تولید برنامه ربات صنعتی بر اساس مبدل های CAD
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|18748||2011||7 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||8 روز بعد از پرداخت||405,000 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||4 روز بعد از پرداخت||810,000 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Robotics and Computer-Integrated Manufacturing, Volume 27, Issue 5, October 2011, Pages 942–948
Industrial robots are widely used in various processes of surface manufacturing, such as spray painting, spray forming, rapid tooling, spray coating, and polishing. Robot programming for these applications is still time consuming and costly. Typical teaching methods are not cost effective and efficient. There are many off-line programming methods developed to reduce the robot programming effort. However, these methods suffer many practical issues, such as cable/hose tangling, robot configuration, collision, and reachability. To solve these problems, this paper discusses a new method to generate robot programs. Since industrial robots have been used in production for decades, there are many robot programs for different parts generated by the robot programmers. These robot programs, which contain not only the robot paths, but also the programmers' knowledge and process parameters, can be transformed to generate new robot programs for similar parts. In this paper, a transformative robot program generation method is developed based on the existing ones in the database. Experiments were performed to validate the developed methodology. The results are very promising in reducing the programming efforts in surface manufacturing.
Surface manufacturing is a process of adding material to or removing material from the surfaces of a part. Spray painting, spray forming, rapid tooling, spray coating, and polishing are some of the typical examples in surface manufacturing. Industrial robots are typically used for these applications. Since there are many requirements for these complex industrial processes, robot programming to satisfy these requirements is very challenging. One example is the robot program generation for the painting processes. The product quality, paint usage as well as the robot performance has to be considered. The quality of the robot programs generated by the teaching methods depends on the programmers' experience and skills. The methods also require that programmers carry out extensive tests on work cells to improve the product quality and system performance. Therefore, they are not cost effective anymore because products are subject to a shorter product life, frequent design changes, small lot sizes, and small in-process inventory restrictions. Furthermore, the programmers have to be exposed to the hazardous environments. To overcome these problems, many off-line programming methods have been developed to make the complex robot programming easier , , , , , , , ,  and . Even though these methods can be used to generate robot paths based on the CAD models of parts, there are many practical issues when the generated robot program is downloaded into a robot controller to control the motion of a robot. Fig. 1 shows a paint robot with hoses attached to the tool for a painting process.
نتیجه گیری انگلیسی
Industrial robots have been used in production for several decades and the robot programmers have been accumulating a lot of CAD models and robot programs, which have been successfully implemented in manufacturing. To use these existing robot programs recorded in a database, which contain robot programmers' knowledge and process information, a new robot program generation method based on the database has been developed. Experiments were performed using real industrial parts to validate the developed method. Experimental results demonstrate that the developed method is feasible to generate new robot programs to reduce the robot programming effort. Our future work will concentrate on three issues: (a) investigate a method to determine the threshold value for similarity comparison because it is important for finding a matching part in the database, (b) develop a method to deal with the local feature on the surface because the local feature will not affect the similarity comparison too much, but it will affect the robot path, and (c) obtain more industrial parts with robot programs to further validate the developed method and find industrial partners to implement the developed method to evaluate its market potentials.