Quality Evaluation Of Products Research Articles

Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network

The electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure, several different types of data are generated daily, including numerical and categorical data. To evaluate the production quality of electronic control modules, an algorithm based on a Deep Belief Network with Multi-mutation Differential Evolution (MDE-DBN) is designed in this study. First, key indicators are extracted to construct a production quality evaluation index system. A Multi-mutation Differential Evolution algorithm is designed to optimize the initial network weights of the Deep Belief Network (DBN) and integrate the production quality information into the pre-training phase. Subsequently, the preprocessed experimental data are input into the MDE-DBN algorithm to obtain the distributions of excellent, general, and unqualified production statuses, verifying the effectiveness of the algorithm. The experimental results show that the MDE-DBN algorithm has significant advantages in evaluation accuracy when compared with DBNs improved by other intelligent optimization algorithms and machine learning methods.

Identification and crude protein prediction of porcini mushrooms via deep learning-assisted FTIR fingerprinting

Wild edible mushrooms are natural, green, and sustainable foods, and the key to their post-harvest safety and quality control lies in species identification, geographic traceability, and quality assessment. This study analyses the validity of partial least squares discriminant analysis (PLS-DA) model with residual convolutional neural network (ResNet) model based on Fourier transform infrared (FTIR) spectroscopy and two-dimensional correlation spectroscopy (2DCOS) for identifying species and geographic origin of porcini mushrooms. Exploring the feasibility of the partial least squares regression (PLSR) model and long short-term memory network (LSTM) model for predicting the crude protein content of porcini mushrooms. The results show that the ResNet model outperforms PLS-DA with 1.00 and 0.98 prediction results and 1.00 and 0.92 prediction accuracy for new samples, respectively. The LSTM model was more effective than the PLSR model in estimating crude protein content. Among them, LSTM model with first order derivative-multiple scattering correction-Savitzky-Golay (1D-MSC-SG) preprocessing combination was optimal with a prediction set RPD = 2.7, Sig. = 0.418. Besides, the sequential projection algorithm (SPA) feature extraction improves the prediction ability of the LSTM model. Its residual prediction deviation (RPD) was 2.9, Sig. = 0.627. The study provides a new reference for quality evaluation of edible mushrooms and other food products in the market.

Analysis of Antioxidants from Three Parts of Polygonum Chinense Based on Online Extraction High Performance Liquid Chromatography Antioxidant Analysis System.

In the current study, a rapid online extraction combined with high-performance liquid chromatography (HPLC) antioxidant analysis approach was developed to explore the antioxidant ingredients in three different parts of Polygonum Chinense. A total of 22 chromatographic peaks were found, among which 8 components were found for the first time. And among these 22 peaks, 19 of them were demonstrated as antioxidants. Furthermo, all three parts from Polygonum Chinense exhibited antioxidant activity (13.19, 3.89, and 19.85 mg CE/g extract, for the leaf, stem, and flower, respectively) and the antioxidant activity values of the leaf and flower were more than 3 times that of the stem. The leaf, flower, and stem of Polygonum Chinense had 17, 13, and 15 antioxidant components, respectively. Among them, neochlorogenic acid (2) and quercetin-3-O-malonylglucoside (18) were considered as the main antioxidants in the leaf of Polygonum Chinense. In terms of flower, geraniin (10) and quercetin-3-O-malonylglucoside (18) were proved as the major antioxidants. This is the first report on the antioxidant components from different parts of Polygonum Chinense. It provides foundational scientific data for the continued exploration of chemical and pharmacological study of Polygonum Chinense, beneficial for the future product development and quality evaluation improvement of Polygonum Chinense.

