, and continuous improvement.

Impact of External Debt and Energy Consumption on Environmental Quality in Somalia

This study investigates the interconnections between external debt, energy consumption, industrialization, and environmental quality in Somalia over the period 1990 to 2019, utilizing the ARDL bounds testing approach. Notably, a positive correlation is found between energy consumption and CO2 emissions, emphasizing the imperative for sustainable practices. In the long run, external debt is observed to have an insignificant impact on environmental quality, suggesting a strategic opportunity for policymakers to channel external funds towards environmentally sustainable projects. Short-term strategies underscore the positive link between decreased industrialization and improved environmental quality, while a cautious approach to external debt is crucial due to its short-term negative impact. The significance of the error correction term underscores the need for adaptive policies. Overall, the study advocates for comprehensive policies integrating sustainable practices and directing external financing towards eco-friendly projects, alongside a capacity building and technology transfer partnerships for environmentally conscious development in Somalia.

Information Modeling Implementation in PAO “Rosneft” as Challenge of Digitization in Construction Industry

Key goals of construction industry modernization and quality improvement are the introduction of information modeling which is an effective tool for increasing the enterprise competitiveness through the quality of design and construction, resource optimization and efficiency of interaction between process participants throughout the entire life cycle.The article presents information modeling (3D design) in PAO “Rosneft” with respect to the tasks of ensuring adaptation to design procedure in the company and the transition to full-scale 3D design. The choice of software is justified, real examples of completed pilot projects using information modeling are described, and information modeling implementation are presented. The experience in information modeling implementation helps companies to choose one or another software, and become familiar with the process at PAO “Rosneft”. As a result of the digital transformation of design processes, a model of the life cycle management system of capital construction project is developed, the basis of which is its information model. In addition, the main problems that require solutions for the successful information modeling implementation are identified, i.e., shortcomings of domestic software, shortage or absence of highly qualified personnel, unwillingness of customers to work with technical information, efficiency decrease of the organization due to the introduction of a new approach to project implementation into existing processes, resistance from employees, customers and contractors of construction, and installation works.The information modeling introduction in the PAO “Rosneft” allow to achieve goals for im[1]port substitution of technologies and prepare a worthy response to global geopolitical challenges.

Experimental Analysis and Optimization of Clearance between Punch and Die in Sheet Metal Blanking Process for Soft Steel

Blanking process is commonly used process in production of sheet metal industries to fabricate components. The Global market of sheet metal production is facing highly competition. Due to unavailability of skilled operators and shortage of affordable CAD/CAM systems, it is difficult to predict optimum clearance between punch and die to obtain high quality in small scale industries. Blanking is metal fabricating process and it is characterized by complete material separation. The simulation of the shearing process is complicated as the shear band formed is narrow and fracture criterion is inappropriate. The effect of potential parameters like thickness of sheet, punch-die clearance, tool wear, and punch geometry are studied to investigate their interactions on blanking process. It is important to select process leading parameters which can lead the process such that two similar products from two separate materials can be manufactured on same mold with high quality. This study will help to investigate the effect of thickness of sheet, clearance between punch and die and type of material on blanking process and will help to predict optimal set of parameters to obtain reasonable quality. A mathematical model of blanking process is developed and design of experiments method is used to predict optimum punch-die clearance for soft steel which will result into high quality of component.

Efficient fabric anomaly detection: A transfer learning framework with expedited training times

In industrial quality control, anomaly detection plays a critical role in identifying defective products. However, because of the rarity and time-consuming nature of defect collection, training models often rely solely on defect-free samples. This necessitates the use of unsupervised anomaly-detection techniques trained exclusively on defect-free data. Alternatively, defect data can be synthesized to augment the dataset with defective samples. In the textile industry, expeditious model training is crucial to ensure a smooth production flow. Unfortunately, most unsupervised methods require extensive training time. This paper proposes a novel transfer learning approach designed to achieve training times in seconds while effectively adapting the model to the target domain of fabric anomaly detection. The key contributions of our method include significantly reduced training times, up to 10 times faster than current state-of-the-art methods, and comparable performance in anomaly detection, achieving results on par with state-of-the-art approaches on benchmark datasets (MVTEC Anomaly Detection, TILDA, AITEX and DAGM). Additionally, our approach improves inference times, ensuring expedited and efficient anomaly detection during production. The proposed method offers a practical and efficient solution for real-time industrial quality control.

Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection

The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.

The Role of Artificial Intelligence Technology in Promoting Socio-Economic Development

This paper comprehensively examines the pivotal role of artificial intelligence (AI) technology in promoting socio-economic development. It commences by introducing the rapid advancements and current status of AI technology, which has permeated diverse industries through breakthroughs in big data, cloud computing, and deep learning. The paper highlights AI’s contributions to economic growth by enhancing production efficiency (e.g., 45% increase in efficiency through automation), optimizing resource allocation (25% raw material utilization improvement), and fostering innovative business models such as personalized customization and intelligent supply chain management. AI also fosters the emergence of new industries (intelligent medicine, education, finance) and creates substantial job opportunities directly for data scientists and algorithm engineers, and indirectly through driving related industries. Furthermore, AI facilitates industrial upgrading, transforming manufacturing into smart manufacturing, enhancing service quality in industries like retail, and modernizing agriculture with smart equipment. A case study on Chongqing Soft River Turing AI Technology Co., Ltd. showcases AI’s practical applications and corporate competitiveness enhancement. Overall, the paper underscores AI’s significance in economic growth, job creation, and industry transformation, while acknowledging challenges like data privacy and job displacement, emphasizing the need for strategic policy guidance and regulation.

Tracing fluorescent dissolved organic matter (fDOM) characteristics and water quality parameters: Insights from an urban industrial river to marine zone.

This study aims to identify continuous water quality changes and identify fluorescence properties from urban rivers to marine zones. Various types of natural and anthropogenic sources derived dissolved organic matter (DOM) have been identified in this study. These include soil-derived DOM, plant remnants, and soluble particles produced when organic material partially decomposes and is released by microorganisms, such as bacteria, algae, and plants. DOM was characterized using a three-dimensional excitation-emission matrix (3DEEM), parallel factor analysis (PARAFAC), and water quality parameters from the Buriganga River, Dhaka to Patenga Seaport, Chittagong, along with the Shitalakshya River, a small portion of the Padma River, and the Meghna River. To better understand the data analysis, the study area was divided into three central regions: urban industrial rivers, industrial estuarine rivers, and marine zones. In the urban industrial river, 3DEEM and PARAFAC identified five fluorophores (peaks: A, C, M, T, and Tuv) with five components: detergent-like, fulvic-like, tryptophan or protein-like, fulvic-like (C-type), and protein-like, which might originate from the industrial activities and sewage pollution. In the industrial estuarine river zone, three fluorophores have been identified (peaks: A, C, Tuv) with two known components, namely, fulvic acid (A-type) and fulvic acid (C-type), with an unknown photoproduct at Ex/Em = 295/368 (peak Tuv). Components in the industrial river zone may originate from terrestrial sources, indicating vegetation along the river. In the marine zone, four fluorophores have been identified (peaks: Tuv, A, T, C) with two components, that is, protein- or tryptophan-like and humic acid-like from coral origin. The intensities of both fulvic-like and protein-like substances were high in urban industrial river water owing to industrial activity and sewage pollution. SUVA254 suggests high aromaticity in all three regions, whereas the optical properties suggest that terrestrial and microbial components are present in the urban industrial and estuarine rivers. This further indicates that urban industrial river water quality is highly polluted. The lowest degradation potential index (DPI) in the marine zone might result from the presence of the highest number of dissolved solids in the water, and the highest DPI of industrial estuarine rivers explains the comparatively high presence of terrestrial-derived humic (A)- and humic (C)-like components in the ratio to the unknown photoproduct of mid-wavelength. PRACTITIONER POINTS: This study's uniqueness is a 220-km cruise from an urban river to a coastal seaport to analyze fluorescence properties. The study found that most water parameters were within the DoE standards, except for DO, which was consistently low. 3DEEM-PARAFAC identified five fluorophores linked to detergent, fulvic, and protein-like substances from sewage and industrial sources. Our study concludes that microbial and terrestrial sources dominate dissolved organic matter in urban, estuarine, and marine regions.

