scholarly journals Exploiting data in smart factories: real-time state estimation and model improvement in metal forming mass production

2019 ◽  
Vol 13 (5) ◽  
pp. 663-673 ◽  
Author(s):  
Jos Havinga ◽  
Pranab K. Mandal ◽  
Ton van den Boogaard
Keyword(s):  
State Estimation ◽  
Mass Production ◽  
Metal Forming ◽  
Particle Filtering ◽  
Production Systems ◽  
Model Parameters ◽  
Real Process ◽  
Sheet Bending ◽  
Model Improvement ◽  
Pseudo Data

Abstract Modern production systems have numerous sensors that produce large amounts of data. This data can be exploited in many ways, from providing insight into the manufacturing process to facilitating automated decision making. These opportunities are still underexploited in the metal forming industry, due to the complexity of these processes. In this work, a probabilistic framework is proposed for simultaneous model improvement and state estimation in metal forming mass production. Recursive Bayesian estimation is used to simultaneously track the evolution of process state and to estimate the deviation between the physics-based model and the real process. A sheet bending mass production process is used to test the proposed framework. A metamodel of the process is built using proper orthogonal decomposition and radial basis function interpolation. The model is extended with a deviation model in order to account for the difference between model and real process. Particle filtering is used to track the state evolution and to estimate the deviation model parameters simultaneously. The approach is tested and analysed using a large number of simulations, based on pseudo-data obtained from a numerical sheet bending model.

Metal Flow and Die Filling in Coining of Micro Structures with and without Flash

2005 ◽  
Vol 6-8 ◽  
pp. 631-638 ◽  
Author(s):  
M. Thome ◽  
Gerhard Hirt ◽  
B. Rattay
Keyword(s):  
Numerical Simulations ◽  
Sheet Metal ◽  
Metal Forming ◽  
Production Systems ◽  
Metal Flow ◽  
Small Scale ◽  
Die Filling ◽  
Metal Plates ◽  
Micro Structures ◽  
Channel Structures

The continuing miniaturization of production systems and products poses a challenge for metal forming technologies to produce precise small scale products with microscopic geometric details. Thin metal plates with channel structures are considered to be typical examples for microfluidic applications [1,2]. In this study the coining process of sheet metal to produce channel and rib structures is examined in terms of geometrical die parameters and tool design. For this reason extensive experimental series and numerical simulations have been realized and evaluated.

scholarly journals Flexibility of production systems and prepare-finish time

2006 ◽  
pp. 23-30 ◽  
Author(s):  
Milan Vukicevic
Keyword(s):  
Production System ◽  
Mass Production ◽  
Production Systems ◽  
Production Cycle ◽  
Finish Time ◽  
Significant Element ◽  
Wide Range ◽  
Eastern Japan

One of the specificities of the large-serial and mass production is the almost neglected percentage of prepare-finish time in the production cycle. In the conditions of today dominant discontinuous production, it becomes a significant element of the production cycle. The eastern (Japan) doctrine of increasing the flexibility of the production systems, is based inter alia also on the extreme reduction of the prepare-finish time. For this reason, the aim of this study was to identify the types and percentages of individual jobs within the group of prepare-finish jobs. The sample consisted of 3 (three) production systems for the production of joinery, with the discontinuous production system. The research shows that the percentage of time of the jobs installation of work instruments, regulation of processing regime, and removal of work instruments is extremely long and that it ranges between 11.83% and 18.93% of the shift time. The reasons of the high percentage of these jobs are the wide range of products and the absence of the rationalisation of prepare-finish jobs. Within the efforts to minimize the effects of disruption and to increase the flexibility of the production systems, the rationalisation of prepare-finish jobs is the unavoidable condition.

scholarly journals An OGI model for personalized estimation of glucose and insulin concentration in plasma

2021 ◽  
Vol 18 (6) ◽  
pp. 8499-8523
Author(s):  
Weijie Wang ◽  
◽  
Shaoping Wang ◽  
Yixuan Geng ◽  
Yajing Qiao ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Gaussian Noise ◽  
Particle Filtering ◽  
In Silico ◽  
Insulin Concentration ◽  
Model Parameters ◽  
Bayesian Filtering ◽  
Extended Kalman Filtering ◽  
External Disturbances ◽  
Non Gaussian