Evaluation of the Galileo high-accuracy service SSR product quality and PPP performance

The Galileo navigation satellite system (Galileo) High-Accuracy Service (HAS) is a global navigation satellite system (GNSS) augmentation service proposed by the European Union that providing free of charge orbit, clock and pseudo-range biases corrections to users around the world through E6 signals and by terrestrial means (Internet). However, there has been limited research on the performance of the HAS State Space Representation (SSR) products broadcast by the Galileo Service Centre (GSC). However, there has been limited research on the performance of the HAS SSR products broadcast by the GSC. This study uses BNC software to receive HAS SSR products and compares them with SSR products from three analysis centers with relatively high accuracy: Wuhan University (WHU), the Chinese Academy of Sciences (CAS), and Centre National d’Etudes Spatiales (CNES), and analyzes them in terms of orbit, clock error, and positioning performance. In addition, Galileo HAS as a satellite-based augmentation service, this study chooses BDS PPP B2b, which is also a satellite-based augmentation service, as a comparison to analyze the performance of the HAS augmentation service. The results indicate that the signal-in-space ranging error (SISRE) values for Global Positioning System (GPS) and Galileo corrected by HAS are 7.26 cm and 5.79 cm, respectively. The average convergence times for the GPS + Galileo combination in the static and kinematic modes are 9.4 min and 14.1 min, respectively. Moreover, in kinematic mode, single-system positioning achieved an accuracy of better than 12 cm in the E direction and better than 17 cm in the U direction. In comparison with BeiDou navigation satellite system (BDS) PPP-B2b, the average convergence time of HAS under static mode is 16 min, while that of B2b requires 19 min. Meanwhile, the positioning accuracy of B2b products in the three directions is improved by more than 1 cm compared with that of HAS. The convergence time of HAS in kinematic mode is 12 min shorter than that of B2b.

Investigating temperature fluctuations in the wine and liquor maritime supply chain from South Africa to the United Kingdom: A case study

This study explored temperature fluctuations in dry shipping containers transporting wine and liquor along maritime supply chains. It also examined how these variations affect product quality, and evaluated the effectiveness of thermal foil container liners in mitigating temperature changes. Temperature trials were conducted from the South African loading depot and concluded at the first distribution centre in the United Kingdom. Ambient temperature sensors were placed on cartons of wine and liquor inside the thermal container liner and between the thermal liner and the door of the container. Weather data was also collected during the trials. Temperature profiles showed large fluctuations in temperature inside containers because of day and night cycles while the containers were stacked at the departure and destination ports, posing challenges for supply chain management. Descriptive statistics were used to analyse the temperature profiles, whereas logit analysis was used to determine the impact of the thermal container liner on the temperatures to which the wine and liquor were exposed. Blind tastings were used to evaluate product quality after exposure to temperature fluctuations. Results showed that thermal liners significantly reduced temperature variability, which is critical for maintaining wine and liquor quality. Despite this, blind tastings confirmed that some products were negatively affected by temperature deviations. The study recommends the use of thermal foil liners for maritime transport of wine and liquor to minimise product damage and financial losses. This finding is particularly relevant for exporters aiming to preserve the quality of their wine and liquor throughout long-distance shipping routes.

An integrated strategy for deciphering quality markers of Terminaliae Belliricae Fructus based on a three-dimensional characteristic model

Terminalia bellirica (Gaertn.) Roxb. is an ethnomedicinal plant that has been utilized in Tibetan and traditional Chinese medicine (TCM). Nevertheless, its quality standard officially listed in the Chinese Pharmacopoeia does not include any content determination of the indicator components of Terminaliae Belliricae Fructus, which constrains the effective quality evaluation of medicinal material and related products. In this paper, a three-dimensional “content-pharmacokinetics-pharmacology” network strategy was developed to identify the quality markers (Q-markers) of Terminaliae Belliricae Fructus in terms of “measurability”, “traceability” and “effectiveness”. Chromatographic fingerprint analysis was performed to outline its chemical contour, and identify the differential components of 17 batches of Terminaliae Belliricae Fructus combined with multivariate statistics analysis and UPLC-QTOF-MS analysis. Serum pharmacochemistry analysis was implemented on rats, and 25 prototype components absorbed into the blood were identified. By network pharmacology analysis, a component-disease-target-pathway network was constructed, thus validating the effectiveness of the chemical components of Terminaliae Belliricae Fructus. Afterwards, the above screened candidate components were put into construction of three-dimensional "radar chart". According to the calculated regression area (RA) and coefficient of variation (CV) values, the potential Q-markers was determined, followed by “specificity” evaluation. Ultimately, ellagic acid (EA), chebulagic acid (CHA), gallic acid (GA), chebulinic acid (CA), corilagin (CO) and chebulanin (CH) were specified as the Q-markers of Terminaliae Belliricae Fructus. Owing to high content, good pharmacokinetic property, high pharmacological activities and specificity. The screened Q-markers could offer a scientific foundation for the quality control of Terminaliae Belliricae Fructus, and the proposed strategy is demonstrated to be reliable and feasible for deciphering Q-markers of TCM.

