scholarly journals TECHNOLOGICAL PREPARATION AND ADAPTIVE CONTROL IN DIGITAL PRODUCTION SYSTEMS

Author(s):  
Alexander R. Ingemansson
Author(s):  
A. R. Ingemansson

In the article the basic patterns of developed methodology of technological preparation of production and adaptive control of stability and quality in automated metalworking industry are described.


Author(s):  
A. Ingemansson ◽  
Ju. Tchigirinsky ◽  
V. Zhukov

The mathematical models for technological preparation of production and adaptive control of turning and milling in digital production systems are developed.


Author(s):  
Aleksandr Ingemansson

The paper is dedicated to the development of solutions for cutting effectiveness increase at the expense of the potentialities use of modern NC automated equipment and these solutions application for the formation of digital production systems (DPS) for machining. There are developed computation formulae allowing the definition of cutting force values during turning and mill operation aimed for technological pre-production (TP) and for the adaptive control of NC equipment in DPSs.


Author(s):  
A. Ingemansson

The ability of adaptive control of stability of machined parts surface integrity deformed condition and stability of working performance of cutting instruments in digital production systems is justified.


2020 ◽  
Vol 110 (04) ◽  
pp. 255-260
Author(s):  
Marvin Carl May ◽  
Andreas Kuhnle ◽  
Gisela Lanza

Im Rahmen der stufenweisen Umsetzung von Industrie 4.0 erreicht die Vernetzung und Digitalisierung die gesamte Produktion. Den physischen Produktionsprozess nicht nur cyber-physisch zu begleiten, sondern durch eine virtuelle, digitale Kopie zu erfassen und zu optimieren, stellt ein enormes Potenzial für die Produktionssystemplanung und -steuerung dar. Zudem erlauben digitale Modelle die Anwendung intelligenter Produktionssteuerungsverfahren und leisten damit einen Beitrag zur Verbreitung optimierender adaptiver Systeme.   In the wake of implementing Industrie 4.0 both integration and digitalization affect the entire production. Physical production systems offer enormous potential for production planning and control through virtual, digital copies and their optimization, well beyond purely cyber-physical production system extensions. Furthermore, digital models enable the application of intelligent production control and hence contribute to the dissemination of adaptively optimizing systems.


Author(s):  
A.V. Martyugin ◽  
I.M. Volodin

The using results of neural network to analyze the balancing of R4 crankshaft forgings using elements of digital production systems based on 3D parametric evaluation of forging imbalance parameters depending on the keywоrd parameters of the forging and the hot die forging process are presented. Specially created and trained neural network is used to approximate the results of the researches. The boundaries of keywоrds parameters of R4 crankshaft forgings are determined based on the analysis. The technological drawing of the forging and all stamping tooling are changed. The solution is implemented into production.


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