Hasil untuk "Manufactures"

Steven Wright

R. Akella, P. Kumar

B. Beck

J. Kruth

امیر نظری

پژوهش پیش رو باهدف شناخت ویژگی‌ها و الزامات دو نوع بسته‌بندی مبتنی بر صنایع دستی و وانموده آن متأثر از نظریه چرخش معناشناختی« کلاوس کریپندورف» و از منظر طراحی انسان محور مورد بحث قرار گرفته است. سؤالات اساسی پژوهش بدین شرح است؛ 1- به‌کارگیری صنایع‌دستی(امر واقعی) یا وانموده آن (امر غیرواقعی، شبیه‌سازی) در طراحی بسته‌بندی محصولات، چگونه منجر به دریافت‌های متفاوت می‌گردد؟ 2- صنایع‌دستی به‌مثابه بسته‌بندی محصولات چگونه می‌تواند مفاهیم و الزامات طراحی انسان‌محور را در جهت تمایز با وانموده آن و «معنا بخشی» و «ارزش‌آفرینی» بکار گیرد؟ پژوهش به‌صورت توصیفی- تحلیلی و مبتنی بر نظریات«کلاوس کریپندورف»  است که بر اصل محوریت معنا در مورد مصنوعات اشاره دارد. نتایج پژوهش نشان می‌دهد رواج بسته‌بندی مبتنی­بر صنایع‌دستی و وانموده از سوی طراحان منجر به شکل‌گیری دو نوع مصرف‌کننده یا کاربران خاص و کاربران عامه گردیده است. هر چند کاربران خود را ملزم به این تقسیم‌بندی نمی‌دانند. در مقوله بسته بندی محصولات مبتنی بر انواع صنایع دستی مفاهیمی مانند درک تنوع کاربران اهمیت پیدا می کند اینکه چه محصولی با چه نوعی از صنایع دستی بسته بندی شود مهم خواهد بود و خاص بودگی به واسطه عدم تولید انبوه با تکیه بر زیباشناسی محلی بر جذابیت این نوع بسته بندی نزد کاربران می افزاید. مفاهیمی که در عین حال می تواند ارزش‌افزوده برای محصول بسته‌بندی‌شده با صنایع‌دستی را به دنبال داشته باشد.  در بسته­بندی وانموده امّا ارزش‌آفرینی بر مبنای فروش محصول و نه ارزیابی رضایت کاربران صورت گرفته است. همچنین بسته‌بندی مبتنی بر صنایع‌دستی ویژگی کاربرد مجدد را با خود دارد که در هر بار استفاده معانی جدیدی در موقعیت استفاده آن شکل خواهد گرفت. این معانی می‌تواند شامل تشخص اجتماعی و احساس رضایت از خرید باشد. در موقعیت بسته‌بندی وانموده کاربرد مجدد نه‌تنها مطرح نیست بلکه طراحان باید این ویژگی را به دلایلی ازجمله مباحث زیست‌محیطی، محدود و کنترل کنند.

Yulin An, Xueqi Zhao, Enrique del Castillo

We present a new method for the statistical process control of lattice structures using tools from Topological Data Analysis. Motivated by applications in additive manufacturing, such as aerospace components and biomedical implants, where hollow lattice geometries are critical, the proposed framework is based on monitoring the persistent homology properties of parts. Specifically, we focus on homological features of dimensions zero and one, corresponding to connected components and one-dimensional loops, to characterize and detect changes in the topology of lattice structures. A nonparametric hypothesis testing procedure and a control charting scheme are introduced to monitor these features during production. Furthermore, we conduct extensive run-length analysis via various simulated but real-life lattice-structured parts. Our results demonstrate that persistent homology is well-suited for detecting topological anomalies in complex geometries and offers a robust, intrinsically geometrical alternative to other SPC methods for mesh and point data.

Maria Isabel Álvarez-Castaño, Ye Pu, Christophe Moser

Light-based 3D printing, which relies on photocurable resins, has shown the capability to produce complex geometries with high resolution and fidelity. Tomographic Volumetric Additive Manufacturing (TVAM) employs a digital micromirror device (DMD) to project high-speed sequences of amplitude light patterns into a rotating resin volume, enabling rapid fabrication of 3D structures through photopolymerization. Typically, the light projection efficiency in such binary amplitude modulator-based systems is below a few percent. Recent advancements introduced phase encoding in TVAM using binary amplitude modulators, improving depth control and boosting light projection efficiency to approximately 10%. This was achieved by implementing the Lee hologram technique to encode phase into binary amplitude patterns. In this work, we present the first 3D printing platform utilizing a phase-only light modulator (PLM), based on an array of micro-electro-mechanical pistons. Compared to amplitude encoding, phase encoding with the PLM yields a 70-fold increase in laser power efficiency. By coupling this efficient light engine with a speckle reduction method in holographic volumetric additive manufacturing (HoloVAM), we experimentally demonstrate printing across different scales from hundreds of micrometers to centimeters using only digital control. The PLM opens up new avenues in volumetric AM for holographic techniques using low-cost single-mode UV laser diodes.

Chathurangi Shyalika, Aryaman Sharma, Fadi El Kalach et al.

Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal reasoning required for a unified decision-support solution. This fragmentation limits their trustworthiness and practical utility in high-stakes industrial environments. In this work, we present CausalTrace, a neurosymbolic causal analysis module integrated into the SmartPilot industrial CoPilot. CausalTrace performs data-driven causal analysis enriched by industrial ontologies and knowledge graphs, including advanced functions such as causal discovery, counterfactual reasoning, and root cause analysis (RCA). It supports real-time operator interaction and is designed to complement existing agents by offering transparent, explainable decision support. We conducted a comprehensive evaluation of CausalTrace using multiple causal assessment methods and the C3AN framework (i.e. Custom, Compact, Composite AI with Neurosymbolic Integration), which spans principles of robustness, intelligence, and trustworthiness. In an academic rocket assembly testbed, CausalTrace achieved substantial agreement with domain experts (ROUGE-1: 0.91 in ontology QA) and strong RCA performance (MAP@3: 94%, PR@2: 97%, MRR: 0.92, Jaccard: 0.92). It also attained 4.59/5 in the C3AN evaluation, demonstrating precision and reliability for live deployment.

Charu Tripathi, Manish Arora, Amaresh Chakrabarti

Direct measurement ergonomic assessment is reshaping occupational safety by facilitating highly reliable risk estimation. Industry 5.0, advocating human-centricity, has catalysed increasing adoption of direct measurement tools in manufacturing industries. However, due to technical and feasibility constraints in their practical implementations, especially within non routine manufacturing processes, task based approach to ergonomic assessment is utilized. Despite enabling operationalization of robust ergonomic assessment technologies within complicated industrial processes, task based approach raises several validity concerns. Hence, to ascertain functional utility of the resultant safety interventions, this study evaluates the construct validity of task based ergonomic assessment within non routine work utilizing Multitrait multimethod (MTMM) matrix followed by video-based content analysis. Ergonomic exposure traits were collected for 46 participants through direct measurement and self reported techniques utilizing inertial motion capture and Borg's RPE rating scale respectively. Findings include unsubstantiated convergent validity (low same trait correlations from 0.149 to 0.243) and weak evidence of discriminant validity with statistical significance (p value less than 0.001). The study also identifies three primary factors undermining construct validity through video based content analysis. Findings also elucidate misinterpretation of ergonomic risk and action levels. Therefore, practical implications entail underestimation of actual ergonomic risks when estimated through task based assessment. This highlights the need for enhancement in ergonomic assessment technologies focused on cumulative load analysis compatible within diverse industrial processes.

Wenjing Zhang, Jizhe Cui, Xiaoyang Wang et al.

Alloying by addition of oxygen interstitials during additive manufacturing provides new routes to strengthen and toughen metals and alloys. The underlying mechanisms by which such interstitial atoms lead to enhanced properties remain, however, unclear, not least due a lack of quantitative atomic-scale models linking microstructure to properties. Here using quasi-3D imaging based on multi-slice electron ptychography, we reveal the importance of a new type of interstitial-character lattice defect, namely zero-dimensional stacking domains (ZDSDs), present in high density in AM-processed oxygen-modulated pure titanium. These ZDSDs promote slip diversity, and support intense work hardening, enabling a three-fold enhancement in both strength and ductility in Ti-0.45O compared to conventional pure Ti. The work demonstrates the potential for using interstitial solutes to enhance mechanical properties in a range of critical engineering alloys.

Philipp Alexander Schwarz, Oliver Schacht, Sven Klaassen et al.

RDD (Regression discontinuity design) is a widely used framework for identifying and estimating causal effects at the cutoff of a single running variable. In practice, however, decision-making often involves multiple thresholds and criteria, especially in production systems. Standard MRD (multi-score RDD) methods address this complexity by reducing the problem to a one-dimensional design. This simplification allows existing approaches to be used to identify and estimate causal effects, but it can introduce non-compliance by misclassifying units relative to the original cutoff rules. We develop theoretical tools to detect and reduce "fuzziness" when estimating the cutoff effect for units that comply with individual subrules of a multi-rule system. In particular, we propose a formal definition and categorization of unit behavior types under multi-dimensional cutoff rules, extending standard classifications of compliers, alwaystakers, and nevertakers, and incorporating defiers and indecisive units. We further identify conditions under which cutoff effects for compliers can be estimated in multiple dimensions, and establish when identification remains valid after excluding nevertakers and alwaystakers. In addition, we examine how decomposing complex Boolean cutoff rules (such as AND- and OR-type rules) into simpler components affects the classification of units into behavioral types and improves estimation by making it possible to identify and remove non-compliant units more accurately. We validate our framework using both semi-synthetic simulations calibrated to production data and real-world data from opto-electronic semiconductor manufacturing. The empirical results demonstrate that our approach has practical value in refining production policies and reduces estimation variance. This underscores the usefulness of the MRD framework in manufacturing contexts.

Mohammad Iqbal Rasul Seeam

In the manufacturing industry, it is very important to keep machines and processes running smoothly and without unexpected problems. One of the most common tools used to check if everything is working properly is called Statistical Process Control (SPC). Traditional SPC methods work by checking whether recent measurements are within acceptable limits. However, they only react after a problem has already occurred. This can lead to wasted materials, machine downtime, and increased costs. In this paper, we present a smarter way to use SPC. Instead of just reacting to issues after they happen, our system can predict future problems before they occur. We use a machine learning tool called Facebook Prophet, which is designed to work with time-series data (data that changes over time). Prophet looks at past data and forecasts what the next value will be. Then, we use SPC rules to decide if the predicted value is in a Safe zone (no problem), a Warning zone (needs attention), or a Critical zone (may require shutting down the process). We applied this system to real data from a semiconductor manufacturing company. One of the challenges with this data is that the measurements are not taken at regular time intervals. This makes it harder to predict future values accurately. Despite this, our model was able to make strong predictions and correctly classify the risk level of future measurements. The main benefit of our system is that it gives engineers and technicians a chance to act early - before something goes wrong. This helps reduce unexpected failures and improves the overall stability and reliability of the production process. By combining machine learning with traditional SPC, we make quality control more proactive, accurate, and useful for modern industry.

A. Tamime, R. Robinson

Akane Wakai, Jenniffer Bustillos, Noah Sargent et al.

Controlling microstructure in fusion-based metal additive manufacturing (AM) remains a challenge due to numerous parameters directly impacting solidification conditions. Multiprincipal element alloys (MPEAs) offer a vast compositional design space for microstructural engineering due to their chemical complexity and exceptional properties. Here, we establish a novel alloy design paradigm in MPEAs for AM using the FeMnCoCr system. By exploiting the decreasing phase stability with increasing Mn content, we achieve notable grain refinement and breakdown of columnar grain growth. We combine thermodynamic modeling, operando synchrotron X-ray diffraction, multiscale microstructural characterization, and mechanical testing to gain insight into the solidification physics and its ramifications on the resulting microstructure. This work paves way for tailoring grain sizes through targeted manipulation of phase stability, thereby advancing microstructure control in AM.

R. Silvestro, L. Fitzgerald, R. Johnston et al.

S. Gershwin

T.A.S., B.L.C., A.G. et al.

Forestry companies have invested in genetic improvement to increase wood production in a shorter amount of time. Thus, studies are needed to compare the properties of clonal and seminal wood materials.  The objective of this study was to analyze physical and mechanical properties of Tectona grandis from clonal (C1 and C2) and seminal (S) origin and evaluate the yield and quality of sawn wood subjected to outdoor and oven drying. Genetic material was collected from six, 15-year-old trees. Clone C2 presented the lowest amount of bark, and 51 % heartwood up to half the commercial height, while the heartwood of C1 and S went up to 25 % of the height. The three materials did not differ statistically for maximum angular deviation, pith eccentricity, basic density, Janka hardness, anisotropy, commercial income of sawn wood and the presence of knots. After the drying processes, the bowing and crooking indexes were less than 5 mm.m-1, however, the seminal material showed a higher cracking incidence after outdoor and oven drying. In conclusion, the wood properties of the three materials are similar. In addition, the oven drying process is recommended.

Mohammad Ashraf Khan, Vandana Tewari, Ruchika Agrawal et al.

Introduction: Cataract arises because of aging of the crystalline lens of the eye which prevents clear vision. X-ray Cross-complementing Group 1 (XRCC1) is a Deoxyribonucleic Acid (DNA) repair protein which is involved in Single-Strand Breaks (SSBs) and Base Excision Repair (BER) pathway which is responsible for the efficient repair of DNA damage is mainly responsible for cataract in patients. Aim: To study the prevalence, risk factors and the molecular characterisation with its special association to XRCC1 gene in senile cataract patients. Materials and Methods: This was a cross-sectional study carried out in the Department of Anatomy and Ophthalmology, Rama Medical College Hospital and Research Centre, Kanpur, Uttar Pradesh, India, from April 2021 to April 2022. A total of 500 clinical patients were included in which 250 patients were confirmed as cataract positive patients. Venous blood of 5 mL was collected in Ethylene diamine tetra-acetic acid tubes. The DNA extraction for the detection of XRCC1gene was done using Qiagen DNA extraction kit as per manufactures guidelines, which was further confirmed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR). Results: A total of 500 clinically suspected patients were included in which 250 cases were confirmed as cataract positive patients. The ratio of females was more (n=130, 52%) compared to males (n=120, 48%) with the mean age for females with 57.6% and for males with 61.13%. Hypertension (n=173, 69.2%) was the most common disease associated with the cataract patients. The ratio of males were more (n=91, 75.8%) compared to females (n=82, 63.07%). The mean age of males was 64.40 years and that of females were 62.45 years. The other co-morbidity included diabetes (48.8%), in which males constituted 67 (55.83%) participants compared to the females with 55 (42.3%) participants. The presence of XRCC1 gene was detected in all cataract positive patients, which was also confirmed by RT-PCR. Conclusion: The polymorphisms of DNA repair genes decreased their ability to repair DNA damage, leaving human body a greatly increased susceptibility to cancer or age-related diseases. The association of XRCC1 gene with age-related cataract susceptibility observed in the present study supports the view that XRCC1 gene plays an important role in susceptibility to age-related cataract, so early screening and its molecular profiling will help the clinician in the early diagnosis as well as early treatment.

Sunny Katyara, Mohammad Mujtahid, Court Edmondson

High-fidelity datasets play a pivotal role in imbuing simulators with realism, enabling the benchmarking of various state-of-the-art deep inference models. These models are particularly instrumental in tasks such as semantic segmentation, classification, and localization. This study showcases the efficacy of a customized manufacturing dataset comprising 60 classes in the creation of a high-fidelity digital twin of a robotic manipulation environment. By leveraging the concept of transfer learning, different 6D pose estimation models are trained within the simulated environment using domain randomization and subsequently tested on real-world objects to assess domain adaptation. To ascertain the effectiveness and realism of the created data-set, pose accuracy and mean absolute error (MAE) metrics are reported to quantify the model2real gap.

Hamed Khosravi, Sarah Farhadpour, Manikanta Grandhi et al.

A significant challenge for predictive maintenance in the pulp-and-paper industry is the infrequency of paper breaks during the production process. In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks are relatively rare but have a high economic impact. Utilizing a dataset comprising 18,398 instances derived from a quality assurance protocol, we address the scarcity of break events (124 cases) that pose a challenge for machine learning predictive models. With the help of Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority Oversampling Technique (SMOTE), we implement a novel data augmentation framework. This method ensures that the synthetic data mirrors the distribution of the real operational data but also seeks to enhance the performance metrics of predictive modeling. Before and after the data augmentation, we evaluate three different machine learning algorithms-Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). Utilizing the CTGAN-enhanced dataset, our study achieved significant improvements in predictive maintenance performance metrics. The efficacy of CTGAN in addressing data scarcity was evident, with the models' detection of machine breaks (Class 1) improving by over 30% for Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression. With this methodological advancement, this study contributes to industrial quality control and maintenance scheduling by addressing rare event prediction in manufacturing processes.