Abstract
At present, energy consumption strongly affects the financial payback period of industrial robots, as well as the related manufacturing process sustainability. Henceforth, during both design and manufacturing management stages, it becomes crucial to assess and optimize the overall energy efficiency of a robotic cell by means of digital manufacturing tools. In practice, robotic plant designers and managers should be able to provide accurate decisions also aimed at the energy optimization of the robotic processes. The strong scientific and industrial relevance of the topic has led to the development of many solutions but, unfortunately, state of the art industrial manipulators are equipped with closed controllers, which heavily limit the feasibility and performance of most of the proposed approaches. In light of the aforementioned considerations, the present paper presents a novel simulation tool, seamlessly interfaced with current robot offline programming tools used in industrial practices, which allows to automatically compute energy-optimal motion parameters, thus reducing the robot energy consumption, while also keeping the same productivity and manufacturing quality. The main advantage of this method, as compared to other optimization routines that are not conceived for direct integration with commercial industrial manipulators, is that the computed parameters are the same ones settable in the robot control codes, so that the results can automatically generate ready-to-use energy-optimal robot code. Experimental tests, performed on a KUKA Quantec KR210 R2700 prime industrial robot, have confirmed the effectiveness of the method and engineering tool.
Highlights
Sustainable manufacturing may be defined as a production model where both present and future needs are contemporarily accounted for, implying that social, ecological and economic impacts should be quantitatively assessed and optimized [1][2][3]
In light of the aforementioned considerations, the present paper presents a novel simulation tool, seamlessly interfaced with current robot offline programming tools used in industrial practices, which allows to automatically compute energy-optimal motion parameters, reducing the robot energy consumption, while keeping the same productivity and manufacturing quality
Eco-efficient programming methods, which offer the possibility to reduce the Energy Consumption (EC) on existing plants, with a minimum investment cost and an overall higher financial impact, due to the huge number of robots installed worldwide. This approach includes the optimization of the production scheduling [6][7][8][9] and the energy-optimal Industrial Robots (IR) programming [10] [11][12][13]. We focus on the latter approach and, in particular, on IR energy-optimal programming for its wider impact: as clearly underlined in the past literature [12], existing robotic plants consume on the order of hundreds of kWh per day, so that any EC reduction may lead to a very substantial cost and carbon footprint decrease in the long term
Summary
Sustainable manufacturing may be defined as a production model where both present and future needs are contemporarily accounted for, implying that social, ecological and economic impacts should be quantitatively assessed and optimized [1][2][3]. Manufacturing companies aim at implementing sustainable manufacturing, in order to improve profitability, by reducing both consumption of resources and global expenses, as well as by satisfying the incoming ecological impact regulatory constraints. The huge adoption of Industrial Robots (IR), needed to satisfy the ever-increasing requirements in terms of manufacturing quality, customization and flexibility, has further raised the necessity of improving the energy efficiency of robotic plants. IR Energy Consumption (EC) has a strong impact on the overall manufacturing costs and final products carbon footprint The rest of this paper is organized as follows: Section 2 provides an in-depth review of the state-of-the-art and recalls methods and concepts which are useful for the scope of this paper; Section 3 presents an overall IR energy-model; Section 4 describes how the EC is computed, leveraging on the standard functionalities offered by the robot simulation tool Delmia Robotics; Section 5 reports about the optimization method and provides a numerical example; Section 6 provides experimental results, whereas Section 7 reports the concluding remarks and discusses about future directions of improvement
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