Estimation of Lifetime Performance Index for Generalized Inverse Lindley Distribution Under Adaptive Progressive Type-II Censored Lifetime Test

The lifetime performance index (LPI) is an important metric for evaluating product quality, and research on the statistical inference of the LPI is of great significance. This paper discusses both the classical and Bayesian estimations of the LPI under an adaptive progressive type-II censored lifetime test, assuming that the product’s lifetime follows a generalized inverse Lindley distribution. At first, the maximum likelihood estimator of the LPI is derived, and the Newton–Raphson iterative method is adopted to solve the numerical solution due to the log-likelihood equations having no analytical solutions. If the exact distribution of the LPI is not available, then the asymptotic confidence interval and bootstrap confidence interval of the LPI are constructed. For the Bayesian estimation, the Bayesian estimators of the LPI are derived under three different loss functions. Due to the complex multiple integrals involved in these estimators, the MCMC method is used to draw samples and further construct the HPD credible interval of the LPI. Finally, Monte Carlo simulations are used to observe the performance of these estimators in terms of the average bias and mean squared error, and two practical examples are used to illustrate the application of the proposed estimation method.

Sentiment analysis using graph-based Quickprop method for product quality enhancement

ABSTRACT The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method’s efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media.

Characteristic volatile product analysis of moldy cigar wrapper and filler using gas chromatography-ion mobility spectrometry.

Monitoring the changes of food products with easily applicable technique is important for the quality control of the products. Cigar wrapper and filler easily get moldy due to the existence of the native bacterial in the material and the moisture storage/production condition. Herein, we investigate the volatile compounds produced during the culture of tobacco using chromatography-ion mobility spectrometry (GC-IMS). 114 and 112 volatile compounds are determined with GC-IMS for the cultured cigar wrapper and cigar filler, respectively. Detailed fingerprint analysis and principal component analysis identify a series of compounds that can be used for the evaluation of the degree of mold development on cigar wrapper/filler. The results reported in this work may provide useful information for the quality evaluation of food products.

Towards AI driven surface roughness evaluation in manufacturing: a prospective study

AbstractIn the era of Industry 4.0 and the digital transformation of the manufacturing sector, this article explores the significant potential of machine learning (ML) and deep learning (DL) techniques in evaluating surface roughness—a critical metric of product quality. The integration of edge computing with current computational resources and intelligent sensors has revolutionized the application of AI-driven algorithms in smart manufacturing. It provides real-time data analysis and decision-making capabilities that were unattainable only a decade ago. The research effort intends to improve data-driven decision-making for product quality evaluation by leveraging data integration from manufacturing operations and surface quality measurements. Although a substantial amount of research has been conducted in the related fields, it is still difficult to comprehend and compile all the data on surface roughness research predictive assessment in the form of a process pipeline. This thorough systematic analysis examines scholarly articles published between 2014 and 2024 focusing on surface roughness assessment in precision manufacturing settings. The article is thoroughly classified based on the manufacturing processes, datasets, and ML models used, giving light on the present status, prominent approaches, and existing issues in this sector. A table summarizing the relevant works in this domain providing an easy access to the current trends have been provided. The article not only compiles essential findings and identifies research gaps and similarities in existing methodologies, but it also discusses future research directions and open issues in AI-aided surface roughness evaluation. The critical analysis of the literature reveals a scientific gaps which includes consistent development of benchmarked datasets and making the AI models more explainable using the state-of-the-art explainable AI (XAI) algorithms. The ultimate objective of the article is not only to provide a guide for the practitioners in either of the three domains of AI, manufacturing or surface metrology but also to pave the path for more robust, efficient, and accurate surface quality evaluation processes in production.

