Intelligent knowledge-based system for the automated screwing process control

Transcription

1 Intelligent knowledge-based system or the automated screwing process control YULIYA LEBEDYNSKA ULRICH BERGER Chair o automation Brandenburg University o Technology Cottbus Konrad-Wachsmann-Allee 1, Cottbus GERMANY ulrich.berger@tu-cottbus.de Abstract: - In this approach a knowledge-based system with a parameters quality assessment or automated screwing processes is developed. To avoid technical risks, it makes sense to develop methods, which automatically extract the process related data and provide it to operator aterwards. By using the knowledgebased system, inormation (process and control data) about the parameterization o the screwing process control with standardized correlations and tested process steps is provided. In this way the running, modiication and optimization o the screwing process can be implemented without errors. This system was tested on an experimental platorm or screwing process in the research laboratory. Thereby, the unctionalities o the platorm and their components are described and a validation o the developed system is carried out. Data interrelations between dierent sotware components are elaborated in detail. The validation o the system is done using the screwing process test case. The precision in the parameterization is made with help o unctional properties o parameters. Consequently, an obvious minimization o the parameterization errors and a simple screwing process control are achieved. Key-Words: - Intelligent knowledge-based system; automated screwing process control; data extraction; process parameterization 1 Introduction Complex technical recourses require most likely process oriented knowledge. It provides a rational design o technological knowledge, which includes personal experiences, documented systematic knowledge and enormous potential or improving screw control [1]. The inormation processing o knowledge or the screwing process control involves the investigation o relevant norms and standards, the change o system properties by local adaptation o the system identiication parameter, the operator experience knowledge and the sotware-based suggestions or parameters settings. That is, the search or causes o aults and the generation o new intuitively designed knowledge and the level o quality process knowledge. For the sae and reliable screwing process a system based on scientiic methods or the targeted process improvement must be developed [2]. The precise user navigation through the individual process steps with methodological justiication o the process steps enables ast and reliable process control [3]. These exists a huge diversity o screw joints as well as the process-related requirements or screw joints. For this reason, it is necessary to dissolve the screwing operation into its screwing process steps. For this and or the assessment o process quality degree appropriate scientiic methods must be selected and the dierent conditions in each screwing step must be deined. A correct parameter variation is diicult to determine due to various actors involved in each screwing process step (Figure 1) [4, 5]. ISBN:

2 Material properties Roughness o the composite contact areas Temperature and corrosion Screw connection Complicated structural design Tolerance o the screw connection Reliability o the screw connection Surace and lubrication conditions Various types o the screwing process Fig. 1: Inluential actors o a screw connection [6] 2 Problem Formulation In order to achieve a high quality o the process method or the extraction o the theory, technology and inormation related to technical experiences rom the considered inormation recourses must be developed. The result leads to an increase in the eectiveness and minimized intererence with automated screwing process and a reduction o technical and economic risks related to post treatment o each screwing step. For the correct and simple process control, the screwing system components must be decomposed into parts. The process low model is based on Petri net with various intermittent appropriate parameters and conditions [7]. The ollowing methodological approach is used or the design o an intelligent knowledge-based system with inormation quality management or the automated screwing process control: Decomposition o the screw process into sub- processes, Schematic description o the process parameters, System description in consideration o standards and norms, Composition o the relevant parameters or the respective screw process, Development o the screwing process control model using Petri net, Procedures or the assessment inormation quality based on the unctional properties o parameters. 3 Problem Solution The objective o this work is to develop an intelligent knowledge-based system with a parameter quality assessment or automated screwing processes. The screwing parameters or each screwing process case were gathered and analyzed. Aterwards the relationships between process parameters were deined. The ocus was made on the concept development o a universal intelligent system with combined chain method or optimizing automated screwing process control. Sometimes it is diicult to parameterize the complicated screwing processes quickly and correctly due to several inluential actors. These deiciencies must be compensated with the help o the intelligent knowledge-based system. The system includes structure or each screwing process considering ive maturity levels. The quality increases with each level. Level 1: The user has no organizational documentation support. Level 2: The individual sub-processes are deined. ISBN:

3 Level 3: A process model is available, on which a better understanding o the process can be established. Level 4: The only standardized quality processes are controlled; the target parameters are related to the actual values. Level 5: The introduced comprehensive and detailed parameters are estimated. The process data is analyzed regularly. The results are used or other parameterizations. In the knowledge-based system comprehensive detailed parameter estimation is integrated. A regular analysis o process data and results are used or other parameter settings. For the achieving o error-ree processes in the system a systematic classiication o process parameters was integrated. This helps in prompt detection o errors in the screwing process control. For simpliy o process the decomposition o the process into sub-processes is used. Degree o detail o a process CAD data o the screw joint Screw CAD parameters conditions Maximum torque Scientiic Konstruktive Petri Daten net methods (SQL Daten, CAD, CAM) Structured Transer o the process sub-processes description in the Petri net Deinition subprocesses Knowledgebased system Output values Parameterized user guide Quality level o parameterization Minimal screwing time Error minimization Inormation content Experienced knowledge Calculated parameter values Estimated parameters Constructive data (SQL data, CAD, CAM) DIN regulations and VDI Guidelines Fig. 2: Concept o knowledge-based system or the automated screwing process control The system should be able to oer operators the ultimately possible alternatives in complex and precise screwing process control. The user riendly machine interace was developed with incorporation o explicit, implicit and hardware inormation. 3.1 Quality estimation o process parameters in the intelligent knowledge-based system The qualitative properties o parameters are weighted according to their reliability. The characteristics o reliability parameters are weighted to analyze the correct and error prone system perormance. As a unction o reliability is deined as the dependence o the proper unction ulilments r (trouble-ree installation process) and the number o alse or impaired unction o settlements (occurring errors or ailures) [VDI08 / 1]. The unction o parameter reliability is ollowing: reab r reab = (2.1) r + A Boolean approach is ollowed. By the quality parameter determination the irst property is the deicient and the second property is advantageous. The property parameter deinition is ollowing: 1. Number o in practice not validated parameters (estimated).- Boolean 2. Number o the determined parameters o process dependencies.- Boolean 3. Number o operations according to the parameters determining standards and guidelines.- Boolean 4. Number o correct tested parameters.- Boolean The complete quality o a parameter block is calculated as the sum o advantageous properties in each screwing process step. 3.2 Inormation content o the knowledgebased system The Figure 3 shows the inormation content o the developed knowledge system. ISBN:

4 Inormation content o the knowledge-based system Digital library explicit knowledge technical/ technological Digital library implicit knowledge userknowledge base Digital library plant/ machine environment CAD - parameter Screwing parameters Size Pitch Strength class Length Screwing conditions Screw deep Material Surace DIN - parameters Maximum torques Type o screw procedure Estimated and proven process low parameters Find stage Speed Control time Angle in the release and tightening direction approach angle Pretightening Speed Find the moment Approach angle Final tightening Starting angle count Turno Angle o the window limits Selection o the tool Maximum and minimum torques Maximum speed Type o power takeo Coniguration o the servo ampliier Calculation and dependence parameter Parameter composition or several screwing processes Quality parameter assessment Intelligent parameter eedback Fig.3: Inormation content o the knowledge-based system With the integration o the dependencies between the parameters in the inormation content a ast parameterization o the screwing process control was achieved. The developed knowledge based system has the ollowing inluence properties on the aultless execution o the screwing process: Automatic veriication o process reliability, Warnings about incorrect parameter settings, Maximum user support or easy understanding, Comprehensive documentation or the process parameterization, Ability to identiy parameters rom calculations, Clear graphical representation o results. A parameterization should be conducted so that any malunction noticed relatively early and not with the real screwing process. 4 Technical realization The developed knowledge-based system was tested on experimental platorm. The igure 4 shows the technical realization. ISBN:

5 Data base server Evaluation results Knowledge-base system Screw Speciication o process variables Servo ampliier Absence o errors Retrieving o inormation Assembly shop Automated screw driver or Semi-automatic screwdriver Input and output Digital Analog Control signals Start Stop deault values: speed, current Transducer EC motor Transmission Torque transducer Drive side Transducer EC motor Transmission Torque transducer Control panel or Drive side Fig.4: Technical realization o the knowledge-based system The screwing process programs, parameter properties and other data are deined with the knowledge-based system at the assembly shop. The screwing operations are stored against an appropriate identiication number in the database server. The results o screw connections are included in the database server in the orm o torque-angle diagrams. Servo ampliier is controlled by the analog and digital signals. The various measurement values by screwing are transmitted back to the screwdriver control: start, stop, and the deault values (speed and power). For each screwing test case screwing torque, the rotation angle and the motor power consumption is determined. The ully automatic or semi automatic screwdrivers can be selected or the screw system. The semi-automatic screwdriver has also a control panel with a manual control with the recording o the screw process results. The process-speciic monitoring o the screwing operation is perormed by means o angle and torque sensor and the acquisition o motor current. The intelligent storage o screwing test cases is realized in the knowledge-based system. 5 Conclusions Trough this approach several technical and economical advantages in the knowledge management or the screwing process control were achieved. A clear model o the screwing process in the light o explicit and implicit knowledge has been implemented. The application o Petri net or the sequencing and stepwise process description contributes or a new method or process analysis and simpliication o the process control. A conceptual approach or parameter determination or screwing process control introduced in this paper. The parameter quality assessment was based on the unctional properties o the immediate results o the screw process. The unction o the storage o the tested parameter blocks was seen as a sellearning or an intelligent unction in the knowledge-based system. The economic objectives were the minimized time or the parameters determination and reducing variety o parameters o ive tested screwing scenarios (Fig. 5). As a result o the process evaluation the process reliability and the error minimization were deined. The developed knowledge-based brings out the ISBN:

6 intuitive process control and signiicant increasing o the technical qualiications o plant operators. Parameters quality OK ive test scenarios app app [8] Bell, R., Research reports or high-strength screw connections based on acoustic emissions, iwb TU Munich, Munich, Herbert Utz Verlag, ISBN [9] Lunze, J., Automation, scientiic publishing Oldenburg, ISBN: [10] Lucko, S., et al., Knowledge Management, 7 blocks or the implementation in practice, 2nd Edition, Carl Hanser publishing, ISBN reduction the variety Variety o parameters Fig.5: Parameters variety reduction o ive test scenarios Reerences: [1] Wenzel, R., et al., Operations Management: the management o plant operations, irst Edition, Munich, Leipzig trade book publishing, ISBN [2] Thurner, E., et al., Evaluation o reliability o complex systems, Berlin Heidelberg, computer science magazine spectrum, Volume 21, Number 6, pp , Springer publishing ISSN X (Online) [3] DIN ISO/IEC : Inormation technology - Process assessment - Part 4: Application guidelines or process improvement and process capability determination, February 2010 [4] Nyhuis, P., Contributions to a theory o logistics, irst Ed, Berlin Heidelberg, Springer publishing, ISBN [5] VDI guideline VDI 2230 Part 1: Systematic calculation o high duty bolted joints cylindrical screw connections, Dusseldor, Beuth publishing, February 2003 [6] VDI guideline VDI 4008 Sheet 4: Methods o reliability, Petri nets, July [7] Klatte, B., Knowledge Management and Communication in Groups: Determination o characteristics, requirements and implications or the distribution o knowledge communication in groups, irst Ed, Norderstedt, GRIN Verlag, ISBN ISBN: