Practical Dimensional Error Control and Surface Roughness Inspection in Turning

2002 ◽  
Author(s):  
M. Shiraishi ◽  
T. Yamagiwa ◽  
A. Ito
Keyword(s):  
Surface Roughness ◽  
Optical Method ◽  
Machine Tools ◽  
Error Control ◽  
Manufacturing Processes ◽  
Dimensional Error ◽  
Final Output

Monitoring of machine tools and optimization of manufacturing processes require accurate values of in process measured quantities such as dimensional error, force, and surface roughness. The measurement as workpiece is in particular important because the final output in machining is evaluated as the quality machined workpiece itself. A new hybrid sensor using pneumatic and optical method has been developed which can monitor the dimensional error and surface roughness in turning. Satisfactory results were obtained through several experiments.

Recent Developments in Desktop-Sized Machine Tools

2017 ◽  
Vol 261 ◽  
pp. 425-431 ◽  
Author(s):  
Jan C. Aurich ◽  
Christopher Müller ◽  
Martin Bohley ◽  
Peter Arrabiyeh ◽  
Benjamin Kirsch
Keyword(s):  
Surface Roughness ◽  
Machine Tools ◽  
Micro Milling ◽  
Bottom Surface ◽  
Manufacturing Processes ◽  
Micro Machining ◽  
Machining Quality ◽  
Recent Developments ◽  
Micro Structures ◽  
Micro Tools

The miniaturization of components and the functionalization via micro structures demands for flexible and economic manufacturing processes. Micro machining, i.e. micro milling and micro grinding can meet these requirements. In this paper, desktop-sized machine tools and their components that were developed at our institute are presented. With those machine tools, micro tools can be machined and used in one clamping, allowing for increased machining quality. Grooves milled with such machine tools achieve a bottom surface roughness below 10 nanometer.

Effects of Cutting Condition on Surface Roughness when Turning Untreated and Sb-Treated Al-11%Si Alloys Using PVD Coated Tools

2013 ◽  
Vol 315 ◽  
pp. 413-417 ◽  
Author(s):  
Mohsen Marani Barzani ◽  
Mohd Yusof Noordin ◽  
Ali Akhavan Farid ◽  
Saaed Farahany ◽  
Ali Davoudinejad
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Manufacturing Processes ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Fabrication Cost ◽  
Coated Tools ◽  
Experimental Trials ◽  
Cutting Speeds

Surface roughness is an important output in different manufacturing processes. Its characteristic affects directly the performance of mechanical components and the fabrication cost. In this current work, an experimental investigation was conducted to determine the effects of various cutting speeds and feed rates on surface roughness in turning the untreated and Sb-treated Al-11%Si alloys. Experimental trials carried out using PVD TIN coated inserts. Experiments accomplished under oblique dry cutting when three different cutting speeds have been used at 70, 130 and 250 m/min with feed rates of 0.05, 0.1 and 0.15 mm/rev, whereas depth of cut kept constant at 0.05 mm. The results showed that Sb-treated Al-11%Si alloys have poor surface roughness in comparison to untreated Al-11%Si alloy. The surface roughness values reduce with cutting speed increment from 70 m/min to 250 m/min. Also, the surface finish deteriorated with increase in feed rate from 0.5 mm/rev to 0.15 mm/rev.

scholarly journals Analysis of the Machining Process of Titanium Ti6Al-4V Parts Manufactured by Wire Arc Additive Manufacturing (WAAM)

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 766 ◽  
Author(s):  
Fernando Veiga ◽  
Alain Gil Del Val ◽  
Alfredo Suárez ◽  
Unai Alonso
Keyword(s):  
Additive Manufacturing ◽  
Arc Welding ◽  
Machine Tools ◽  
Optimal Strategies ◽  
Machining Process ◽  
Manufacturing Processes ◽  
Plasma Arc Welding ◽  
Traditional Manufacturing ◽  
Wire Arc Additive Manufacturing

In the current days, the new range of machine tools allows the production of titanium alloy parts for the aeronautical sector through additive technologies. The quality of the materials produced is being studied extensively by the research community. This new manufacturing paradigm also opens important challenges such as the definition and analysis of the optimal strategies for finishing-oriented machining in this type of part. Researchers in both materials and manufacturing processes are making numerous advances in this field. This article discusses the analysis of the production and subsequent machining in the quality of TI6Al4V produced by Wire Arc Additive Manufacturing (WAAM), more specifically Plasma Arc Welding (PAW). The promising results observed make it a viable alternative to traditional manufacturing methods.

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

2020 ◽  
pp. 095440542097106
Author(s):  
Yu Su ◽  
Congbo Li ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Guangxi Zhao
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Tool Wear ◽  
Specific Energy ◽  
Machine Tools ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Support Vector

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

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