<abstract><p>Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC $ 9.49\pm3.81 $ mU/L, and PGC $ 0.89\pm0.19 $ mmol/L. For human, the OGI with PFM has the promise to identify disturbances ($ 95.46\%\pm0.65\% $ accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.</p></abstract>

Seru Production System

2021 ◽  
pp. 113-138
Author(s):  
Emre Bilgin Sarı ◽  
Sabri Erdem
Keyword(s):  
Production System ◽  
Mass Production ◽  
Lean Manufacturing ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Production Systems ◽  
Cost Effective ◽  
Opportunity To Respond ◽  
Manufacturing Management ◽  
The Impact

Seru production system is a flexible, cost-effective, workforce competence-oriented manufacturing management system that provides the opportunity to respond quickly to customer demand. As in parallel to technology and physical improvements, customer demands are also effective for development of production systems. The impact of change in demand has been seen on changeover from job shop to mass production, flexible, and lean manufacturing systems. Seru production system is more appropriate for targeting work both cost-effectively like mass production and maximum diversification like job shop production. This chapter clarifies the Seru production system and explain its use and benefits in the clothing industry. In the application, a shirt production is illustrated according to the principles of mass production, lean production, and Seru production. Thus, different types of production systems have been benchmarked. There will be potential study areas for proving the efficiency of Seru soon.

An Unscented Particle Filtering Approach to Decentralized Dynamic State Estimation for DFIG Wind Turbines in Multi-Area Power Systems

2020 ◽  
Vol 35 (4) ◽  
pp. 2670-2682
Author(s):  
Samson Shenglong Yu ◽  
Junhao Guo ◽  
Tat Kei Chau ◽  
Tyrone Fernando ◽  
Herbert Ho-Ching Iu ◽  
...  
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Wind Turbines ◽  
Particle Filtering ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Filtering Approach

A Particle Filter Approach for Identifying Tire Model Parameters From Full-Scale Experimental Tests

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Edoardo Sabbioni ◽  
Ruixin Bao ◽  
Federico Cheli ◽  
Davide Tarsitano
Keyword(s):  
Particle Filtering ◽  
Experimental Tests ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Tire Model ◽  
Passenger Cars ◽  
Sideslip Angle ◽  
Lane Changes ◽  
Filter Approach

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in these type of models lies in tire–road interaction, due to high nonlinearity. Proper estimation of tire model parameters is thus of utter importance to obtain reliable results. This paper presents a methodology aimed at identifying the magic formula-tire (MF-Tire) model coefficients of the tires of an axle only based on measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed, and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.). The proposed methodology is based on particle filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then, PF was applied to experimental data collected using an instrumented passenger car.

A Particle Filter Approach for Identifying Tyre Model Parameters From Full-Scale Experimental Tests

2015 ◽  
Author(s):  
Ruixin Bao ◽  
Francesco Braghin ◽  
Federico Cheli ◽  
Edoardo Sabbioni
Keyword(s):  
Particle Filtering ◽  
Experimental Tests ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Passenger Cars ◽  
Sideslip Angle ◽  
Tyre Model ◽  
Lane Changes ◽  
Filter Approach

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in this type of models lies in the tyre-road interaction, due high nonlinearity. Proper estimation of tyre model parameters is thus of utter importance to obtain reliable results. A methodology aimed at identifying the Magic Formula-Tyre (MF-Tyre) model coefficients of the tyres of an axle based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.) is presented in this paper. The proposed methodology is based on Particle Filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then PF was applied to experimental data collected on an real instrumented passenger-car vehicles.

scholarly journals Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

2019 ◽  
Vol 11 (7) ◽  
pp. 745 ◽  
Author(s):  
Maya Ilieva ◽  
Piotr Polanin ◽  
Andrzej Borkowski ◽  
Piotr Gruchlik ◽  
Kamil Smolak ◽  
...  
Keyword(s):  
Life Cycle ◽  
Prediction Models ◽  
Time Lag ◽  
Model Parameters ◽  
Time Intervals ◽  
Deformation Velocity ◽  
Model Improvement ◽  
High Level ◽  
Deformation Models ◽  
Differential Radar Interferometry

The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe–Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (−0.04 m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (0.2 km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.

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