Wenzhen Yang, Yu Liu, Jinghua Chen, Yanqiu Chen and Erwei Shang
This paper endeavors to create a predictive model for the energy consumption associated with the multi-material fused deposition modeling (FDM) printing process.
Abstract
Purpose
This paper endeavors to create a predictive model for the energy consumption associated with the multi-material fused deposition modeling (FDM) printing process.
Design/methodology/approach
An online measurement system for monitoring power and temperature has been integrated into the dual-extruder FDM printer. This system enables a comprehensive study of energy consumption during the dual-material FDM printing process, achieved by breaking down the entire dual-material printing procedure into distinct operational modes. Concurrently, the analysis of the G-code related to the dual-material FDM printing process is carried out.
Findings
This work involves an investigation of the execution instructions that delineate the tooling plan for FDM. We measure and simulate the nozzle temperature distributions with varying filament materials. In our work, we capture intricate details of energy consumption accurately, enabling us to predict fluctuations in power demand across different operational phases of multi-material FDM 3D printing processes.
Originality/value
This work establishes a model for quantifying the energy consumption of the dual-material FDM printing process. This model carries significant implications for enhancing the design of 3D printers and advancing their sustainability in mobile manufacturing endeavors.
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Keywords
Wenzhen Yang, Shuo Shan, Mengting Jin, Yu Liu, Yang Zhang and Dongya Li
This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.
Abstract
Purpose
This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.
Design/methodology/approach
The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.
Findings
Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.
Originality/value
The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.
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Wenzhen Yang, Johan K. Crone, Claus R. Lønkjær, Macarena Mendez Ribo, Shuo Shan, Flavia Dalia Frumosu, Dimitrios Papageorgiou, Yu Liu, Lazaros Nalpantidis and Yang Zhang
This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid…
Abstract
Purpose
This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid with injection molding process.
Design/methodology/approach
In the system, a robot equipped with a camera and a custom-made gripper as well as driven by a visual servoing (VS) controller is expected to perceive objective, handle variation, connect multi-process steps in soft tooling process and realize automation of vat photopolymerization AM. Meanwhile, the vat photopolymerization AM printer is customized in both hardware and software to interact with the robotic system.
Findings
By ArUco marker-based vision-guided robotic system, the printing platform can be manipulated in arbitrary initial position quickly and robustly, which constitutes the first step in exploring automation of vat photopolymerization AM hybrid with soft tooling process.
Originality/value
The vision-guided robotic system monitors and controls vat photopolymerization AM process, which has potential for vat photopolymerization AM hybrid with other mass production methods, for instance, injection molding.
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This study investigates whether Chinese local governments’ environmental attention can mitigate corporate “greenwashing”, focusing on the extent of environmental content in annual…
Abstract
Purpose
This study investigates whether Chinese local governments’ environmental attention can mitigate corporate “greenwashing”, focusing on the extent of environmental content in annual government work reports as indicative of government environmental attention. This study aims to determine whether enterprises respond to changes in local governmental attention by improving the quality of their environmental information disclosures.
Design/methodology/approach
Data from China’s A-share listed companies spanning 2013–2021 were sourced from the CSMAR database and company annual reports. Environmental attention data were manually gathered from local government work reports published on official local government websites by using text analysis methods. These datasets were analyzed empirically to assess the impact of local governments’ environmental attention on corporate greenwashing behavior.
Findings
Results show that increased governmental environmental attention significantly reduces corporate greenwashing behavior by alleviating corporate financing constraints, enhancing independent engagement in environmental initiatives and bolstering stakeholder oversight. Moreover, heterogeneity analysis indicates that the influence of government environmental concerns is pronounced in non-state-owned enterprises, firms with subpar audit quality and those exhibiting myopic management tendencies.
Originality/value
This study enriches the existing literature on the government–business nexus. It also introduces methodological innovations by employing a lexical analysis of environmental themes in local government work reports instead of using typical event study approaches. Furthermore, it uses a mediating effect model to identify the mechanisms through which government environmental attention influences corporate greenwashing, namely, government subsidies, corporate environmental initiatives and external stakeholder oversight.
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Ravikantha Prabhu, Sharun Mendonca, Pavana Kumara Bellairu, Rudolf Charles D’Souza and Thirumaleshwara Bhat
This paper aims to report the effect of titanium oxide (TiO2) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs…
Abstract
Purpose
This paper aims to report the effect of titanium oxide (TiO2) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.
Design/methodology/approach
In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO2 particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.
Findings
The research study reveals that TiO2 filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.
Originality/value
This research paper attempts to include both TiO2 filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO2 micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.