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  • 1.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Altarabichi, Mohammed Ghaith
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ginsberg, Fredrik
    Mälardalens högskola, Västerås, Sweden.
    Glaes, Robert
    Mälardalens högskola, Västerås, Sweden.
    Östgren, Magnus
    Mälardalens högskola, Västerås, Sweden.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sorensen, Magnus
    Mälardalens högskola, Västerås, Sweden.
    Avision-based indoor navigation system for individuals with visual impairment2019Inngår i: International Journal of Artificial Intelligence, ISSN 0974-0635, E-ISSN 0974-0635, Vol. 17, nr 2, s. 188-201Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.

  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Quality index analysis on camera- A sed R-eak identification considering movements and light illumination2018Inngår i: Studies in Health Technology and Informatics, vol 249, IOS Press , 2018, s. 84-92Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: Normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men. 

  • 3.
    Andersson, Alf
    et al.
    Volvo Car Corporation Manufacturing Engineering.
    Erdem, Ilker
    Chalmers University of Technology.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Kihlman, Henrik
    ProdTex.
    Bengtsson, Kristofer
    Chalmers University of Technology.
    Falkman, Petter
    Chalmers University of Technology.
    Torstensson, Johan
    Fraunhofer-Chalmers Centre.
    Carlsson, Johan
    Fraunhofer-Chalmers Centre.
    Scheffler, Michael
    Carl Zeiss Automated Inspection GmbH & Co.
    Bauer, Stefan
    Carl Zeiss Automated Inspection GmbH & Co.
    Paul, Joachim
    Carl Zeiss Automated Inspection GmbH & Co.
    Lindkvist, Lars
    Chalmers University of Technology.
    Nyqvist, Per
    Chalmers University of Technology.
    Inline Process Control – a concept study of efficient in-line process control and process adjustment with respect to product geometry2016Inngår i: Swedish Production Symposium 2016 SPS 2016, Lund, Sweden, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    All manufacturing processes have variation which may violate the fulfillment of assembly, functional, geometrical or esthetical requirements and difficulties to reach desired form in all areas. The cost for geometry defects rises downstream in the process chain. Therefore, it is vital to discover these defects as soon as they appear. Then adjustments can be done in the process without losing products or time. In order to find a solution for this, a project with the overall scope “development of an intelligent process control system” has been initiated. This project consists of five different work packages: Inline measurement, Process Evaluation, Corrective actions, Flexible tooling and demonstrator cell. These work packages address different areas which are necessary to fulfill the overall scope of the project. The system shall both be able to detect geometrical defects, propose adjustments and adjust simple process parameters. The results are demonstrated in a demo cell located at Chalmers University of Technology. In the demonstrator all the different areas have been verified in an industrial case study – assembly of GOR Volvo S80. Efficient offline programming for robot based measurement, efficient process evaluation based on case base reasoning (CBR) methodology, flexible fixtures and process adjustments based on corrective actions regarding in going part positioning.

  • 4.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    An Intelligent Non-Contact based Approach for Monitoring Driver’s Cognitive Load2018Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The modern cars have been equipped with advanced technical features to help make driving faster, safer and comfortable. However, to enhance transport security i.e. to avoid unexpected traffic accidents it is necessary to consider a vehicle driver as a part of the environment and need to monitor driver’s health and mental state. Driving behavior-based and physiological parameters-based approaches are the two commonly used approaches to monitor driver’s health and mental state. Previously, physiological parameters-based approaches using sensors are often attached to the human body. Although these sensors attached with body provide excellent signals in lab conditions it can often be troublesome and inconvenient in driving situations.  So, physiological parameters extraction based on video images offers a new paradigm for driver’s health and mental state monitoring. This thesis report presents an intelligent non-contact-based approach to monitor driver’s cognitive load based on physiological parameters and vehicular parameters. Here, camera sensor has been used as a non-contact and pervasive methods for measuring physiological parameters.