Experimental review and accuracy of etchants used for phase analysis of SAF2507 Super Duplex Stainless Steel

The mechanical and corrosion properties of duplex stainless steels (DSS) depend on the distribution of ferrite and austenite as well as the presence of secondary phases. It is therefore necessary to accurately determine both the distribution of the phases and their morphology. The aim of this research is to define a standard method for the determination of the volume fraction of the phases present in a SAF2507 SDSS by analysing images acquired by light microscope. To do this, the material is heat-treated at 850 °C for 5 min, 15 min, 30 min, 1h, 5h to stimulate the formation of the σ-phase. The metallographic etchant capable of maximising phase contrast is then identified. Micrographic analyses are performed using different reagents at different holding times and temperatures, in order to find the best combination. A standard procedure is then defined for the image analysis. The data obtained were are processed to calculate the phase distribution. The results are compared with the quantitative analysis performed using X-ray diffraction as reference.The research evidence that Kalling's reagent is best etchant for fully solubilised materials, as it accurately estimates ferrite and austenite content. On the other hand, Marble's reagent should be preferred for the detection of σ-phase. For comprehensive analysis of all phases, Murakami's reagent is recommended.The approach defined in this paper not only improves the understanding of the microstructure of DSS, but also provides an important tool for industrial quality control and material characterisation.

Hyperspectral guidance for summer tea processing: Enhancing taste and aroma through short‐term cycled heaping

AbstractTo reduce the unpleasant flavor in tea infusion made from summer fresh leaves and lessen tea resource wastage, a short‐term cycled heaping method, addressing the temperature/moisture variations during prolonged heaping, was introduced to produce high‐quality yellow tea with significantly enhanced taste and aroma. Furthermore, a hyperspectral approach was developed to anticipate such improvements. Specifically, short‐term cycled heaping reduced summer tea's astringency and bitterness while increasing sweetness. Using near‐infrared spectroscopy, the multiplicative scatter correction (MSC)–support vector machine (SVM) model predicted tea umami with 66.25% precision and richness with 77.64% accuracy via standard normal variate (SNV)–SVM, based on epigallocatechin (EGC), epicatechin (EC), and gallocatechin gallate (GCG) variations. Aroma enhancement was forecasted by a hyperspectral–electronic nose regression model, achieving over 81.50% prediction accuracy for summer tea aromas in the visible spectrum and surpassing 73.45% in the near‐infrared domain, primarily attributed to pentanal, propanal, toluene, ethyl propionate, and 2,3‐pentanedione variations. Overall, hyperspectral‐guided summer tea processing, particularly for summer‐harvested leaves, shows potential in offering a valuable screening tool, enhancing quality, and efficiency in the tea industry and promoting sustainable development.

Lessons Learned and Outcomes from Risk-Based Modernisation of Post-Mortem Inspection and Disposition Criteria of Beef, Sheep, Goat, and Pig Carcasses in Australia.

