AN INTEGRATED APPROACH FOR DESIGN AND MANUFACTURE OF PLASTIC PRODUCTS

The aim of this paper is to present an integrated design procedure for plastic part development and manufacture. Reverse engineering (RE), re-engineering (ReE) and mold design have been incorporated to infuse agile characteristics in the proposed design and development process. The integration of RE and ReE presented in this paper accelerate the design process and provide information necessary for complete injection molding process of plastic parts. The purpose is to create a new object geometrically similar to the existing object. The new object must have better mechanical properties and more favorable geometry from the standpoint of mold ability. This paper also, describes a methodology for quality geometry check of the manufactured part that is need for verified the used integrated CAD/CAE technologies

Different Approaches in Uncertainty Evaluation for Measurement of Complex Surfaces Using Coordinate Measuring Machine

This paper describes a methodology for uncertainty assessment for Coordinate Measuring Machine measurement of complex real work pieces from industry. The study applied two approaches (in scanning mode only) for estimating the measurement uncertainty with the support of Taguchi plan in the experiment containing five factors: scanning speed, sample density, probe configuration, scanning direction, and position of measuring object. In the first approach the uncertainty was estimated by measuring the basic geometric objects (primitives like sphere and torus) representing the decomposition of complex surfaces and in the second one a complex surface was treated as an unknown quantity. Calculated uncertainty Type A for both measurement tasks was in the range from 0.65 μm to 6.47 μm. Evaluation of the uncertainty Type B covered specifications of the machine and standard uncertainties derived from temperature effects. Total uB component was found to be in order of 0.4 μm. Future research will be directed towards the development and application of simulation method

Comparative Characteristics of Ductile Iron and Austempered Ductile Iron Modeled by Neural Network

Experimental research of cutting force components during dry face milling operations are presented in the paper. The study was provided when milling of ductile cast iron alloyed with copper and its austempered ductile iron after the proper austempering process. In the study, virtual instrumentation designed for cutting forces components monitoring was used. During the research, orthogonal cutting forces components versus time were monitored and relationship of cutting forces components versus speed, feed and depth of cut were determined by artificial neural network and response surface methodology. An analysis was made regarding the consistency of the measured cutting forces and the values obtained from the model supported by an artificial neural network for the investigated interval of the cutting regime. Based on the results, an analysis of the feasibility of the application of austempered ductile iron in the industrial sector with the aspect of machinability as well as the application of the models based on artificial intelligence, was given. At the end of the presentation, the influence of the aforementioned cutting regimes on cutting force components is presented as well

Applying the MIMO BP Neural Network and Cloud-Based Monitoring of Thermal Behavior for High-Speed Motorized Spindle Units

Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools

Comparative characteristics of ductile iron and austempered ductile iron modeled by neural network

© 2019 by the authors. Experimental research of cutting force components during dry face milling operations are presented in the paper. The study was provided when milling of ductile cast iron alloyed with copper and its austempered ductile iron after the proper austempering process. In the study, virtual instrumentation designed for cutting forces components monitoring was used. During the research, orthogonal cutting forces components versus time were monitored and relationship of cutting forces components versus speed, feed and depth of cut were determined by artificial neural network and response surface methodology. An analysis was made regarding the consistency of the measured cutting forces and the values obtained from the model supported by an artificial neural network for the investigated interval of the cutting regime. Based on the results, an analysis of the feasibility of the application of austempered ductile iron in the industrial sector with the aspect of machinability as well as the application of the models based on artificial intelligence, was given. At the end of the presentation, the influence of the aforementioned cutting regimes on cutting force components is presented as well