A Sustainable Reverse Engineering Process

Saiga, Katsuhiro; Ullah, Amm Sharif; Kubo, Akihiko; Tashi,

doi:10.1016/j.procir.2021.01.144

Cited by 27 publications

(15 citation statements)

References 24 publications

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“…The simulation studies underpin the suitability of the overall method. Nevertheless, human intelligence is needed to evaluate the simulation results and decide on the next steps that will ultimately affect system efficiency [58].…”

Section: Discussionmentioning

confidence: 99%

Simulation of Energy and Media Demand of Beverage Bottling Plants by Automatic Model Generation

et al. 2021

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Facing environmental challenges, high energy costs and a growing public awareness, the global brewing industry is increasingly publishing ambitious targets toward a more sustainable production. Small and medium-sized enterprises of the brewing and beverage industry cannot ensure energy and media efficiency mainly due to capital and knowledge inadequacy. This article addresses this problem and presents a pragmatic method to determine the energy and media demand. Accordingly, a modeling editor as well as a standardized data structure and automatic simulation parameter determination tools were developed to implement the method. A given production plant can be modeled with adequate details using the presented editor. Based on a configuration file, a holistic simulation model can be generated automatically in a simulation environment. A beverage bottling plant was studied, and the necessary datasets were obtained for implementing the proposed editor and, thereby, the method. It was confirmed that the simulated values of electrical energy and compressed air consumption match the measured empirical data. The measures to increase energy and media efficiency were also found effective. Using the presented method, enterprises of the brewing and beverage industry can easily uncover avenues for potential savings, test the effectiveness of optimization strategies, and substantiate possible investment decisions.

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Section: Discussionmentioning

confidence: 99%

Simulation of Energy and Media Demand of Beverage Bottling Plants by Automatic Model Generation

et al. 2021

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“…Consequently, various operational inputs such as, for example, capital, human resource and know-how-related aspects are not considered in particular [26]. Other impact categories, such as, for example, system and product efficiency [29] are neglected due to the focus on natural resources as well as the system boundary of the manufacturing system.…”

Section: Definition Of Resource Efficiencymentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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“…Injection-molded products often have many defects due to the improper flow of plastic in the mold cavity, such as cavitation, welding line, and even product deformation [7,8] simulates the flow of plastic melt in the mold cavity, predicts and analyzes the filling mode, and predicts the pressure and temperature changes during the filling process, as well as the welding lines and bubbles formed in this process [9,10]. This helps engineers to understand and identify possible defects during pouring before testing the mold, so as to improve the mold before manufacturing and reduce rework waste.…”

Section: Introductionmentioning

confidence: 99%

Inverse Reconstruction of Rearview Mirror Shell and Mold Flow Analysis

Huang¹,

Wu²,

Wang³

2023

J. Phys.: Conf. Ser.

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The three-dimensional model of the automobile rearview mirror is made in three steps. Firstly, a non-contact 3D laser scanner is used to obtain the point cloud of the automobile rearview mirror, and then Image ware is used to denoise the point cloud, construct lines from the points, and obtain the grid model of the lines. Finally, the grid model is poured into the 3D software for surface reconstruction, and the product model is redesigned. After obtaining the three-dimensional model of the automobile rearview mirror, Moldflow is further used to simulate the injection molding process. The runner system of the first mock examination with two cavities is established. The filling parameters of the side gate runner system and the ejector rod latent gate runner system are compared. Combined with the CAE analysis results, it is concluded that the side gate runner system has better filling process parameters.

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A Sustainable Reverse Engineering Process

Cited by 27 publications

References 24 publications

Simulation of Energy and Media Demand of Beverage Bottling Plants by Automatic Model Generation

Simulation of Energy and Media Demand of Beverage Bottling Plants by Automatic Model Generation

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Inverse Reconstruction of Rearview Mirror Shell and Mold Flow Analysis

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