The lessons learned from reviewing national risk assessments to modernise the Australian Standard for the post-mortem inspection and disposition judgement of beef, sheep, goat, and pig carcases are discussed. The initial risk profiles identified priorities for quantitative assessments. Broadly, the main difficulty encountered was the paucity of quantified performance for the current inspection. Resolving this involved acquiring gross abnormality data representing regional production/proportional abattoir volumes, the range of gross abnormalities appearing nationally, proportional occurrence at carcase sites, and seasonality to enable the comparison of procedures. The methodologies followed the Codex Alimentarius Commission's risk assessment guidelines and are fully documented in the associated publications. The evidence and discussion are provided for the associated challenges experienced, including preventing contamination, the use of food chain information to support amendment, inspection as a part of industry Quality Assurance programmes, and opportunities to improve inspector training. The criteria considered by the Competent Authority for the determination of the equivalence of alternative post-mortem inspection techniques included comparisons of public health risk, non-detection rates for gross abnormalities, and microbial contamination resulting from inspection activities, as appropriate. Most of the gross abnormalities detected arose from animal health and welfare conditions affecting wholesomeness and did not present as food safety hazards. The non-detection rates between the current and alternative inspection (observation) were negligible. A quantitative risk assessment for Cysticercus bovis was conducted. Carcases with multiple gross abnormalities predominantly reflected historic infections (prior septicaemia), where trimming achieved wholesomeness unless they were cachexic.

Application Progress of Robot Precision Compensation Technology in Aviation Manufacturing

With the increasing demand for product quality and precision in aviation manufacturing industry, the application of robot technology in this field is more and more extensive. However, the accuracy of robot operation has always been a key factor restricting its application. This paper summarizes the research progress of robot precision compensation technology in the field of aviation manufacturing in recent years. This paper first introduces the application status of robot technology in aviation manufacturing, and then analyzes the principles of various precision compensation technologies and their application examples in aviation manufacturing. Through the analysis and summary of the research content, through reasonable precision compensation technology, the operation precision of the robot can be significantly improved, thus meeting the high precision requirements of aviation manufacturing. This paper also analyzes the challenges of robot precision compensation technology in aviation manufacturing field, and looks forward to the application prospect of precision compensation technology in aviation manufacturing field, which provides valuable reference for researchers and enterprises in related fields.

Dynamical Adsorption Behavior Prediction of Dried Tobacco Leaf Heterogeneous Interfaces through Simulation and Image Recognition Techniques.

The process of spraying water and flavorings on dry tobacco is an important factor in the industrial environment and product quality. Tobacco as a complex porous fiber material, the interfacial transfer process of water is complex. In this study, machine learning and image recognition techniques were utilized to quickly obtain the structural parameters of the tobacco surface and construct a cellular structure model of the tobacco surface. In situ observation of the droplet impact spreading process was carried out using a high-speed camera to explore the droplet dissipation dynamics on different surfaces. And the competing processes of droplet wetting and evaporation under the influence of surface microstructure were determined by combining experimental studies and finite element simulation calculations. Based on the characteristics of tobacco pore size distribution, the infiltration under gas-liquid two-phase action was transformed into single-phase flow transfer under capillary force, and the continuous droplet infiltration process was simulated. A parallel artificial membrane permeability measurement method of bionic tobacco waxy layer was constructed for the screening of spray dosing copenetrant. This study brings new insights into the wetting of porous fibrous materials and is important for exploring the wetting process and additive development process influenced by the microstructure.

Hurdle technology using enzymes and essential oil to remove biofilm and increase the effectiveness of this process with the microencapsulation method.

The formation of biofilm in different places and the failure to effectively remove it by the usual disinfection methods is due to its structure and the rich genetic resource available in it to deal with disinfectants. These impenetrable structures and diverse microbial genetics have caused biofilm pollution in different industries like the food industry, the medicine industry, the hospitals and the water distribution system, resulting in pathogenicity and reduction of industrial quality. An efficient way to deal with the resistant population of biofilm-forming microbes is the use of hurdle technology including enzymes and essential oils. Enzymes reduce the resistance of the biofilm structure due to degradation of its extracellular polymer matrix (EPS) by their abilities to break down the organic molecules, and then the essential oils weaken the cells by penetrating the lipid membrane of the cell and destroying its integrity; as a result, the biofilm will be destroyed. The advantage of this hurdle technology is the environmental friendly of both methods, which reduces concerns about the use of chemical disinfection methods, but on the other hand, due to the sensitivity of enzymes as biological agents also the expensiveness of this technique and the considerations of working with essential oils as volatile and unstable liquids should abandon the routine methods of applying this disinfectant to biofilm and go for the microencapsulation method, which as a protective system increases the effectiveness of enzymes and essential oils as antibiofilm agents.

