Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning

Hoefer, Michael

doi:10.31274/etd-180810-4948

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…Moreover and using advanced ML techniques, Maghrebi et al (2015), developed a model for predicting the duration of concrete operations, thereby reducing idleness and the cost of equipment in construction sites. Likewise, Hoefer (2017) in his master thesis, developed an automated method that characterizes a conceptual design's geometry and uses that information to help select a suitable manufacturing process. In the same vein and his master thesis as well, Zhu (2019), developed an image processing method for automatic detection of products mounted in a cabinet to produce an error free product detection that will be applied as an automated quality assurance step in the factory.…”

Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Younis

Sundarakani

Alsharairi

2021

JM2

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Purpose The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof. Design/methodology/approach Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM). Findings AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness. Research limitations/implications This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue. Practical implications This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore. Originality/value Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

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Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Younis

Sundarakani

Alsharairi

2021

JM2

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“…Parameter optimization methods present a more flexible way to solve the inverse problems [8,9]. With the rapid development of computer science, machine learning and optimization algorithms have been widely used in a variety of fields such as machine design [10,11], image identification [12][13][14], and data analysis and mining [15,16], providing additional opportunities for parameter optimization. Optimization procedures facilitate simultaneous estimation of hydraulic and retention conductivity functions based on transient flow data [17].…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Hydraulic Parameters during Ponding Infiltration Using a Hybrid Algorithm

Liu

2023

Agronomy

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Accurate inversion of soil hydraulic parameters based on the van Genuchten–Mualem model has received much attention in soil science research. Herein, a hybrid algorithm method using particle swarm optimization and vector-evaluated genetic algorithm was used to invert the parameters θs, α, n, and Ks, with the objective functions of infiltration rate, cumulative infiltration, and soil water content. Then, numerical experiments were conducted on four typical soils at three initial water content levels (20, 40, and 60% effective saturation) to verify the accuracy of the inverse method. The results showed that the inversed soil water retention and conductivity curves were approximately the same as the real curves, with the root mean square errors of 0.00101–0.00192 cm3·cm−3, 0.00800–0.02519 cm3·cm−3, respectively, and both the Nash-Sutcliffe coefficients were approximately 1.0. Additionally, laboratory experiments were also performed to compare with the inversed parameters for verification, within small root mean squared errors and approximately 1.0 Nash–Sutcliffe coefficients. Furthermore, the method can also achieve acceptably accurate parameter inversion even with substantial measurement errors included in the cumulative infiltration, initial water content, and final water content. Thus, the method is effective and robust and found to be practical in field experiments.

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“…The minimum tool diameter for each facet ( ) is defined as the diameter of the smallest tool that can machine the entirety of a facet (M. J. D. Hoefer, 2017). These values were then used to determine the diameter of the smallest tool necessary to fully machine the surface of a design ( ):…”

Section: Methodsmentioning

confidence: 99%

Evaluating manufacturing energy consumption for a part using geometry

Guzmán¹,

Alicia²

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Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning

Cited by 3 publications

References 33 publications

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Estimating Soil Hydraulic Parameters during Ponding Infiltration Using a Hybrid Algorithm

Evaluating manufacturing energy consumption for a part using geometry

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