    The contribution of this thesis is in three folds: 1) Implementation of a camera-based method to extract physiological parameters e.g., heart rate (HR), heart rate variability (HRV), inter-bit-interval (IBI), oxygen saturation (SpO2) and respiration rate (RR) considering several challenging conditions e.g. illumination, motion, vibration and movement. 2) Vehicular parameters e.g. lateral speed, steering wheel angle, steering wheel reversal rate, steering wheel torque, yaw rate, lanex, and lateral position extraction from a driving simulator. 3) Investigation of three machine learning algorithms i.e. Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to classify driver’s cognitive load. Here, according to the results, considering the challenging conditions, the highest correlation coefficient achieved for both HR and SpO2 is 0.96. Again, the Bland Altman plots shows 95% agreement between camera and the reference sensor. For IBI, the quality index (QI) is achieved 97.5% considering 100 ms R-peak error. For cognitive load classification, two separate studies are conducted, study1 with 1-back task and study2 with 2-back task and both time domain and frequency domain features are extracted from the facial videos. Finally, the achieved average accuracy for the classification of cognitive load is 91% for study1 and 83% for study2. In future, the proposed approach should be evaluated in real-road driving environment considering other complex challenging situations such as high temperature, complete dark/bright environment, unusual movements, facial occlusion by hands, sunglasses, scarf, beard etc.

  • 5.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Deep Learning based Person Identification using Facial Images2018Inngår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 111-115Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Person identification is an important task for many applications for example in security. A person can be identified using finger print, vocal sound, facial image or even by DNA test. However, Person identification using facial images is one of the most popular technique which is non-contact and easy to implement and a research hotspot in the field of pattern recognition and machine vision. n this paper, a deep learning based Person identification system is proposed using facial images which shows higher accuracy than another traditional machine learning, i.e. Support Vector Machine.

  • 6.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Non-contact heart rate monitoring using lab color space2016Inngår i: Studies in Health Technology and Informatics, 2016, Vol. 224, s. 46-53Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Research progressing during the last decade focuses more on non-contact based systems to monitor Heart Rate (HR) which are simple, low-cost and comfortable to use. Most of the non-contact based systems are using RGB videos which is suitable for lab environment. However, it needs to progress considerably before they can be applied in real life applications. As luminance (light) has significance contribution on RGB videos HR monitoring using RGB videos are not efficient enough in real life applications in outdoor environment. This paper presents a HR monitoring method using Lab color facial video captured by a webcam of a laptop computer. Lab color space is device independent and HR can be extracted through facial skin color variation caused by blood circulation considering variable environmental light. Here, three different signal processing methods i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and blood volume pulse (BVP) has been extracted from the facial regions. In this study, HR is subsequently quantified and compare with a reference measurement. The result shows that high degrees of accuracy have been achieved compared to the reference measurements. Thus, this technology has significant potential for advancing personal health care, telemedicine and many real life applications such as driver monitoring.

  • 7.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Non-Contact Physiological Parameters Extraction Using Camera2016Inngår i: Internet of Things. IoT Infrastructures: Second International Summit, IoT 360° 2015 Rome, Italy, October 27–29, 2015. Revised Selected Papers, Part I, 2016, Vol. 169, s. 448-453Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Physiological parameters such as Heart Rate (HR), Beat-to-Beat Interval (IBI) and Respiration Rate (RR) are vital indicators of people’s physiological state and important to monitor. However, most of the measurements methods are connection based, i.e. sensors are connected to the body which is often complicated and requires personal assistance. This paper proposed a simple, low-cost and non-contact approach for measuring multiple physiological parameters using a web camera in real time. Here, the heart rate and respiration rate are obtained through facial skin colour variation caused by body blood circulation. Three different signal processing methods such as Fast Fourier Transform (FFT), independent component analysis (ICA) and Principal component analysis (PCA) have been applied on the colour channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. HR, IBI and RR are subsequently quantified and compared to corresponding reference measurements. High degrees of agreement are achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine. 

  • 8.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Vision-Based Remote Heart Rate Variability Monitoring using Camera2018Inngår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 10-18Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Heart Rate Variability (HRV) is one of the important physiological parameter which is used to early detect many fatal disease. In this paper a non-contact remote Heart Rate Variability (HRV) monitoring system is developed using the facial video based on color variation of facial skin caused by cardiac pulse. The lab color space of the facial video is used to extract color values of skin and signal processing algorithms i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA), Principle Component Analysis (PCA) are applied to monitor HRV. First, R peak is detected from the color variation of skin and then Inter-Beat-Interval (IBI) is calculated for every consecutive R-R peak. HRV features are then calculated based on IBI both in time and frequency domain. MySQL and PHP programming language is used to store, monitor and display HRV parameters remotely. In this study, HRV is quantified and compared with a reference measurement where a high degree of similarities is achieved. This technology has significant potential for advancing personal health care especially for telemedicine.

