Modern manufacturing enterprises must be agile to cope with sudden demand changes arising from increased global competition, geopolitical factors, and unforeseen circumstances such as the Covid-19 pandemic. Small and Medium-Sized Enterprises (SMEs) in the manufacturing sector lack agility due to lower penetration of Information Technology (IT) and Operational Technology (OT), the inability to employ highly skilled human capital, and the absence of a formal innovation ecosystem for new products or solutions. In recent years, Cloud-based Design and Manufacturing (CBDM) has emerged as an enabler for product realization by integrating various service-based models. However, the existing framework does not support the innovation ecosystem thoroughly from concept to product realization by addressing economic challenges and human skillset requirements in a formal way. The present work considers augmentation of Design-as-a-Service (DaaS) model into the existing CBDM framework for enabling systematic product innovations. The DaaS model proposes to connect skilled human resources with enterprises interested in transforming an idea into a product or solution through the CBDM framework. The model presents an approach for integrating human resources with various CBDM elements and end-users through a service-based model. The challenges associated with the successful implementation of the proposed model are also discussed. It is established that the DaaS has a potential for rapid and economical product discovery and can be readily accessible to SMEs or independent individuals.
The technologies related to manufacturing processes monitoring, optimization, and control are becoming prevalent to achieve autonomous operations in Smart Manufacturing. The present work establishes an edge-level system based on the Long Short-Term Memory (LSTM) model for monitoring significant variations of cutting depths during end milling of near-net-shaped components. The proposed system consists of a trained LSTM model that decodes force data to identify cutting depths and an edge-level interface for displaying abnormal changes to the operator. The LSTM model development requires considerable labeled data consisting of cutting force sequences and corresponding depth classes generated using machining experiments. The present work proposes to develop the LSTM model using synthetic datasets generated using the Mechanistic force model to minimize experimental efforts. The optimum configuration was derived by investigating the effect of network parameters and adaptive learning methods. The performance of an optimal network was substantiated by conducting tests using previously unseen synthetic datasets derived from the Mechanistic model. The optimal network architecture was integrated with a dynamometer and an edge-level system to capture end milling force data and display cutting depth information. A set of end milling experiments are carried over a range of parameters to examine the efficacy of the proposed approach in estimating cutting depth deviations. It has been demonstrated that the approach can be effectively employed as an edge-level system to capture significant cutting depth variations during the end milling and alert machine operators.
A predictive cutting force model is essential for power requirement estimation, cutting tool design, surface error estimation and stability analysis during the end milling operation. Mechanistic model estimates cutting forces by correlating analytically computed chip geometry with lumped coefficients combining tool-work material properties through empirical relationships. Establishing reliable relationships through the statistical curve-fitting is demanding due to the need for several experiments, anomaly or noise in the experimental data, and process disturbances that deteriorate the goodness of fit. Machine learning models can effectively deal with such inherent uncertainties and serve as an alternative to the statistical curve-fitting. The present work proposes to improve the empirical relationship between instantaneous uncut chip thickness and cutting coefficients by employing a deep learning algorithm, namely Adaptive Moment Estimation (ADAM). The ADAM algorithm is augmented with decoupled weight decay and warm restart features for the improved performance. The decoupled weight decay assigns dynamic sensitivity values to the data points for outlier removal resulting in better model generalization, while warm restart allows better guesses through adaptive learning rates. The proposed approach has been implemented as a computational tool to determine improved coefficients values and empirical relationships. The cutting forces predicted using coefficient values determined using statistical curve fitting and ADAM-based machine learning are compared with experimentally measured data over an extensive range of cutting conditions. It is concluded that the augmentation of the ADAM approach enables the Mechanistic force model to effectively capture end milling process physics by estimating better coefficients values resulting in enhanced prediction abilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.