17 research outputs found
Virtuell simulering av en monteringssekvens
No full textThe dataset contains an operation sequence built with the IPS IMMA software, it represents the tasks that a manikin family performs in a virtual simulation. With this operation sequence the manikin family can simulate the process of collecting parts from the material façade as well as collecting the tightening tool and simulate the assembly.
The mp4 showcases the original solutions process description used for the multi-objective optimization of the assembly station. The animation can give the reader of the manuscript further insight of the process to be carried out at the station.
For further information see:
Lind, Andreas, Veeresh Elango, Lars Hanson, Dan Högberg, Dan Lämkull, Pär Mårtensson, and Anna Syberfeldt. 2023. "Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory" Systems 11, no. 8: 395. https://doi.org/10.3390/systems11080395Datasetet innehåller en operationssekvens skapad med mjukvaran IPS IMMA, animeringen redovisar hur artiklar och verktyg plockas av en manikin familj. Med operationssekvensen kan process simuleras där en manikin familj hämtar komponenter from materialfasaden samt använder ett åtdragningsverktyg för att genomföra en simulerad montering.
Animeringen visa upp den ursprungliga lösningens processbeskrivning som används för multi-objektiv optimering av monteringsstationen. Animering kan ge läsaren av manuskriptet ytterligare insikt i processen som ska genomföras på stationen.
För ytterligare information se:
Lind, Andreas, Veeresh Elango, Lars Hanson, Dan Högberg, Dan Lämkull, Pär Mårtensson, and Anna Syberfeldt. 2023. "Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory" Systems 11, no. 8: 395. https://doi.org/10.3390/systems1108039
Virtuell simulering av en monteringssekvens
No full textThe dataset contains an operation sequence built with the IPS IMMA software, it represents the tasks that a manikin family performs in a virtual simulation. With this operation sequence the manikin family can simulate the process of collecting parts from the material façade as well as collecting the tightening tool and simulate the assembly. The mp4 showcases the original solutions process description used for the multi-objective optimization of the assembly station. The animation can give the reader of the manuscript further insight of the process to be carried out at the station. For further information see: Lind, Andreas, Veeresh Elango, Lars Hanson, Dan Högberg, Dan Lämkull, Pär Mårtensson, and Anna Syberfeldt. 2023. "Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory" Systems 11, no. 8: 395. https://doi.org/10.3390/systems11080395Datasetet innehåller en operationssekvens skapad med mjukvaran IPS IMMA, animeringen redovisar hur artiklar och verktyg plockas av en manikin familj. Med operationssekvensen kan process simuleras där en manikin familj hämtar komponenter from materialfasaden samt använder ett åtdragningsverktyg för att genomföra en simulerad montering. Animeringen visa upp den ursprungliga lösningens processbeskrivning som används för multi-objektiv optimering av monteringsstationen. Animering kan ge läsaren av manuskriptet ytterligare insikt i processen som ska genomföras på stationen. För ytterligare information se: Lind, Andreas, Veeresh Elango, Lars Hanson, Dan Högberg, Dan Lämkull, Pär Mårtensson, and Anna Syberfeldt. 2023. "Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory" Systems 11, no. 8: 395. https://doi.org/10.3390/systems1108039
REPETER
No full textA tool to help in annotation of relevant papers to read during Literature Review for research.A tool to help in annotation of relevant papers to read during Literature Review for research
Evaluating ERAIVA - a software for video-based awkward posture identification
No full textThe convergence of the focus of Industry 5.0 on human well-being and the prevalent problem of work-related musculoskeletal disorders necessitates advanced digital solutions due to limitations in manual risk assessment methods. This research aimed to compare usability of a newly developed video-based awkward posture identification software, the ergonomist assistant for evaluation (ERAIVA) with a conventional manual method. The risk assessment tool utilised in this study, integrated into the ERAIVA digital platform, is the risk management assessment tool for manual handling proactively (RAMP). Four assessors evaluated video-recorded tasks using both methods (manual and ERAIVA). The usability was assessed through the post-study system usability questionnaire, time consumption, number of video replays and video annotation deletions. The impact on identification of awkward posture durations was also studied. ERAIVA exhibited the highest usability score; it showed a higher number of video replays of specific sequences and annotations without significant differences in time consumption.CC BY 4.0Veeresh Elango: [email protected]</p
Change Point Detection in Sequential Sensor Data using Recurrent Neural Networks
