... features and tool surface morphology, life of a typical PCD tool could be divided into three stages viz., abrasion stage (0–23% of total tool life), loading stage (23–77% of total tool life), and chipping stage (77–100% of total tool life). A machine learning model utilizing support vector machine (SVM...

...Alexandra Schueller; Christopher Saldaña Tool condition monitoring (TCM) has become a research area of interest due to its potential to significantly reduce manufacturing costs while increasing process visibility and efficiency. Machine learning (ML) is one analysis technique which has demonstrated...

... training data. For further research on activity recognition in manual manufacturing, we propose the explicit consideration and evaluation of disturbance variables and diversity in data collection for the training of machine learning models. 1 Corresponding author. Email: matthias.doerr@kit.edu...

... the printability of a nickel-rich NiTi powder, where printability refers to the ability to fabricate macro-defect-free parts. Specifically, single track experiments were first conducted to select key processing parameter settings for cubic specimen fabrication. Machine learning classification techniques were...

...Christian Kubik; Dirk Alexander Molitor; Marco Becker; Peter Groche Sensorial acquired process data combined with machine learning (ML) algorithms are fundamental for mastering the challenges of modern production systems, however, their potential is rarely exploited in real-world manufacturing...

...Joseph Cohen; Jun Ni Machine learning and other data-driven methods have developed at a prolific rate for industrial applications due to the advent of industrial big data. However, industrial datasets may not be especially well-suited to supervised learning approaches that require extensive domain...

.... The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large data set of over 530,000 repairs...

... and component optimizations. However, surrogate models using traditional scalar-based machine learning methods (SBMLMs) fall short on accuracy and generalizability. This is because SBMLMs fail to harness the location information available from the simulations. To overcome this shortcoming, the theoretical...

... from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index...

... that the average tool diameter reduces by 32 μm, 67 μm and 108 μm, and the average resultant cutting force were 2.45 N, 4.17 N, and 4.93 N in stage 1, 2, and 3, respectively. To avoid catastrophic breakage of the tool, the tool life stages are predicted from the force data using machine learning models. Among...

.... Machine learning has been emerged as a promising tool for the system classification. Oleaga et al. [ 28 ] developed a machine learning method for chatter detection. Ball and roller bearing fault detections are another important area where machine learning techniques have been used. Hoang and Kang [ 29...

... and FSW; and (9) efforts in advanced sensing, data fusion/sensor fusion, and process control using machine learning/deep learning, model predictive control (MPC), and adaptive control. Email: yuming.zhang@uky.edu Email: yyang@ewi.org Email: zhang.3978@osu.edu Email: sjoona@kaist.ac.kr...

.... Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied...

... 06 2019 18 06 2019 02 08 2019 laser shock peening selective laser melting residual stresses Bayesian inference random field machine learning additive manufacturing laser processes modeling and simulation To perform LSP, the specimen is usually covered with an opaque...

... the use phase. The internet of things (IoT) makes data transfer possible at any time to close the loop for the product lifecycle data and methods like machine learning promote new uses of those data. This paper proposes a methodology to capture the most relevant data on product use and human–product...

... forcing production engineers to push the grinding process to its limits. In such a scenario, process monitoring is absolutely essential not only for safety reasons but also to reduce the number of bad parts and machine downtime. grinding process monitoring machine learning feature selection...