1. Progression of Product Manufacturing Technologies 2. Evaluative Criteria for Product Manufacturing and Optimization Fundamentals 3. Technologies for Product Manufacturing Innovation 4. Involvement of People in Product Manufacturing 5. Product Manufacturing Support Technologies 6. Optimization Technologies for Product Manufacturing 7. Decision-Making Methods 8. Design Optimization for Creativity and Balance

System design optimization for product manufacturing

The modelling and optimization of gear systems is a complex procedure with results dependent on the conceptual formulation of the problem, uncertainty, bias and imprecision in weight assignment. Fu...

https://www.tandfonline.com/doi/pdf/10.1080/23311916.2020.1802816

Gear concept selection procedure using fuzzy QFD, AHP and tacit knowledge

In theprevious study, wedesigned onepersonal rescue winchfor high-rise building rescue. Its key requirement is to be small and light enough to suit users. In addition to using lightweight and reasonable materials as in the proposed winch design, in this study, we proceed to optimize the weight of one two-level gear train, which accounts for a large proportion of weight. The ﬁrst stage is building a weight optimization problem model with seven independent variables, establishing one optimal algorithm, and investigating the variables by Matlab software. The other is replacing the web material of the gears and pinions with Aluminum 6061-T6 and optimizing their hole diameters and hole numbers through using Ansys software. The obtained result shows a signiﬁcant weight reduction. Compared to the original design, the weight reduces by 10.21% and 52.40% after the ﬁrst optimal and last stages, respectively.

pdf/optimizing-the-weight-of-the-two-level-gear-train-in-the-3s89u9wgbr.pdf

Optimizing the weight of the two-level gear train in the personal rescue winch

Abstract The electrification of vehicle powertrains and the expected engineering labor shortage are ongoing key challenges in the gear transmission development. Because traditional methods reach limits, the solution is further automating the design process while enabling flexible and optimal design solutions even with rapidly changing constraints and requirements. We therefore review the current design process, review state-of-the-art methods for automated gear transmission design, and evaluate their potential and the challenges in combination with using machine learning methods. In focus are grammars and graph grammars in particular, which offer an approach to represent and generate the relational structure of transmission topologies or shaft arrangements. Other potential approaches are knowledge-based engineering, which allows to choose various predefined expert design solution and combine them to new designs, and constraint programming for gear transmission generation. Combining these methods with latest advances in reinforcement learning, machine learning for inverse problem-solving, and graph neural networks offers promising capabilities for automatic topology generation and dimensioning of gear transmissions. 

Potentials and challenges in enhancing the gear transmission development with machine learning methods—a review

Multi-Objective Optimization of Planetary Gear Train Using Genetic Algorithm

This article presents a method for multidisciplinary design optimization of a one-stage gear train transmission for an industrial application. The formulation and implementation that enable the int...

Multidisciplinary design optimization of a gear train transmission

In this paper, we present a two level optimization approach in order to enhance the design process of a one-stage speed reducer. The proposed design methodology is performed using genetic algorithms which are judiciously combined with the use of :i) analytical models (1stlevel) and ii) Finite Element Method (FEM)based models ( 2nd level), to evaluate design candidates. Indeed, the use of CAD-CAE tools to develop higher fidelity FEM models allows to re-evaluate the attained first level designs, while accounting for new design parameters and advanced aspects which have been ignored in the first level. In order to minimize the computational burden, a metamodel based optimization technique is adopted at this second level. To illustrate the efficiency of the proposed approach, a case study of a spur gear based reducer is presented where the design of experiments is built using Hypercube Latin Sampling and surrogate models are constructed using Radial Basic Functions.

/pdf/optimum-design-of-a-spur-gear-using-a-two-level-optimization-2lh6sg1hiz.pdf

Optimum design of a spur gear using a two level optimization approach

La transmission de puissance par roue et vis sans fin est très importante sur plusieurs plans. Elle donne un rapport de réduction important, elle assure une sécurité de fonctionnement par les notions: d'irréversibilité, de silence et d'un engrènement doux. L'objectif à travers notre travail est de traité le comportement dynamique des engrenages à roue et vis sans fin. Nous allons développer un modèle à huit degrés de liberté, en introduisant des éléments discrets de masse, d’inertie, de rigidité et d’amortissement. L'obtention des réponses vibratoires à partir de notre modèle revient à la résolution d’un système d’équation par une méthode d'intégration numérique (Runge-Kutta). Un calcul de l'erreur de transmission sur la base des résultats obtenus par la modélisation dynamique nous permettra de mieux comprendre le comportement dynamique des engrenages à roue et vis sans fin tout en varient le matériau de la roue (bronze, aluminium et POM). Le résultat obtenu constitue une base pour réaliser un banc d’essai pour déterminer le comportement dans un stade avancé de nos travaux de recherche.

Modélisation d'un système roue et vis sans fin pour déterminer un processus de conception et de fabrication

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Multidisciplinary design optimization of a gear train transmission

Optimum design of a spur gear using a two level optimization approach

Modélisation d'un système roue et vis sans fin pour déterminer un processus de conception et de fabrication