Manufacturing knowledge for industrial robot systems: Review and synthesis of model architecture

Kuss, Alexander; Diaz, R P Julian; Hollmann, Rebecca; Dietz, Thomas; Hägele, Martin

doi:10.1109/coase.2016.7743427

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Moreover, another important cost factor is the training of the operators. After the running-in process, the benefits of the industrial robot materialize, as the increasing output, quality, or productivity causes cost savings [34]. Maintenance is considered as a relatively independent life-cycle phase, since it interrupts the operation of the industrial robot.…”

Section: Cost Factors Related To the Lcc Of Robot Substitutionmentioning

confidence: 99%

Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Zhao

Liu

2021

Symmetry

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Within the context of the large-scale application of industrial robots, methods of analyzing the life-cycle cost (LCC) of industrial robot production have shown considerable developments, but there remains a lack of methods that allow for the examination of robot substitution. Taking inspiration from the symmetry philosophy in manufacturing systems engineering, this article further establishes a comparative LCC analysis model to compare the LCC of the industrial robot production with traditional production at the same time. This model introduces intangible costs (covering idle loss, efficiency loss and defect loss) to supplement the actual costs and comprehensively uses various methods for cost allocation and variable estimation to conduct total cost and the cost efficiency analysis, together with hierarchical decomposition and dynamic comparison. To demonstrate the model, an investigation of a Chinese automobile manufacturer is provided to compare the LCC of welding robot production with that of manual welding production; methods of case analysis and simulation are combined, and a thorough comparison is done with related existing works to show the validity of this framework. In accordance with this study, a simple template is developed to support the decision-making analysis of the application and cost management of industrial robots. In addition, the case analysis and simulations can provide references for enterprises in emerging markets in relation to robot substitution.

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Section: Cost Factors Related To the Lcc Of Robot Substitutionmentioning

confidence: 99%

Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Zhao

Liu

2021

Symmetry

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“…The work of Perzylo et al is a part of the EU funded SMErobotics consortium [13] which aims to simplify production systems for small and medium-sized enterprises. The consortium includes a product-process-resource technology model [14] that allows process-level information such as welding voltage to be incorporated through the technology model. The research presented by the consortium approaches a wide range of manufacturing domains from assembly to welding, and includes a product-centric robot instructions and uncertainty-aware robot skills [15].…”

Section: Related Workmentioning

confidence: 99%

A System Architecture for CAD-Based Robotic Assembly With Sensor-Based Skills

Pane

Arbo

Aertbeliën

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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Specifying assembly tasks in CAD-level is a promising approach to intuitively program complex robot skills. In this paper, a three-layered system architecture is presented to generate sensor-based robot skills from an assembly task instance. The architecture consists of an application layer where the user instantiates assembly tasks by specifying CAD constraints between geometric primitives pairs. A process layer infers the most suitable robot skills and their appropriate parameters. This inference is made possible by reasoning on a knowledge database represented as an ontology. The ontology contains semantic models of relevant classes such as tasks, skills, and geometric primitives as well as the relations between them. A control layer executes the sensor-based skills in real time using the eTaSL programming framework. A software implementation for the three layers is presented. The application layer is implemented in FreeCAD while the process layer consists of an OWL ontology, a Prolog-based reasoner and fuzzy inference to correctly select the skill and generate its parameters. In the control layer, the instantiated eTaSL skills execute the assembly tasks by sending an optimized control command to the robot. The system is validated on two challenging assembly cases with two distinct robot types, thus demonstrating the system's capability across different scenarios.Note to Practitioners-The widespread use of CAD models for describing parts assembly has motivated the research community to create systems that automatically generate robot programs satisfying the assembly goal. While most of the existing literature focuses on generating the assembly sequence, this paper deals with the aspect of translation from CAD-level assembly specification to executable robot motion, also called skills. The paper systematically addresses the problem by dividing it into different layers and solving them separately. Parameters that influence the successful execution of an assembly task are identified and categorized into application-related and processrelated parameters. Different inference techniques are employed to address each parameter category. Experimental results show that the proposed system is able to successfully generate and execute robot skills for assembly scenarios of an air compressor and an electric motor.

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“…We have developed a model-based architecture for SME-suitable production systems that extends the well-known productprocess-resource modeling approach [8] with a technology model [9] (Figure 4) that provides specific knowledge about manufacturing technologies, e.g., the optimal orientation of a welding torch relative to a work piece or parameters such as the typical voltage or wire feed speed for the given materials. In SMErobotics, these models are either serialized to Automation-ML [9] for higher compatibility with existing automation solutions or represented in a semantic description language (see the following section), which allows logical reasoning on the represented models to be conducted.…”

Section: Product-process-resource Technology Modelmentioning

confidence: 99%

SMErobotics: Smart Robots for Flexible Manufacturing

Perzylo

Rickert

Kahl

et al. 2019

IEEE Robot. Automat. Mag.

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urrent market demands require an increasingly agile production environment throughout many manufacturing branches. Traditional automation systems and industrial robots, on the other hand, are often too inflexible to provide an economically viable business case for companies with rapidly changing products. The introduction of cognitive abilities into robotic and automation systems is, therefore, a necessary step toward lean changeover and seamless human-robot collaboration. In this article, we introduce the European Union (EU)funded research project SMErobotics (http://www.smerobotics .org/), which focuses on facilitating the use of robot systems in small and medium-sized enterprises (SMEs). We analyze open challenges for this target audience and develop multiple efficient technologies to address related issues. Realworld demonstrators of several end users and from multiple application domains show the impact these smart robots can have on SMEs. This article intends to give a broad overview of the research conducted in SMErobotics. Specific details of individual topics are provided through references to our previous publications.

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Manufacturing knowledge for industrial robot systems: Review and synthesis of model architecture

Cited by 8 publications

References 20 publications

Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

A System Architecture for CAD-Based Robotic Assembly With Sensor-Based Skills

SMErobotics: Smart Robots for Flexible Manufacturing

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