Towards Data Driven Stamping Process Modelling by Optical Inline Monitoring of Cutting Surface Parameters

2021 ◽  
Author(s):  
Maximilian Lorenz ◽  
Matthias Menzl ◽  
Christian Donhauser ◽  
Michael Layh ◽  
Bernd R. Pinzer
Keyword(s):  
Quantitative Description ◽  
Quality Metrics ◽  
Process Modelling ◽  
Data Driven ◽  
Cutting Surface ◽  
Image Capturing ◽  
The Status ◽  
Data Driven Approach ◽  
Cut Surface

Abstract Stamping is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. For this reason, a highly efficient process is crucial. The cutting process is sensitive to a multitude of parameters. A process that is not correctly adjusted is subject to considerable wear and therefore not efficient. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a cutting part, which in turn requires a quantitative description of the quality of a part. A data driven approach allows to meet this challenge and quantify these influences. We developed an optical inline monitoring system, which consists of a image capturing, triangulation and image processing, that is capable of deriving quality metrics from 2D images and triangulation data of the cutting surface, directly inside the machine and without affecting the process. We identify features that can be automatically turned into quality metrics, like fraction of the burnish surface or the cut surface inclination. As an application, we show that the status of tool wear can be inferred by monitoring the burnish surface, with immediate consequences for predictive maintenance. Furthermore, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data driven process modelling and ultimately the implementation of intelligent stamping machines.

scholarly journals Optical inline monitoring of the burnish surface in the punching process

2021 ◽  
Author(s):  
Maximilian Lorenz ◽  
Matthias Menzl ◽  
Christian Donhauser ◽  
Michael Layh ◽  
Bernd R. Pinzer
Keyword(s):  
Quantitative Description ◽  
Quality Parameters ◽  
Data Driven ◽  
Cutting Surface ◽  
The Status ◽  
Punching Process ◽  
Images Processing ◽  
Triangulation Sensor ◽  
Single Part

AbstractPunching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.

Connecting Teacher-Made Assessments to Course Learning Outcomes Through Learning Analytics

2019 ◽  
pp. 101-125
Author(s):  
Julia Chen ◽  
Dennis Foung
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Structural Equation ◽  
Data Driven ◽  
Equation Modeling ◽  
Curriculum Alignment ◽  
Outcome Based Education ◽  
Data Driven Approach ◽  
The Subject

This chapter explores the possibility of adopting a data-driven approach to connecting teacher-made assessments with course learning outcomes. The authors begin by describing several key concepts, such as outcome-based education, curriculum alignment, and teacher-made assessments. Then, the context of the research site and the subject in question are described and the use of structural equation modeling (SEM) in this curriculum alignment study is explained. After that, the results of these SEM analyses are presented, and the various models derived from the analyses are discussed. In particular, the authors highlight how a data-driven curriculum model can benefit from input by curriculum leaders and how SEM provides insights into course development and enhancement. The chapter concludes with recommendations for curriculum leaders and front-line teachers to improve the quality of teacher-made assessments.

scholarly journals Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 100
Author(s):  
Daniele Apiletti ◽  
Eliana Pastor
Keyword(s):  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Rule Mining ◽  
Electronic Noses ◽  
Data Driven Approach ◽  
The Many ◽  
Coffee Machine

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries.

scholarly journals Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Hezhou Qu ◽  
Xiaoyue Xu ◽  
Steven Chien
Keyword(s):  
Joint Probability ◽  
Wait Time ◽  
Service Planning ◽  
Data Driven ◽  
Passenger Demand ◽  
Passenger Travel ◽  
Data Driven Approach ◽  
System Data ◽  
Transit Performance

The service quality of public transit, such as comfort and convenience, is an important factor influencing ridership and fare revenue, which also reflects the passengers’ perception to the transit performance. Passengers are frustrated while waiting to board a crowded train especially during the peak hours, while the fail-to-board (FtB) situation commonly exists. The service performance measures determined by deterministic passenger demand and service frequency cannot reflect the perceived service of passengers. With the automatic fare collection system data provided by Chengdu Metro, we develop a data-driven approach considering the joint probability of spatiotemporal passenger demand at stations based on posted train schedule to approximate passenger travel time (e.g., in-vehicle and out-of-vehicle times). It was found that the estimated wait time can reflect the actual situation as passengers FtB. The proposed modeling approach and analysis results would be useful and beneficial for transit providers to improve system performance and service planning.

Degree Distribution and Quality in Complex Engineered Systems

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Manuel Sosa ◽  
Jürgen Mihm ◽  
Tyson Browning
Keyword(s):  
Complex System ◽  
Degree Distribution ◽  
Data Driven ◽  
Small Subset ◽  
System Quality ◽  
Complex Engineered Systems ◽  
Data Driven Approach ◽  
Engineered Systems ◽  
System Designs

Complex engineered systems tend to have architectures in which a small subset of components exhibits a disproportional number of linkages. Such components are known as hubs. This paper examines the degree distribution of systems to identify the presence of hubs and quantify the fraction of hub components. We examine how the presence and fraction of hubs relate to a system’s quality. We provide empirical evidence that the presence of hubs in a system’s architecture is associated with a low number of defects. Furthermore, we show that complex engineered systems may have an optimal fraction of hub components with respect to system quality. Our results suggest that architects and managers aiming to improve the quality of complex system designs must proactively identify and manage the use of hubs. Our paper provides a data-driven approach for identifying appropriate target levels of hub usage.