No full textChange-point detection is the problem of recognizing the abrupt variations in sequential data. This covers a wide range of real world problems within medical, meteorology and automotive industry, and has been actively addressed in the community of statistics and data mining. In the automotive industry, sequential data is collected from various components of the vehicles. The changes in the underlying distribution of the sequential data might indicate component failure, sensor degradation or different activity of the vehicle, which explains the need for detecting these deviations in this industry. The research question of this thesis focuses on how different architectures of the recurrent neural network (RNN) perform in detecting the change points of sequential sensor data. In this thesis, the sliding window method was utilised to represent the variable sequence length into fixed length. Then this fixed length sequences were provided to many input single output (MISO) and many input many output (MIMO) architectures of RNN to perform two different tasks such as sequence detection, where the position of the change point in the sequence is recognized and sequence classification, where the sequence is checked for the presence of a change point. The stacking ensemble technique was employed to combine results of sequence classification with the sequence detection to further enhance the performance. The result of the thesis shows that the MIMO architecture has higher precision than recall whereas MISO architecture has higher recall than precision but both having almost similar f1-score. The ensemble technique exhibit a boost in the performance of both the architectures.Ändringspunktdetektering är problemet med att upptäcka den plötsliga förändringen av egenskaperna hos sekventiell data. Detta täcker ett brett spektrum av problem inom t.ex. medicin, meteorologi och fordonsindustrin, och har diskuteras aktivt i statistikoch datavinnnings området. I bilindustrin samlas sekventiella data från olika delar av fordonet. Förändringen i egenskapen hos sekventiella data kan indikera komponentfel, sensornedbrytning eller förändring av fordonets användning, vilket förklarar behovet av att detektera dessa avvikelser i denna bransch. I denna uppsats undersöker vi olika arkitekturer av återkopplade neurala nätverk (engelska recurrent neural networks, RNN), såsom många insignaler och en utsignal (MISO) och många inoch utsignaler (MIMO) arkitekturer, för att detektera detektera förändringspunkter över sekventiella data från fordonsensorer. I denna uppsats användes ett glidande fönster för att omvandla de variabla sekvenslängderna till sekvenser av fasta längder. Dessa sekvenser tillhandahölls till MISOoch MIMO-arkitekturerna för RNN för att utföra två olika uppgifter: sekvensdetektering för att hitta positionen av ändringspunkten i sekvensen och sekvensklassifiering för att upptäcka om sekvensen innehåller en ändringspunkt. Stapling av klassificerare har använts för att kombinera resultaten av sekvensklassificering med sekvensdetektering för att ytterligare förbättra prestanda. Resultatet av uppsats visar att MIMO-arkitekturen har högre precision än känslighet medan MISO-arkitekturen har högre känslighet än precision men båda har liknande f1-poäng. Stackningssamlingstekniken förbättrar resultaten i båda arkitekturerna
Upper Body Postural Assessment of Forklift Operators using Artificial Intelligence
No full textObservational postural assessment methods which are commonly used in industry suffer from low inter- and intra-rater reliability, as well as high time consumption. Postural assessment systems based on motion capture have been researched, but they have mainly been tested inside lab environments. This thesis aims to develop and evaluate an upper body postural assessment system using a depth camera and OpenPose, a human pose estimation library, for forklift driving in an industrial setting. The results from the computer vision system were compared to XSens, an IMU-based system. Data from three operators that performed seven indoor and outdoor forklift driving tasks in total was recorded with a depth camera and XSens sensors. The angles calculated using the OpenPose keypoints generally followed the trend of the XSens angles and showed small errors. However, the results after applying RAMP thresholds significantly differed. Thus, the direct applicability of the computer vision system for postural assessment could not be confirmed. Limitations that affected the performance were the small scale of the study, as well as the camera field of view and perspective, self-occlusions, and angle calculation formulas. Further study should be conducted to assess the reliability of XSens in industrial environments. In addition, the computer vision system should be compared to manual evaluations to assess its applicability. Further work should also focus on alternative camera positions for improving the field of view and self-occlusion problems
Evaluation of upper body postural assessment of forklift driving using a single depth camera
No full textObservational postural assessment methods which are commonly used in industry are time consuming and have issues of inter- and intra-rater reliability. Computer vision (CV) based methodshave been proposed, but they have mainly been tested inside lab environments. This study aims to develop and evaluate an upper body postural assessment system in a real industry environment using a single depth camera and OpenPose for the task of forklift driving. The results were compared with XSens, an Inertial Measurement Unit (IMU) based system. Data from three forklift drivers performing seven indoor and outdoor tasks were recorded with a depth camera and XSens sensors. The data were then analyzed with OpenPose with additional custom processing. The angles calculated by the computer vision system showed small errors compared to the XSens system and generally followed the trend of the XSens system joint angle values. However, the results after applying ergonomic thresholds were vastly different and the two systems rarely agreed. These findings suggest that the CV system needs further study to improve the robustness on self-occlusion and angle calculations. Also,XSens needs further study to assess its consistency and reliability in industrial environments.</p
Investigation on the effect of friction stir processing on tribological and mechanical properties of Al 7075-T651 alloy
No full textInvestigation of mechanical properties of aluminium reinforced glass fibre polymer composites
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