A review of organometallic compounds as versatile sensors in environmental, medical, and industrial applications

Abstract Sensing technology is gaining attention and continuously advancing, making it a recommended element of individualized healthcare management. This is due to the powers exhibited by organometallic compounds, which are further enhanced by the field of bioengineering. Organometallic compounds have a wide range of biological activity and find uses in industrial and material science fields. Their unique ability to specifically target and overcome constraints faced by traditional counterparts makes them potential contenders for sensor technology. These compounds are highly sensitive to changes in their environment, allowing them to be utilized as sensors for detecting various chemicals or conditions. Additionally, the versatility of organometallic compounds enables their integration into different sensor platforms, making them suitable for environmental monitoring, medical diagnostics, and industrial quality control. This article provides a comprehensive summary of recent advancements in the design and synthesis of organometallic compounds, with a specific emphasis on their potential use as sensors. It also discusses the changes made to the structure, the processes used for functionalization, the incorporation of microfluidics, and the resulting impact on the materials’ sensing capabilities. These biologically derived methods align with sustainability goals and enhance the affordability, applicability, and effectiveness of sensing.

Rapid estimation of wood density and total extractives of two important Dalbergia species using near-infrared (NIR) spectroscopy and multivariate analysis

ABSTRACT Wood density and chemical extractives are important parameters influencing timber quality and utility in wood industry. Several wood properties, particularly mechanical parameters and durability, depend respectively on density and extractive contents. This study aimed to apply the near-infrared (NIR) spectroscopy technique for rapid and accurate estimation of density and extractive contents of Dalbergia latifolia and D. sissoo wood species. The objective was to develop a predictive model for properties, addressing the critical gap in efficient and large-scale assessment techniques for wood industries. Density and extractive contents were determined by conventional and wet chemistry methods. Spectral data of woods were subjected to multivariate analysis to improve the accuracy of calibration models and performance was assessed using coefficient of determination (R²) and root mean square error of prediction. The optimised models exhibited good predictive accuracy with high R²of 0.85 and 0.82 for density and extractive contents respectively. Spectral patterns correlating density and extractive variations identified provided valuable insights into the chemical composition and density relationship in Dalbergia species. The study thus highlights the potential of this non-invasive analytical approach in assessing the quality parameters, which would facilitate sustainable utilisation and contribute to the advancement of woodworking industries.

Task-Adaptive Angle Selection for Computed Tomography-Based Defect Detection.

Sparse-angle X-ray Computed Tomography (CT) plays a vital role in industrial quality control but leads to an inherent trade-off between scan time and reconstruction quality. Adaptive angle selection strategies try to improve upon this based on the idea that the geometry of the object under investigation leads to an uneven distribution of the information content over the projection angles. Deep Reinforcement Learning (DRL) has emerged as an effective approach for adaptive angle selection in X-ray CT. While previous studies focused on optimizing generic image quality measures using a fixed number of angles, our work extends them by considering a specific downstream task, namely image-based defect detection, and introducing flexibility in the number of angles used. By leveraging prior knowledge about typical defect characteristics, our task-adaptive angle selection method, adaptable in terms of angle count, enables easy detection of defects in the reconstructed images.

Innovative strategies for protein content determination in dried laver (Porphyra spp.): Evaluation of preprocessing methods and machine learning algorithms through short-wave infrared imaging

In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900–1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp2) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth.