bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Model to Balance Production Workload Distribution in a Trailer Manufacturing Organisation Under Fluctuating Customer Ordering Condition
Authors: van der Walt Gerhard , Makinde Olasumbo , Mpofu Khumbulani 
Published: April 2023

AbstractTrailer manufacturing organisation considered in this study is currently experiencing a high volume of backlog orders due to its poor balancing of production workload distribution during capacity planning and scheduling. This issue has resulted in loss of sales orders experienced by the trailer manufacturing organisation. In light of this, this research study developed a model that could be used to balance production workload distribution that could be used to timeously meet varying customer orders as well as drastically minimise the backlog cost experienced in a trailer manufacturing organisation. To achieve this, on the one hand, a system model of the current production workload distribution used at this trailer manufacturing organisation was developed using AnyLogic software and parametrized using the manufacturing system operation operating conditions obtained via system observation for a period of three (3) months, in order to identify the bottleneck stations and inefficiencies present within this organisation. On the other hand, design of experiments, equipped with feasible workload control strategies were conducted on the model. The result of the simulated model revealed that the usage of an additional two bending machines and two primer paint workers, usage of additional three laser machines and three treatment workers, Heijunka order dispatching principle and Constant Work-In-Process (ConWIP) will increase the service level and mean machine capacity utilisation of the organisation, as well as reduce the backlog cost, opportunity cost and average order lead time.
Resource-Efficient Process Chains for the Production of High-Performance Powertrain Components in the Automotive Industry
Authors: Denkena Berend , Bergmann Benjamin , Wichmann Marcel , Handrup Miriam , Katzsch Daniel , Pillkahn Philipp , Reuter Leon , Schmidt Christopher , Stelljes Frederik 
Published: April 2023

AbstractThis paper focuses on process chains for power train components of passenger cars and heavy duty vehicles. In the project “Powertrain 2025” particular attention is being paid to increase the resource efficiency of the manufacturing process chains and reduce energy demand during service life. In detail cylinder liners are equipped with an adapted geometry and topography which reduces friction losses. Process chains for chassis components are investigated and optimized in order to increase the resource efficiency during manufacturing, service life and maintenance. In addition, process chains for the manufacturing of drive shafts are adjusted. By eliminating hard machining, energy is saved and friction losses are reduced by laser machining of microstructures. Furthermore, micro dimples are applied in vane pumps, which leads to a tribological improvement and thus enhances their friction behaviour. Moreover, a system architecture for process planning is developed and ecologically optimized process parameters are calculated. For a final consideration, a calculation software is developed which enables to calculate the main energy consumption of the manufacturing processes and the carbon footprint for the expected service life. A weight reduction of the powertrain components of 4.5 kg per vehicle and a potential annual energy saving of 13,073 MWh is obtained.
The Costs of More Sustainable Castings Can we Afford the Change?
Authors: Pelchen Lucas , Müller Sebastian 
Published: April 2023

AbstractRising costs for fossil fuels and the need to reduce emissions in the production of castings are subjecting foundries worldwide to increasing pressure to act and innovate. Due to fierce global competition in this sector and high investment costs for alternative technologies, foundries are therefore under high pressure to transform, while at the same time having limited financial resources. This article examines the economic and environmental differences between a conventional industrial foundry process chain, a process chain based on a hydrogen (H2)-fired melting furnace and an all-electric approach using conversion of H2 to electricity within a solid oxide fuel cell. To ensure an unbiased comparison of the process chains introduced, the respective mass efficiencies are first determined using an absorbing Markov chain before calculating the mass-specific costs and emissions of each approach using a literature-based process model. The comparison of the setups shows negligible differences in terms of material loss and cost in the respective best case. However, significantly higher emission minimums are found for both H2 approaches compared to the biogas-based conventional approach, especially for the use of green H2. In summary, no significant economic disadvantages of the H2-based approaches can be identified. Even considering that the economic comparison is biased in favor of biogas due to accounting measures, the environmental difference is comparatively small. The results indicate that the conversion of the foundry industry towards more sustainable H2-based foundry process chains is in principle reasonable as well as affordable and should therefore be achieved in the medium term.
Increasing Energy Efficiency and Flexibility by Forecasting Production Energy Demand Based on Machine Learning
Authors: Trenz André , Hoffmann Christoph , Lange Christopher , Öchsner Richard 
Published: April 2023

