... assurance and process optimization. In this article, we presented a machine learning-embedded machine vision control scheme that is able to characterize jetting and recognize the printing quality by using only low-resolution observations of the Taylor Cone. An innovative approach was introduced to identify...

... spindle vibration machine learning recurrent neural network gated recurrent unit unbalance bearing fault detection machine tool dynamics modeling and simulation sensing monitoring and diagnostics Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038 IRCPJ...

... 07 07 2023 06 02 2024 06 02 2024 24 04 2024 Graphical Abstract Figure milling dynamics process damping Bayesian learning machine learning machine tool dynamics machining processes Process damping is a phenomenon where a machining process dissipates...

... is empowered by an embedded inductive inference-based machine learning module which learns a welded object's structural properties in the form of geometrical properties. In particular, the system tries to recognize line segments, using a spatial (three-dimensional) visual sensor in order to autonomously...

... the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning...

... components: (1) the ISBJSSP simulator, which manages the dynamic graph generation and machine availability complexity, which are existent in the ISBJSSP setting but absent in the JSSP one, and (2) GNN-RL, a machine learning technique employed to learn the scheduling policy by observing the dynamic graphs...

...] Samie Tootooni , M. , Dsouza , A. , Donovan , R. , Rao , P. K. , Kong , Z. , and Borgesen , P. , 2017 , “ Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches ,” ASME J. Manuf...

...@intel.com 12 05 2023 13 08 2023 14 08 2023 11 09 2023 Bayesian networks multistage manufacturing processes failure mode and effect analysis (FMEA) process monitoring root cause identification machine learning big data modeling and simulation sensing monitoring...

... in this machine learning architecture are inspired by iterative thermal model equations. In other words, each iteration of the thermal model is mapped to a layer of the neural network. The hyper-parameters of the APIDL model are tuned, and its performance is analyzed. The APIDL for 1000 points with 80:20 split...

... 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...

... detection long short-term memory neural networks machine learning soft-computing techniques milling operation ball screw drive advanced materials and processing computer-integrated manufacturing machining processes sensing monitoring and diagnostics Flexibility of cutting tools is a major...