Simultaneous determination of picroside I and picroside II in dietary supplements by high performance liquid chromatography with PDA detector

Picroside I and picroside II are two compounds found in the Picrorhiza Kurroa plant, which have anti-cirrhosis, antioxidant, anti-inflammatory, fever, and asthma effects, etc. They are included in the ingredients of many dietary supplement products that help protect the liver and bile, prevent oxidation, etc. Therefore, determining the content of these two active ingredients in dietary supplements is necessary to evaluate product quality. In this study, highperformance liquid chromatography (HPLC-PDA) was used to simultaneously determine picroside I and picroside II in dietary supplements. The analytes in the samples were extracted with methanol for 60 min using a horizontal shaking technique. The extract was then filtered and determined by the HPLC–PDA method with the following conditions: C18 Column (250 mm x 4.6 mm; 5 µm) and a corresponding pre-column (3.9 mm x 5 mm; 5 µm), the mobile phase consists of a solvent mixture of acetonitrile and water according to the gradient program, detection wavelength 265 nm. The method has good specificity, the calibration curve of picroside I and picroside II has a linear coefficient R2 > 0.9999 and the repeatability and recovery meet the AOAC requirements. The method limits of detection (MDL) for these two substances were 4.3 mg/kg and 3.2 mg/kg, respectively. The method was applied to simultaneously determine picroside I and picroside II in 30 different dietary supplement products. The results detected analytes in 3 samples positive with picroside I and picroside II concentrations ranging from 98.8 to 148 mg/kg.

Evaluation of Egg Production, Fertility, Hatchability, Embryonic Mortality and Chick Quality of Different Chickens

The present study was evaluated the egg production performance, fertility, hatchability, embryonic mortality and chick quality from Cosmopolitan (C), Improved Horro (H), ♂Improved Horro*Cosmopolitan ♀ (HC), ♂ Cosmopolitan*Improved Horro ♀ (CH), Indigenous (L), and Koekoek (KK) genotypes. A Completely randomized design was used in the study. A total of 1800 eggs and 300 eggs of each genotype were used for the hatchability and fertility trials. A total of 360 chicks and 60 chicks of each genotype were used for chick quality study. A total of 720 genotypes (5female: 1 male) and 120 from each genotype were used for egg production. Age at first egg and age at peak varied across genotypes. KK had the highest egg production followed by CH, H, CH and C but L had the lowest egg production. KK had the highest weight and feed intake followed by HC, HC, and C, whereas had the lowest followed by H. Feed conversion ratio varied among genotypes. H had the highest egg fertility followed by CH. Conversely, C, HC, L and KK chicken genotypes were comparable and had the least egg fertility. H and CH showed the highest hatchability from set egg, whereas L showed the lowest hatchability from set egg followed by the intermediate KK, HC and C. CH indicated the highest hatchability from fertile egg set, while L confirmed that the lowest hatchability from fertile egg set followed by the intermediate KK, HC and C. The overall embryonic mortality of L was the highest, but CH had significantly lowest overall embryonic mortality followed by KK, HC and C. Chicks hatched from KK had the highest chick weight and chick length, but chicks hatched from L had the least chick weight and chick length. Eggs of L had the highest percentage egg weight yield followed by KK, whereas the CH, C, HC and H had intermediate yield percent during incubation. Conclusively: The genotype differences of hens substantially influenced egg production performance, fertility, embryonic mortality, hatchability and chick quality.

Frequency selection method with support vector machine to minimize the impact of individual differences on the electrical impedance spectra of fruits

Individual differences in electrical impedance spectroscopy (EIS) parameters is a technical bottleneck in fruit quality detection. To achieve non-destructive fruit quality testing and minimize the impact of individual differences, this paper proposes a frequency selection + support vector machine strategy based on the frequency response characteristics of biological tissues and the path of current flow. This study found obvious differences in the EIS parameters of different samples. The measurement stability was affected by the electrode type and frequency. The destructive needle electrode was much less affected by individual differences than the non-destructive patch electrode. The effect of low frequencies was greater than that of high frequencies. The heterogeneity of the fruit peel and pulp and the contact area between the electrode and peel were the main reasons for individual differences. When detecting banana maturity, the highest precision of the models was 98.9% by proposed methods; however, the generalizability of the model and its application for other fruit quality tests still require further verification and optimization. The results promote the practical application of EIS in the field of fruit quality assessment and provide new insights for the quality evaluation of other agricultural products.