AbstractThe ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.
Load Profile Optimization Using Electricity Wholesale Market Price Data for Discrete Manufacturing
Authors: Schwaiger Clemens , Trautner Thomas , Bleicher Friedrich 
Published: April 2023

AbstractSeveral strategies for reducing energy costs can be derived from the energy procurement cost function for Austrian end users of electrical energy. Based on short-term energy procurement on the day-ahead trading floor an optimization problem for cost-optimal scheduling of the load curve of a single plant has been formulated. A preliminary study for an annealing furnace is presented and it is found that the approach can lead to significant savings during periods of volatile prices. Furthermore, the strategy is applicable to any production process that provides sufficient flexibility, and therefore, if the trade-off between peak energy costs is included, can be applied to entire production systems.
Pharma 4.0: Revealing Drivers of the Digital Transformation in the Pharma Sector
Authors: Tetteh Michelle Grace , Jagtap Sandeep , Salonitis Konstantinos 
Published: April 2023

AbstractThe primary goal of this paper was to uncover the drivers of digital transformation (DT) in the pharmaceutical industry. The Covid-19 pandemic and tightening regulatory standards in the global pharma industry necessitated pharma executives need to understand the most critical drivers of digitalizing the sector and charting a new path that improves production processes and streamlines these companies’ overall supply chain. This research aims to identify the drivers for DT in the pharmaceutical industry. The Web of Science Core Collection and Scopus gathered relevant data for the research study. The research employs objective analysis to ensure that more reliable conclusions were drawn following the study. The study also proposed that external and internal factors, such as supply chain difficulties and data silos, impact DT adoption in the pharmaceutical sector.
Key Enablers of Industry 5.0 - Transition from 4.0 to the New Digital and Sustainable System
Authors: Trstenjak Maja , Hegedić Miro , Tošanović Nataša , Opetuk Tihomir , Đukić Goran , Cajner Hrvoje 
Published: April 2023

AbstractAs Industry 4.0 has reached its first decade the new concept has appeared in the literature - Industry 5.0. With an emphasis on human role with the digital system, Industry 5.0 emphasizes the aspects of environmental awareness and sustainability in focus which serves as an upgrade to the previously presented concept. Although Industry 4.0 hasn’t yet been implemented at the expected level in manufacturing companies all around the globe, this paper studies the current trends in Industry 5.0, the transition from 4.0 to 5.0 or directly to 5.0 by current evidence from the literature.Based on the given extensive literature review, this paper provides a list of key enablers of Industry 5.0, possible directions of its development, influential transitional criteria, its advantages, and barriers.
A Hybrid Structural Interaction Matrix Approach to Prioritise Process Wastes Generated in a Manufacturing Organisation
Authors: Makinde Olasumbo , Munyai Thomas , Nesamvuni Edgar 
Published: April 2023

AbstractThe productivity of a manufacturing organisation is limited by myriads of process wastes generated in this organisation. In light of this, the aim of this study is to prioritise various process wastes generated in a manufacturing organisation. In order to achieve this, on the one hand, a Hybrid Structural Interaction Matrix (HSIM), which is premised on the theory of subordination via systems thinking was deployed to carry out the process wastes pairwise ranking and weighting analysis. On the other hand, the Pareto Chart, was thereafter deployed to ascertain the vital few process wastes contributing to productivity loss experienced in a manufacturing organisation. A case study of the process wastes generated in an Electronic-Product Manufacturing organisation was used to validate the process wastes prioritisation model developed in this study. The result of the HSIM prioritisation analysis revealed that the intensity rating scores of the process wastes; overproduction, excess inventory, defect, motion, transport, waiting and over-processing limiting the productivity of an organisation are 7.53, 4.59, 6.06, 1.65, 3.12, 0.18 and 9 respectively. The result of the validation exercise revealed that transport, excess inventory and defects are the core process wastes that limit the productivity of an Electronic-Product Manufacturing organisation considered in this study. With this approach, operations managers of a manufacturing organisation would obviously reduce errors in the rating of process wastes, which is vital towards achieving continuous productivity improvement and sustainable manufacturing.
Identification of Product Specifications Based on KANO Model and Application to Ecodesign
Authors: Mishimar Nozomu 
Published: April 2023

AbstractIt is said that KANO model is one of the key findings in analyzing and categorizing various product qualities. The model says qualities are not only one dimensional in terms of the relations between quality fulfilment and customer satisfaction. If a designer could find a delighter which can increase customer satisfaction greatly without enhancing specification too much may contribute to increase product eco-efficiency, since higher specifications usually lead to larger environmental burden. The study extracts some design specifications of smartphones and carries out an online survey to smartphone users in order to identify the relations between the specification levels and customer satisfaction levels. As the result of the survey, a true exciter could not be found in the case study products. However, regression coefficients estimated by applying conjoint analysis to the selected specifications showed there were specifications which can effectively increase customer satisfaction such as battery capacity or display size, and also there was a specification having little effect in increasing it. The survey results suggested that there are optimum values of product specifications. Identifying the optimum values and avoiding over-specification is significant in finding appropriate design solutions which may decouple the customer satisfaction and the environmental burden increase. The study concluded that the approach is helpful in enhancing product sustainability by Ecodesign.
Salvage Esophagectomy After Definitive Chemoradiotherapy for Squamous Cell Esophageal Cancer: A Propensity Score Matching Study in a High‐Volume Center
Authors: Pan Jie , Liu Zhichao , Yang Yang , Li Bin , Hua Rong , Guo Xufeng , Sun Yifeng , Li Chunguang , Li Zhigang 
Published: April 2023

AbstractBackgroundSalvage esophagectomy, indicated for some patients with locally recurrent/persistent disease after definitive chemoradiotherapy (dCRT), reportedly has high postoperative complications. This study aims to compare the safety and efficacy of dCRT followed by salvage esophagectomy (DCRE) with planned esophagectomy after neoadjuvant chemoradiotherapy (NCRE) in esophageal squamous cell carcinoma (ESCC).MethodsWe retrospectively reviewed all locally advanced ESCC patients treated with DCRE or NCRE at Shanghai Chest Hospital from 2018 to 2021. Propensity score matching (PSM) was used to balance baseline differences. DCRE is defined as esophagectomy for recurrent/persistent disease after dCRT.ResultsA total of 302 patients (41 for DCRE and 261 for NCRE) were included. The median interval of chemoradiotherapy‐to‐surgery was 47d in NCRE, 43d and 440d in DCRE of persistent disease (n = 24) and recurrence (n = 17), respectively. DCRE was observed with advanced ypT stage (63% vs 38%), poorer differentiation (32% vs 15%) and more lymphovascular invasion (29% vs 11%) compared with NCRE (all p < 0.05). The above factors were comparable between the two groups after PSM (all p > 0.05). There were no significant differences before and after PSM in postoperative complications over Clavien‐Dindo grade III (e.g., respiratory failure and anastomotic leak), 30/90‐day postoperative mortality, and survival.ConclusionThrough a standardized surgical procedure in a high‐volume center, DCRE exhibited comparable postoperative complications and prognosis with NCRE.
Enhanced skeletal muscle contractile function and corticospinal excitability precede strength and architectural adaptations during lower-limb resistance training
Authors: Wilson Matthew T. , Hunter Angus M. , Fairweather Malcolm , Kerr Stewart , Hamilton D. Lee , Macgregor Lewis J. 
Published: April 2023

AbstractPurposeEvolving investigative techniques are providing greater understanding about the early neuromuscular responses to resistance training among novice exercisers. The aim of this study was to investigate the time-course of changes in muscle contractile mechanics, architecture, neuromuscular, and strength adaptation during the first 6-weeks of lower-limb resistance training.MethodsForty participants: 22 intervention (10 males/12 females; 173.48 ± 5.20 cm; 74.01 ± 13.13 kg) completed 6-week resistance training, and 18 control (10 males/8 females; 175.52 ± 7.64 cm; 70.92 ± 12.73 kg) performed no resistance training and maintained their habitual activity. Radial muscle displacement (Dm) assessed via tensiomyography, knee extension maximal voluntary contraction (MVC), voluntary activation (VA), corticospinal excitability and inhibition via transcranial magnetic stimulation, motor unit (MU) firing rate, and muscle thickness and pennation angle via ultrasonography were assessed before and after 2, 4, and 6-weeks of dynamic lower-limb resistance training or control.ResultsAfter 2-weeks training, Dm reduced by 19–25% in the intervention group; this was before any changes in neural or morphological measures. After 4-weeks training, MVC increased by 15% along with corticospinal excitability by 16%; however, there was no change in VA, corticospinal inhibition, or MU firing rate. After 6-weeks training there was further MVC increase by 6% along with muscle thickness by 13–16% and pennation angle by 13–14%.ConclusionEnhanced contractile properties and corticospinal excitability occurred before any muscle architecture, neural, and strength adaptation. Later increases in muscular strength can be accounted for by architectural adaptation.
Uncertainty estimation-based adversarial attacks: a viable approach for graph neural networks
Authors: Alarab Ismail , Prakoonwit Simant 
Published: April 2023

AbstractUncertainty estimation has received momentous consideration in applied machine learning to capture model uncertainty. For instance, the Monte-Carlo dropout method (MC-dropout), an approximated Bayesian approach, has gained intensive attention in producing model uncertainty due to its simplicity and efficiency. However, MC-dropout has revealed shortcomings in capturing erroneous predictions lying in the overlapping classes. Such predictions underlie noisy data points that can neither be reduced by more training data nor detected by model uncertainty. On the other hand, Monte-Carlo based on adversarial attacks (MC-AA), an outstanding method, performs perturbations on the inputs using the adversarial attack idea to capture model uncertainty. This method admittedly mitigates the shortcomings of the previous methods by capturing wrong labels in overlapping regions. Motivated by this method that was only validated with neural networks, we sought to apply MC-AA on various graph neural network models to obtain uncertainties using two public real-world graph datasets known as Elliptic and GitHub. First, we perform binary node classifications, then we apply MC-AA and other recent uncertainty estimation methods to capture the uncertainty of the models. Uncertainty evaluation metrics are computed to evaluate and compare the performance of the uncertainty of the model. We highlight the efficacy of MC-AA in capturing uncertainties in graph neural networks wherein MC-AA outperforms other given methods.
Switching network for mixing experts with application to traffic sign recognition
Authors: Ahangi Amir , Möckel Rico 
Published: April 2023

AbstractThe correct and robust recognition of traffic signs is indispensable to self-driving vehicles and driver-assistant systems. In this work, we propose and evaluate two network architectures for multi-expert decision systems that we test on a challenging Traffic Sign Recognition Benchmark dataset. The decision systems implement individual experts in the form of deep convolutional neural networks (CNNs). A gating network CNN acts as final decision unit and learns which individual expert CNNs are likely to contribute to an overall meaningful classification of a traffic sign. The gating network then selects the outputs of those individual expert CNNs to be fused to form the final decision. In this work we study the advantages and challenges of the proposed multi-expert architectures that in comparison to other network architectures allow for parallel training of individual experts with reduced datasets. Under the challenging conditions introduced by the benchmark dataset, the demonstrated multi-expert decision systems achieve a recognition performance that is superior to those of humans: with an accuracy of 99.10%, when training experts with the complete dataset and 98.94%, when individual experts are only trained with 36% of the training samples. Overall, our approach ranked fourth on the list of the applied approaches proposed for the German traffic sign Recognition Benchmark (GTSRB) dataset.
Multifunctional Ti3AlC2-Based Composites via Fused Filament Fabrication and 3D Printing Technology
Authors: Liu Dongyan , Hentschel Lukas , Lin Guoming , Kukla Christian , Schuschnigg Stephan , Ma Na , Wallis Christopher , Momeni Vahid , Kitzmantel Michael , Sui Guoxin 
Published: April 2023

AbstractMAX phase, as a group of layered ternary carbides and nitrides exhibiting combined properties of metallic and ceramic materials, attracts increasing interest because they own exceptionally chemical, physical, electrical, thermal, and mechanical properties. In the present paper, a novel Ti3AlC2-based green part was manufactured by extrusion-based fused filament fabrication (FFF) and 3D printing technologies. The morphology, thermal/electrical conductivity, thermal stability, electromagnetic interference (EMI) shielding effectiveness (SE), and mechanical properties of Ti3AlC2/binder with the volume ratio of 1:1 were investigated. The tensile and compressive strengths and elongation are measured to be 8.29 MPa and 18.20%, 44.90 MPa and 33.76%, respectively. The morphology of the filament reveals that Ti3AlC2 powders are well bonded by the thermoplastic binder. More importantly, the composite shows good thermal and electrical conductivities together with the excellent EMI shielding effectiveness, which is of great potential in the practical applications as conductor, heat dissipating, anti-static, and EMI shielding materials. The successful fabrication of Ti3AlC2-based composites via FFF-based 3D printing technology is beneficial to develop other MAX phase products with complex geometries and additional functionalities.
Lithofacies and Depositional Environments of the Garubathan Alluvial Fan, North Bengal, India
Authors: Sasmal Ramapada , Bhattacharya Soma 
Published: April 2023

Abstract
The sedimentology of the Garubathan alluvial fan of north Bengal foothills is a product of Himalayan neotectonics and monsoon climate. The active neo-tectonics of the Main Central Thrust (MCT), Ramgarh Thrust (RT) and Ghis Transverse Fault (GTF) produce a huge amount of angular clasts in the upper reaches of the river Chel and Mal. During the wet season, either sediment flow by gravity or turbulent stream flow or both, move the clasts to the downhill. The tectonic zone of Main Boundary Thrust (MBT) and Main Frontal Thrust (MFT) are relative passive thrusts than the MCT, RT and GTF in the study area. A very low angle slope is observed in the area of MBT and MFT of the study area. So, the downhill deposition occurred in MBT and MFT zones of the study area. The river Chel and Mal are perinial rivers. During dry periods (pre-monsoon and post-monsoon), in the absence of sediment flow by gravity or turbulent stream flow, the low stream competency deposit the fine sediments. To identify the depositional environments three, seven and six lithofacies varieties were identified in the upper, middle and lower fan surfaces, respectively. The non-cohesive sediment flows by gravity deposited large angular boulders, cobbles and pebbles in the upper fan area. These deposits are either chaotic, unsorted, un-imbricated or graded and imbricated. Deposition of sand is scanty. Both, sediment flow by gravity and fluvial processes deposited angular to round clasts with matrix in the middle fan area. In this area the deposits are reverse graded, reverse to normally graded, laminated and cross-bedded. The high facies diversity of this area indicates highly variable nature of the depositional environments. In the lower fan area, presence of both clast-supported chaotic and matrix-supported laminated or cross-bedded layers suggests a complex nature of the depositional environment of sediment flow by gravity and fluvial processes.