  • 9.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Real Time Heart Rate Monitoring from Facial RGB Color Video using Webcam2016Inngår i: The 29th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2016, Malmö, Sweden, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Heart Rate (HR) is one of the most important Physiological parameter and a vital indicator of people’s physiological state and is therefore important to monitor. Monitoring of HR often involves high costs and complex application of sensors and sensor systems. Research progressing during last decade focuses more on noncontact based systems which are simple, low-cost and comfortable to use. Still most of the noncontact based systems are fit for lab environments in offline situation but needs to progress considerably before they can be applied in real time applications. This paper presents a real time HR monitoring method using a webcam of a laptop computer. The heart rate is obtained through facial skin color variation caused by blood circulation. Three different signal processing methods such as Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. HR is subsequently quantified and compared to corresponding reference measurements. The obtained results show that there is a high degrees of agreement between the proposed experiments and reference measurements. This technology has significant potential for advancing personal health care and telemedicine. Further improvements of the proposed algorithm considering environmental illumination and movement can be very useful in many real time applications such as driver monitoring.

  • 10.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Hök, Bertil
    Hök Instrument AB.
    A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a case-based classification system for alcohol detection using physiological parameters. Here, four physiological parameters e.g. Heart Rate Variability (HRV), Respiration Rate (RR), Finger Temperature (FT), and Skin Conductance (SC) are used in a Case-based reasoning (CBR) system to detect alcoholic state. In this study, the participants are classified into two groups as drunk or sober. The experimental work shows that using the CBR classification approach the obtained accuracy for individual physiological parameters e.g., HRV is 85%, RR is 81%, FT is 95% and SC is 86%. On the other hand, the achieved accuracy is 88% while combining the four parameters i.e., HRV, RR, FT and SC using the CBR system. So, the evaluation illustrates that the CBR system based on physiological sensor signal can classify alcohol state accurately when a person is under influence of at least 0.2 g/l of alcohol.

  • 11.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Intelligent Driver Monitoring Based on Physiological Sensor Signals: Application Using Camera2015Inngår i: IEEE 18th International Conference on Intelligent Transportation Systems ITSC2015, Canary Islands, Spain, 2015, s. 2637-2642Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recently, there has been increasing interest in low-cost, non-contact and pervasive methods for monitoring physiological information for the drivers. For the intelligent driver monitoring system there has been so many approaches like facial expression based method, driving behavior based method and physiological parameters based method. Physiological parameters such as, heart rate (HR), heart rate variability (HRV), respiration rate (RR) etc. are mainly used to monitor physical and mental state. Also, in recent decades, there has been increasing interest in low-cost, non-contact and pervasive methods for measuring physiological information. Monitoring physiological parameters based on camera images is such kind of expected methods that could offer a new paradigm for driver’s health monitoring. In this paper, we review the latest developments in using camera images for non-contact physiological parameters that provides a resource for researchers and developers working in the area.

  • 12.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Driver monitoring in the context of autonomous vehicle2015Inngår i: Frontiers in Artificial Intelligence and Applications, Amsterdam, 2015, Vol. 278, s. 108-117Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Today research is going on within different essential functions need to bring automatic vehicles to the roads. However, there will be manual driven vehicles for many years before it is fully automated vehicles on roads. In complex situations, automated vehicles will need human assistance for long time. So, for road safety driver monitoring is even more important in the context of autonomous vehicle to keep the driver alert and awake. But, limited effort has been done in total integration between automatic vehicle and human driver. Therefore, human drivers need to be monitored and able to take over control within short notice. This papers provides an overview on autonomous vehicles and un-obstructive driver monitoring approaches that can be implemented in future autonomous vehicles to monitor driver e.g., to diagnose and predict stress, fatigue etc. in semi-automated vehicles. 

  • 13.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ins and Outs of Big Data: A Review2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Today with the fast development of digital technologies and advance communications a gigantic amount of data sets with massive and complex structures called ‘Big data’ is being produced everyday enormously and exponentially. Again, the arrival of social media, advent of smart homes, offices and hospitals are connected as Internet of Things (IoT), this influence also a lot to Big data. According to the study, Big data presents data sets with large magnitude including structured, semi-structured or unstructured data. The study also presents the new technologies for data analyzing, collecting, fast searching, proper sharing, exact storing, speedy transferring, hidden pattern visualization and violations of privacy etc. This paper presents an overview of ins and outs of Big Data where the content, scope, samples, methods, advantages, challenges and privacy of Big data have been discussed. The goal of this article is to provide big data knowledge to the research community for the sake of its many real life applications such as traffic management, driver monitoring, health care in hospitals, meteorology and so on.