Iron saturation and binding capacity of lactoferrin - development and validation of a colorimetric protocol for quality control

Among the numerous biofunctional properties of lactoferrin, its ability to bind iron ions can be considered a core function. The saturation level with ferric iron affects the stability and functionality of the protein. To reliably quantify the iron saturation, an assay based on the color reagent Ferrozine was developed and validated concerning the lower detection (0.023 μg mL−1) and quantification limits (0.069 μg mL−1), as well as precision, recovery and accuracy values.The established assay was used to monitor iron uptake, comparing two commercially available bovine lactoferrin powders. Significant differences between the samples were observed. One sample exhibited nearly ideal binding behavior with a high affinity for ferric iron (saturation > 98 %), while the comparison sample did not exceed saturation values >80 %. This finding underscores the importance of assessing the iron status and binding capacity for the quality evaluation of lactoferrin products.

Sentiment Analysis of Skincare Products Using the Naive Bayes Method

The number of reviews about skincare products can be used as an evaluation of product quality and satisfaction from consumers who have used it as well as considerations for other consumers to try the product. With the number of reviews, it is important to classify reviews into positive, negative, and neutral classes so that the level of product quality from each classification class can be known. The number of reviews causes the review classification process to be unable to be carried out automatically, so sentiment analysis is carried out. To determine the classification of positive sentiment, negative sentiment, and neutral sentiment on the skincare product, the Naive Bayes algorithm method is used. Naive Bayes was chosen because it is easy to implement and has a probability value to classify data. To determine the percentage of results from the specified classification, the Confusion Matrix will be used. The results of the classification process using the Naive Bayes method produce data into 3 types, namely 65 positive classes, 87 neutral classes, and 24 negative classes with an accuracy value of 73%, precision 77%, recall 61%, and f1-score 63%.

Mitigation of polycyclic aromatic hydrocarbons (PAHs) in roasted beef patties by cold plasma treatment and products quality evaluation

The cold plasma (CP) technique was applied to alleviate the contamination of polycyclic aromatic hydrocarbon (PAH) in this investigation. Two different CP treatments methods were implemented in the production of beef patties, to investigate their inhibition and degradation capacity on PAHs. With 5 different cooking oils and fats addition, the inhibition mechanism of in-package cold plasma (ICP) pretreatment was explored from the aspect of raw patties fatty acids composition variation. The results of principal component analysis showed that the first two principal components accounted for more than 80 % of the total variation in the original data, indicating that the content of saturated fatty acids was significantly positively correlated with the formation of PAHs. ICP pretreatment inhibited the formation of PAHs by changing the composition of fatty acids, which showed that the total amount of polyunsaturated fatty acids decreased and the total amount of monounsaturated fatty acids increased. Sensory discrimination tests demonstrated there were discernable differences between 2 CP treated samples and the controls, utilization of the ICP pretreatment in meat products processing was expected to achieve satisfying eating quality. In conclusion, CP treatment degraded PAHs through stepwise ring-opening oxidation in 2 reported pathways, the toxicity of PAHs contaminated products was alleviated after CP treatment.

Quality Optimization and Evaluation of New Cookie Product with Celery Root Powder Addition.

Combined drying, an energy-efficient method that includes osmotic pretreatment in molasses and shortened successive lyophilization, was used to obtain celery root powder and incorporate it in the formulation of cookies, with the aim of obtaining a new product. Wheat flour was substituted with combinedly dehydrated celery root powder at levels from 0 to 30%, and optimization of the amount of wheat flour substitution regarding technological, sensory and nutritive characteristics was performed. The optimal level of 20% substitution was determined using Z-score analysis, from the aspect of the best nutritive improvement and the mildest adverse impact on the technological and sensory quality. In the second research phase, comparison of the cookies with the 20% celery root powder substitution, dehydrated by different methods, indicated that combined dehydration showed upgraded results in terms of the overall quality of the final product, for 28.85 percentile points higher than cookies with lyophilized and for 65.24 percentile points higher than cookies with the addition of convectively dried celery root powder. The cookie containing celery powder previously osmodehydrated in molasses had higher contents of analyzed minerals (1.2-3.3 times), total phenols (10.8%) and antioxidant activities (14% for DPPH and 4% for ABTS) compared to the cookie with lyophilized powder.