Data-Driven Approach for Human Locomotion Generation

2015 ◽  
Vol 15 (02) ◽  
pp. 1540001
Author(s):  
Yejin Kim ◽  
Myunggyu Kim
Keyword(s):  
Motion Capture ◽  
Human Locomotion ◽  
Data Driven ◽  
Motion Path ◽  
High Quality ◽  
Straight Path ◽  
Interactive Editing ◽  
Continuous Output ◽  
Data Driven Approach

This paper introduces a data-driven approach for human locomotion generation that takes as input a set of example locomotion clips and a motion path specified by an animator. Significantly, the approach only requires a single example of straight-path locomotion for each style expressed and can produce a continuous output sequence on an arbitrary path. Our approach considers quantitative and qualitative aspects of motion and suggests several techniques to synthesize a convincing output animation: motion path generation, interactive editing, and physical enhancement for the output animation. Initiated with an example clip, this process produces motion that differs stylistically from any in the example set, yet preserves the high quality of the example motion. As shown in the experimental results, our approach provides efficient locomotion generation by editing motion capture clips, especially for a novice animator, at interactive speed.

scholarly journals An extensible data-driven approach for evaluating the quality of microservice architectures

2019 ◽  
Author(s):  
Mario Cardarelli ◽  
Ludovico Iovino ◽  
Paolo Di Francesco ◽  
Amleto Di Salle ◽  
Ivano Malavolta ◽  
...  
Keyword(s):  
Data Driven ◽  
Data Driven Approach

scholarly journals A Data-Driven Approach for Video Game Playability Analysis Based on Players’ Reviews

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 129
Author(s):  
Xiaozhou Li ◽  
Zheying Zhang ◽  
Kostas Stefanidis
Keyword(s):  
Video Games ◽  
Video Game ◽  
Binary Classification ◽  
Game Studies ◽  
Data Driven ◽  
Software Products ◽  
Data Driven Approach ◽  
Definition Of

Playability is a key concept in game studies defining the overall quality of video games. Although its definition and frameworks are widely studied, methods to analyze and evaluate the playability of video games are still limited. Using heuristics for playability evaluation has long been the mainstream with its usefulness in detecting playability issues during game development well acknowledged. However, such a method falls short in evaluating the overall playability of video games as published software products and understanding the genuine needs of players. Thus, this paper proposes an approach to analyze the playability of video games by mining a large number of players’ opinions from their reviews. Guided by the game-as-system definition of playability, the approach is a data mining pipeline where sentiment analysis, binary classification, multi-label text classification, and topic modeling are sequentially performed. We also conducted a case study on a particular video game product with its 99,993 player reviews on the Steam platform. The results show that such a review-data-driven method can effectively evaluate the perceived quality of video games and enumerate their merits and defects in terms of playability.

scholarly journals Evaluation of Transducer and Signature Selections on the Performance of Artificial Intelligence Machine Tool Wear Prognosis

2021 ◽  
Author(s):  
I-Chun Sun ◽  
Renchi Cheng ◽  
Kuo-Shen Chen
Keyword(s):  
Artificial Intelligence ◽  
Performance Index ◽  
Prediction Accuracy ◽  
Training Data ◽  
Data Driven ◽  
Data Types ◽  
Physical Knowledge ◽  
Status Monitoring ◽  
The Status ◽  
Data Driven Approach

Abstract The qualities of machined products are largely depended on the status of machines in various aspects. Thus, appropriate condition monitoring would be essential for both quality control and longevity assessment. Recently, with the advance in artificial intelligence and computational power, status monitoring and prognosis based on data driven approach becomes more practical. However, unlike machine vision and image processing, where data types are fixed and the performance index has already well defined, sensor selection and index for machine tools are versatile and not standardized at this moment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. This would be a key obstacle for promoting data driven based prognosis in general intelligent manufacturing field. In this work, the status monitoring and prediction of a cutter wear problem is investigated to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures and the key dominated indexes are identified. Finally, three multilayer perception (MLP) artificial neural network models are established. These models trained by different input features are compared to examine the influence of selected sensors and indexes on the prediction accuracy. The results show that with appropriate sensors and signatures, even with less amount of experimental data, the model can indeed achieve a better prediction. Therefore, a proper selection of indexes guided by physical knowledge based experiment or theoretical investigation would be critical.

scholarly journals Data-Driven Prediction of Freezing of Gait Events From Stepping Data

2020 ◽  
Vol 2 ◽  
Author(s):  
Midhun Parakkal Unni ◽  
Prathyush P. Menon ◽  
Lorenzo Livi ◽  
Mark R. Wilson ◽  
William R. Young ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Treatment Options ◽  
Freezing Of Gait ◽  
Significant Negative Correlation ◽  
Data Driven ◽  
Sensory Cues ◽  
Data Driven Approach ◽  
One Step ◽  
Walking Performance

Freezing of gait (FoG) is typically a symptom of advanced Parkinson's disease (PD) that negatively influences the quality of life and is often resistant to pharmacological interventions. Novel treatment options that make use of auditory or sensory cues might be optimized by prediction of freezing events. These predictions might help to trigger external sensory cues—shown to improve walking performance—when behavior is changed in a manner indicative of an impending freeze (i.e., when the user needs it the most), rather than delivering cue information continuously. A data-driven approach is proposed for predicting freezing events using Random Forrest (RF), Neural Network (NN), and Naive Bayes (NB) classifiers. Vertical forces, sampled at 100 Hz from a force platform were collected from 9 PD subjects as they stepped in place until they at least had one freezing episode or for 90 s. The F1 scores of RF/NN/NB algorithms were computed for different IL (input to the machine learning algorithm), and GL (how early the freezing event is predicted). A significant negative correlation between the F1 scores and GL, highlighting the difficulty of early detection is found. The IL that maximized the F1 score is approximately equal to 1.13 s. This indicates that the physiological (and therefore neurological) changes leading to freezing take effect at-least one step before the freezing incident. Our algorithm has the potential to support the development of devices to detect and then potentially prevent freezing events in people with Parkinson's which might occur if left uncorrected.

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