  • 14.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Iyer, Shankar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Meusburger, Caroline
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Dobrovoljski, Kolja
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Stoycheva, Mihaela
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Turkulov, Vukan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    SmartMirror: An Embedded Non-contact System for Health Monitoring at Home2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The ‘Smart Mirror’ project introduces non-contact based technological innovations at our homes where its usage can be as ubiquitous as ‘looking at a mirror’ while providing critical actionable insights thereby leading to improved care and outcomes. The key objectives is to detect key physiological markers like Heart Rate (HR), Respiration Rate (RR), Inter-beat-interval (IBI) and Blood Pressure (BP) and also drowsiness using the video input of the individual standing in front of the mirror and display the results in real-time. A satisfactory level of accuracy has been attained with respect to the reference sensors signal.

  • 15.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sandberg, Johan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Eriksson, Lennart
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Heidari, Mohammad
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Arwald, Jan
    Exformation AB, Lidingö, Sweden.
    Eriksson, Peter
    Exformation AB, Lidingö, Sweden.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Falling Angel - a Wrist Worn Fall Detection System Using K-NN Algorithm2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A wrist worn fall detection system has been developed where the accelerometer data from an angel sensor is analyzed by a two-layered algorithm in an android phone. Here, the first layer uses a threshold to find potential falls and if the thresholds are met, then in the second layer a machine learning i.e., k-Nearest Neighbor (k-NN) algorithm analyses the data to differentiate it from Activities of Daily Living (ADL) in order to filter out false positives. The final result of this project using the k-NN algorithm provides a classification sensitivity of 96.4%. Here, the acquired sensitivity is 88.1% for the fall detection and the specificity for ADL is 98.1%.

  • 16.
    Tomasic, Ivan
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Erdem, Ilker
    Chalmers University of Technology, Sweden.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Andersson, Alf
    Volvo Car Corporation Manufacturing Engineering, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Process2016Inngår i: Swedish Production Symposium 2016 SPS 2016, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The assembly quality is affected by various factors within which fixture variations are the most important. For that reason significant research on fixture variations has already been done. In this work we propose a linear mixed models (LMMs) application for the purpose of analyzing sources of variation in the fixture Objective: To estimate the strength of influences of different sources of variation on the control and assembly fixtures. The variables considered are: time, operator, default pin positions, shifts from the default pin positions . Methods: The data was collected through assembly and measurement for repeatability and experimental corrective actions. We use LMMs to model the relation between features measured on the assembled parts and the input variables of interest. The LMMs allow taking into account the correlation of observations contained in the dataset. We also use graphical data presentation methods to explore the data. Results: The expected results are the strengths of influences of the individual variables considered, and the pairwise interactions of between the variables, on the assembled parts variations.

  • 17.
    Tomasic, Ivan
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Erdem, Ilker
    Chalmers University of Technology, Sweden.
    Andersson, Alf
    Volvo Car Corporation Manufacturing Engineering, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Input-output Mapping and Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Processes2016Inngår i: Swedish Production Symposium 2016 SPS 2016, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The assembly quality is affected by various factors within which fixture variations are the most important. For that reason an extensive research on fixture variations has already been done. In this work we propose a linear models (LMs) application for the purpose of analyzing sources of variation in the fixture as well as establishing a model between positions of ingoing parts and measured geometrical characteristics of the assemblies. Objectives: (1) To estimate the strengths of different sources of variation on the assembled parts. (2) Estimate a regression model between the positions of ingoing parts as inputs (that are defined by positions of pins holding them), and measured geometrical characteristics as outputs, that can be used to determine which measured characteristics are influenced by which input variable. Methods: The data was experimentally collected in a laboratory environment by intentionally changing positions of ingoing part, assembling the parts and subsequently measuring their geometrical characteristics. We use liner model to establish the relation between geometrical characteristics measured on the assembled parts, and the input variables of interest. Results: Presented is a modeling technique that can be used to establish which measured geometrical characteristics are influenced by input variables (i.e.pins’ positions) of interest. The natural variation in the system (i.e. not modeled variation) is quite high. The time passed between measurements has a significant influence on measured values.

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