Mô hình lựa chọn nhà cung cấp: Ứng dụng phương pháp phân tích thứ bậc mờ

It has been argued that supplier selection is of paramount importance for production companies. In particular, selecting appropriate suppliers may improve production outputs’ quality for firms. Until date, some methods have been developed to select suppliers; yet, some weaknesses still exist. To fill the literature gap, this paper aims to set up a model for judging and selecting potential suppliers from manufactures’ perspectives by the fuzzy Analytic Hierarchy Process (fuzzy AHP). It is posulated that fuzzy AHP is not only applicable for supplier selection, but also helpful in assessing staff’s operational efficiency, evaluating product quality, and appraising the types of technologies to use in the production line.

ENHANCING DIGITAL TRANSACTIONS WITH BLOCKCHAIN TECHNOLOGY: DESCRIPTIVE-ANALYTICAL STUDY

Background: The emergence of Blockchain technology has led to profound transformations in digital transactions, offering a secure and transparent ledger for recording and processing transactions. This innovation holds promise for enhancing security, efficiency, and costeffectiveness across various sectors, including healthcare, education, finance, and real estate. Methods: This descriptive-analytical study explores the potential of Blockchain technology to revolutionise digital transactions. It employs a comprehensive review of existing literature and case studies to analyse the impact and applications of Blockchain across different domains. Results and conclusions: This research underscores the multifaceted benefits of Blockchain technology in streamlining processes, reducing transaction times, minimising fraud, and lowering costs across diverse industries. Blockchain emerges as a pioneering technology, functioning as the largest decentralised open database and facilitating transparent and secure data management. The technology, categorised into public, private, and hybrid types, comprises fundamental elements such as blocks, consensus mechanisms, cryptographic hash functions, and timestamps. With its core functions of transmission, storage, and automation, Blockchain disrupts conventional processes. Smart contracts, supported by external intermediaries like Oracle Programs, access data from external systems, enhancing their functionality and applicability. Moreover, Blockchain enables a departure from routine practices, ensuring robust monitoring of manufacturing processes, evaluating product quality, and verifying compliance with standards prior to market release.

Effect of nanosized carbon nanotubes, Titanium Nitride and cubic Boron Nitride powders on mechanical and thermal properties of SLA 3D printed resin composites

AbstractThis study explores the development of nanocomposites by incorporating multi‐walled carbon nanotubes (MWCNT), titanium nitride (TiN), and cubic boron nitride (c‐BN) nano powders into photo‐polymer epoxy resins for use in Stereolithography (SLA) additive manufacturing. Mechanical and thermal properties were systematically investigated to assess their performance. Initially, MWCNT, TiN, and c‐BN nano powders were homogeneously mixed with the resin to ensure uniformity. The resulting mixtures were then processed using SLA technology to evaluate production quality and material performance. The study revealed significant improvements in mechanical and thermal properties compared to pure resin, aligning with previous research outcomes. Specifically, the incorporation of MWCNT led to a notable enhancement in thermal conductivity, showing an increase of 24.21% as the thermal conductivity coefficient increased from 0.19 to 2.36 W/m·k. On the other hand, c‐BN significantly boosted mechanical strength, resulting in a substantial 20.45% increase as the shore D hardness went from 88.53 to 106.64. These findings highlight the promising potential of nanocomposites across various industries including electronics, aerospace, automotive, construction, medical devices, and manufacturing.Highlights Developed nanocomposites with MWCNT, TiN, and c‐BN in SLA resin. c‐BN increased hardness by 20.45%, proving its effectiveness in composites. MWCNT enhanced thermal conductivity by 24.21%, boosting material performance. Pioneering use of TiN and c‐BN in SLA, addressing gaps in existing research. Applications span electronics, aerospace, automotive and medical devices.