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  • 1.
    Gopinath, Varun
    et al.
    Department of Management and Engineering, Linköping University, Linköping, Sweden.
    Johansen, Kerstin
    Understanding situational and mode awareness for safe human-robot collaboration: case studies on assembly applications2019In: Production Engineering, ISSN 0944-6524, E-ISSN 1863-7353, Vol. 13, no 1, p. 1-9Article in journal (Refereed)
    Abstract [en]

    In order for humans and robots to collaborate on an assembly line, safety of operations is a prerequisite. In this article, two assembly stations where a large industrial robots collaborate with humans will be analysed with the aim to 1. determine the characteristics of hazards associated with human-robot interaction and 2. design solutions that can mitigate risks associated with these hazards. To support the aim of this article, a literature review will attempt to characterize automation and detail the problems associated with human-automation interaction. The analysis points at situational awareness and mode-awareness as contributing factors to operator and process safety. These underlying mechanisms, if recognised by the risk assessment team as hazards, can mitigate risks of operator injury or production delays. This article details the function of visual and physical interfaces that allow operators to comprehend system-state in order to avoid undesirable situations. 

  • 2.
    Salim, Roaa
    et al.
    Jönköping University, School of Engineering, JTH, Industrial Engineering and Management. Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Mapulanga, Mwanza
    Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Saladi, Praveen
    Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Karltun, Anette
    Jönköping University, School of Engineering, JTH, Industrial Engineering and Management. Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Automation in the wood products industry: challenges and opportunities2016Conference paper (Refereed)
    Abstract [en]

    A stagnation of productivity increase has been observed in the Swedish wood product industry. The manufacturers believe that there is a need to invest further in automation in order to stay competitive. For this reason, the paper seeks to understand the role of automation in the Swedish wood product industry, and focuses on identifying the challenges and opportunities of automation. The following research question was addressed: What are the challenges and opportunities of automation in the wood product industry? In order to answer the research question, four case studies were conducted, each case representing a different business area. The research question was examined in terms of internal – and external challenges and opportunities. The internal challenges and opportunities examine the manufacturing, while the external challenges and opportunities examine the influence of the business environment. Findings indicate that lack of manufacturing strategies, and lack of awareness of automation technologies were some of the main challenges. Regarding the opportunities, increased profitability and competitiveness were emphasized. The identification of the challenges and opportunities of automation in the wood product industry can provide insights and be used as underlying decisions for automation investments.

  • 3.
    Svensson, Simon
    et al.
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Wadsten, Adam
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Kriterier för automation vid inlagring: Ett beslutsunderlag i valet av artikelplacering2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The role of warehouse in the value stream, plays a significant part in how well a company can satisfy the requirements of a customer. To be able to support the demand of the customers, production companies are forced to produce a bigger variety of models in the product range, which results in an increasing assortment. With an effective stock keeping, there is a potential to reduce waste and increase profitability. In the choice of warehouse-design, warehouse-operations have the opportunity of choosing between a number of different designs containing manual and automated put away.It has become common that companies are choosing a combination of automated and manual processes. With this combination of processes, the companies are faced with choices about stock locations. With the help of well developed criterias about stock locations, there is a potential to achieve a more effective put away. The purpose of the study has been formulated as ”Investigate when automation in warehouse management makes the put away process in a central warehouse more efficient”.

     

    Through a case study on a warehouse, located in Nässjö, this report will answer the research questions regarding which of the manual and automated put away processes to use. The research questions are:

    1. Which criteria affect the decision of automated or manual put away?
    2. Based on the criteria that are raised in research question 1, when is it more efficient to store carton in AS/RS in comparison with manual picking with truck in pallet racking?

    The research questions wish to lift criteria as a basis for decision making and when it is more effective to use automated put away. The warehouse that this study is based on is using both automated and manual materials handling. The specific automation system that has been the basis for this study’s result is AutoStore. This report has a qualitative approach, the methods that has been used for collecting data are interviews, observations and document studies.

     

    Dimension, process time and utilization were the three criteria that are presented in the result. Based on the pallets limit of space in the automated system, the dimension of the article is the first criteria. In addition to the article’s measurements, the articles shape and material of the package had an impact if the article would fit in the load carrier. The two put away processes include different operations that differ in how long they take to execute, which has an impact on the company's efficiency. One reason for the time consuming difference, turned out to be how the quantity affects the time for put away in AutoStore comparing with the constant time for manual put away. A time consuming operation in the process with AutoStore was the one with repacking for the systems pallet. The operation also turned out to affect the utilization rate, due to its measurement of a pallets fill rate.  The quantity of put away in combination with the dimensions of the article, affects the total utilization rate.

     

    With the result, the aim of the study is to contribute to companies that contains a combination of warehouse-design with a basis for the decision making about stock locations. 

  • 4.
    Vilhelmsson, André
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. 9502071419.
    Detektion av landningsbanor med maskininlärning: Optimering av maskininlärningsparametrar för videoigenkänning2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    PurposeThe purpose of the thesis is to use design science research to produce information to improve a support vector machine (SVM) model and to gain knowledge about optimization of hyperparameters through experiments. The objective will be answered through evaluation of performance gained by adjusting the hyperparameters. The report will also evaluate the further use of the techniques with IR cameras in combination with histogram of oriented gradients (HOG) as translated input for the SVM-model trained to detect and do a classification of runways.MethodThe method was performed through three technical experiments performed at Saab Avionics Systems. The experiments contain adjusting and optimizing settings in Saabs framework for detecting runways with the machine learning method support vector machine. The experiments with manual grid search will test and evaluate the performance of the support vector ratio, accuracy and more. Three different optimization methods will be evaluated which consists of grid search, random grid search and Bayesian optimization and all three will optimize the parameters C and gamma.FindingsThe findings indicate that all the evaluated parameters could bring positive performance impact on the SVM. Some important results were the HOG settings 6 orientation histograms, 64*64 pixels per cell, 2*2 cells per pixel, which performed best. An even dividable resolution of the image was also important for the HOG calculations. Other results indicate that the Radial Basis Function - kernel was the better choice over the linear kernel, as well as a higher amount of training data was better for a higher performance.The best performing optimization algorithm was the Bayesian optimization which found and improved three of eight testcases. The technique combining SVM with HOG-vectors for detecting runways are shown being implementable with high performance. A hardware implementation with an FPGA would be the recommended implementation which would improve classification time as well as the HOG computation time. Around 35-145 fps per detection window would be achievable with this technique with an accuracy above 99.4%.ComplicationsFew implications were found during the work apart from one. Which were the case of the detection window of one of the IR cameras (LWIR) which was a bit skewed compared to the SWIR sensor. The result contains some uncertainties of the performance compared between SWIR and LWIR sensors.LimitationsThe limitation of the report is that the training data were similar and only consisting of one runway. The accuracy performance can be a bit misleading when evaluating an imbalanced class.KeywordsSVM, HOG, runway, hyperparameters, optimization, RBF kernel, grid search, Bayesian optimization, random grid search, object detection, classification, margin for detecting window, over fitting, C, gamma, range, IR camera, training data, data augmentation, class balance in data, implementation.

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