<title>Real-time overlay modeling in a sub-0.50 um production environment using the IVS-100 optical metrology system</title>

Abstract. OPTIMUM is a novel optical coordinate measurement system designed to determine the location of omnidirectional targets within a large volume, the previous version of the system could determine the targets location with an uncertainty of 50 × 10−6 m. This paper describes some of the limitations of the original embodiment and changes being developed to address them. Version 2 of the system aims to solve the limitations of version 1 by integrating photogrammetric processes into the design and control processes of the system, along with redesigning the mechanical and optical relationships. The redesigned instrument will result in the ability to fully automate the initial calibration process and to allow for real-time processing of the measurement algorithms.

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s179396231850040x ◽

2018 ◽

Vol 09 (05) ◽

pp. 1850040 ◽

Cited By ~ 1

Author(s):

Chuyuan Wang ◽

Linxuan Zhang ◽

Chongdang Liu

Keyword(s):

Real Time ◽

Pattern Classification ◽

Feature Selection Method ◽

Classification Algorithm ◽

Adaptive Scheduling ◽

Gradient Boosting ◽

Production Environment ◽

Robotic Cell ◽

Extreme Gradient Boosting ◽

Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

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Real Time Monitoring of the CNC Process in a Production Environment- the Data Collection & Analysis Phase

Procedia CIRP ◽

10.1016/j.procir.2015.12.008 ◽

2016 ◽

Vol 41 ◽

pp. 920-926 ◽

Cited By ~ 13

Author(s):

Jonathan Downey ◽

Denis O'Sullivan ◽

Miroslaw Nejmen ◽

Sebastian Bombinski ◽

Paul O’Leary ◽

...

Keyword(s):

Data Collection ◽

Real Time ◽

Production Environment ◽

Real Time Monitoring ◽

Analysis Phase

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Multi-armed bandits for bid shading in first-price real-time bidding auctions

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202665 ◽

2021 ◽

pp. 1-15

Author(s):

Tuomo Tilli ◽

Leonardo Espinosa-Leal

Keyword(s):

Real Time ◽

Economic Benefit ◽

Real Data ◽

Demand Side ◽

Production Environment ◽

Online Advertisements ◽

Ad Auctions ◽

Real Scenario ◽

Second Price Auctions ◽

Naive Approach

Online advertisements are bought through a mechanism called real-time bidding (RTB). In RTB, the ads are auctioned in real-time on every webpage load. The ad auctions can be of two types: second-price or first-price auctions. In second-price auctions, the bidder with the highest bid wins the auction, but they only pay the second-highest bid. This paper focuses on first-price auctions, where the buyer pays the amount that they bid. This research evaluates how multi-armed bandit strategies optimize the bid size in a commercial demand-side platform (DSP) that buys inventory through ad exchanges. First, we analyze seven multi-armed bandit algorithms on two different offline real datasets gathered from real second-price auctions. Then, we test and compare the performance of three algorithms in a production environment. Our results show that real data from second-price auctions can be used successfully to model first-price auctions. Moreover, we found that the trained multi-armed bandit algorithms reduce the bidding costs considerably compared to the baseline (naïve approach) on average 29%and optimize the whole budget by slightly reducing the win rate (on average 7.7%). Our findings, tested in a real scenario, show a clear and substantial economic benefit for ad buyers using DSPs.

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Real-time spectral interleaved electro-optic dual-comb spectroscopy

10.21203/rs.3.rs-63093/v2 ◽

2020 ◽

Author(s):

Bingxin Xu ◽

Xinyu Fan ◽

Shuai Wang ◽

Zuyuan He

Keyword(s):

High Resolution ◽

Real Time ◽

Figure Of Merit ◽

Frequency Comb ◽

Sampling Rate ◽

Optical Metrology ◽

Broad Bandwidth ◽

Frequency Combs ◽

Electro Optic ◽

Spectral Sampling

Abstract Optical frequency comb with evenly spaced lines over a broad bandwidth has revolutionized the fields of optical metrology and spectroscopy. Here, we propose an electro-optic dual-comb spectroscopy to real-time interleave the spectrum with high resolution, in which two electro-optic frequency combs are seed by swept light source. An interleaved spectrum with a high resolution is real-time recorded by the sweeping probe comb without gap time, which is multi-heterodyne detected by the sweeping local comb. The proposed scheme measures a spectrum spanning 304 GHz in 1.6 ms with a resolution of 1 MHz, and reaches a spectral sampling rate of 1.9*108 points/s under Nyquist-limitation. A reflectance spectrum is measured with a calculated figure-of-merit of 4.2*108, which shows great prospect for fast and high-resolution applications.

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An Agent-Based System for Automated Configuration and Coordination of Robotic Operations in Real Time—A Case Study on a Car Floor Welding Process

Journal of Manufacturing and Materials Processing ◽

10.3390/jmmp4030095 ◽

2020 ◽

Vol 4 (3) ◽

pp. 95

Author(s):

Sotiris Makris ◽

Kosmas Alexopoulos ◽

George Michalos ◽

Andreas Sardelis

Keyword(s):

Real Time ◽

Welding Process ◽

Real Data ◽

Robotic Assembly ◽

Production Environment ◽

Agent Based ◽

Manufacturing Operations ◽

Assembly Cell ◽

Production Automation

This paper investigates the feasibility of using an agent-based framework to configure, control and coordinate dynamic, real-time robotic operations with the use of ontology manufacturing principles. Production automation agents use ontology models that represent the knowledge in a manufacturing environment for control and configuration purposes. The ontological representation of the production environment is discussed. Using this framework, the manufacturing resources are capable of autonomously embedding themselves into the existing manufacturing enterprise with minimal human intervention, while, at the same time, the coordination of manufacturing operations is achieved without extensive human involvement. The specific framework was implemented, tested and validated in a feasibility study upon a laboratory robotic assembly cell with typical industrial components, using real data derived from a car-floor welding process.

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