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A comparative thermal analysis of two workpiece materials of different machinability when turning based on IR thermography

Uhrynski Andrzej, Nurkusheva Saltanat, Usserbayev Muratbek, Hyla Pawel, Bembenek Michał

Journal of Machine Engineering

2024

Vol. 24, No. 1

50--59

The comparative thermography analysis of temperature during machining by turning was presented. For the tests cast iron EN-GJL 250 and stainless steel 1.4301 were used. The machining by turning was performed with the TNMG 220408HS PC9030 and TNMA 220208 NC6210 cutting inserts design for machining that kind of materials. The temperature was measured on the machined material and on the surface of the cutting insert. The temperature distribution was performed during 3 subsequent turning passes; therefore, the coolant was not used during machining. The emissivity of TNMG 220408HS PC9030 and TNMA 220208 NC6210 cutting inserts was performed. In the case of EN-GJL-250 cast iron, the tests have shown that due to safety reasons (the lack of the safety cover in the working area of the lathe) it was impossible to perform the measurements at the highest assumed machining speed of 339.1 m/min. The higher average temperatures in the material were recorded for 1.4301 steel, even though the machining process was performed at a much lower machining speed than in the case of EN-GJL-250 cast iron. The average cutting insert temperature when turning EN-GJL-250 cast iron was approximately 100°C higher than for 1.4301 steel.

Autonomous vehicles energy based operation capacity planning

Szpytko Janusz, Hyla Pawel, Duarte Yorlandys Salgado

Journal of Machine Engineering

2020

Vol. 20, No. 4

126--138

The paper presents an optimization model for an Automatic Guided Vehicle (AGV) operation capacity planning with focused to complete predicted mission. To successfully complete the mission the available resources related to the mission task we need to predict set of the device operation capacity indicator: technical status of the device structure and functions, device control strategy, access to the energetic resources type and others. Paper is focusing on device control strategy of the AGV under operation optimisation results minimizing possible gaps corresponded with the access to the energy. The scenarios are proposed by a Particle Swarm Optimization (PSO) algorithm, and the AGV operation is evaluated with the State of Charge (SoC) variable. The selected SoC variable allows us to describe the simulated operation in detail over time. The model output is the optimal trajectory for the AGV system considering the working environment and the satisfaction of the mission preestablished by the user. The inputs parameters of the optimization model are validated by a real environment created in a laboratory scale. The localization system, trajectories planning, workspace mapping and AGV control system concepts are briefly described, as well as the artificial intelligence used as methods and tools for AGV working control, to guide the discussion towards the contribution proposed.