Evaluation and systematic selection of significant multi-scale surface roughness parameters (SRPs) as process monitoring index

doi:10.1016/j.jmatprotec.2017.01.017

Journal of Materials Processing Technology

Volume 244, June 2017, Pages 157-165

https://doi.org/10.1016/j.jmatprotec.2017.01.017 Get rights and content

Abstract

The evaluation of multi-scale surface roughness parameters (SRPs) is important to solve many engineering problems (e.g. contact stress, sealing, friction) and is closely related to further fundamental problems (e.g. microbial contamination). Traditionally, surface roughness has been used as a standard for indicating process performance, such as tool wear, tool vibration etc. This paper also aims to find appropriate surface roughness parameters (SRPs) that can be used as process monitoring indices. Grade 304 stainless steel surfaces, generated by extrusion and grinding processes, were used in this study. The evaluation of different SRPs and their topography properties (such as fractal dimension) is discussed for extruded and ground surfaces. One problem with existing surface metrology is the availability of a multitude of disconnected roughness parameters. A statistical approach is presented in this paper that allows the most appropriate roughness parameters to monitor whether the intended surface quality converges to be found.

Introduction

Finding the right balance between reliable product performances and maximizing manufacturing process efficiency is complex and depends on a large number of processing factors. Different tool geometries, choice of cutting tools and workpiece materials, tool and machine wear, processing time are some of very critical factors among a vast amount of other considering factors (Jawahir et al., 2011). Among the high volume of input output parameters, surface roughness plays a significant role for assessing product performance. Different surface roughness parameters (SRPs) not only affect the mechanical and physical properties (e.g. friction coefficient, residual stress) of mating parts, but also the optical behavior and coating behavior of non-contacting parts like shininess or glossiness (Linke and Das, 2016). Thus, surface roughness parameters can control the surface functional properties like wear, friction, lubrication, fatigue, sealing, reflection, adhesion of microorganisms, visual and aesthetic appearance. Moreover, the surface roughness of organs, tissues, texture direction of fresh produce (i.e. lettuce, spinach) is becoming a great area of interest for biologists, food scientists, and metallurgists who seek to control persistent food outbreak over the past few years (Han et al., 2016).

Measurement and analysis of surface topography is important to all industries. Three-dimensional characterization gets more attention due to increased availability of optical, nondestructive measurement methods. Different ISO standards were established to standardize roughness parameters. (Whitehouse, 2011) and (Leach, 2013) described the physical significance of different 2D profile and 3D areal surface roughness parameters. Generally, different roughness features refer to amplitude, spatial distribution, texture direction, or pattern of surfaces (Zhou et al., 2016). Conventionally, average profile or areal roughness (i.e. Ra, Sa), average maximum height (i.e. Rz, Sz), or maximum height (Rt) of the profile are most widely used in industries in order to evaluate surface metrological features (Terry and Brown, 1997). However, Ra, Rz, or Rt only refer to amplitude variation or extreme features of surfaces but do not assess the shape of the profile, which defines functionality like bacterial retention, microbial growth, stress, etc (Asiltürk et al., 2016). Therefore, other stratified and functional parameters, like skewness (Ssk), kurtosis (Sku), load bearing area curve (BAC), volumetric ratio, or core roughness parameter (Rk), can be more useful parameters for a detailed analysis of surfaces (Raymond et al., 2016).

The aim of this paper is to evaluate stratified and functional parameters for extruded and ground surfaces comprehensively to predict texture behavior more accurately. Since advanced manufacturing processes are more and more focusing on producing smart surfaces in a cost-effective way, the second aim of this paper is to find a systematic approach to choose a few appropriate roughness parameters that can act as process monitoring indices for different manufacturing processes.

Section snippets

Background

Abrasive finishing operations, like grinding, polishing, or lapping, work with multiple cutting edges. (Kiyak and Çakır, 2007) found that different abrasive grain sizes (given as mesh numbers) and grain size distributions change surface roughness and affect functionality. (Linke, 2015) has discussed the importance of proper selection and implementation of abrasive tools for machining. (Das and Linke, 2016) has shown how the abrasive grit numbers and process parameters are used to achieve the

Workpiece materials and surface generation

Two different types of manufacturing processes i.e. extrusion and grinding were used in this paper as a case study. Three surface types were produced out of these two manufacturing processes i.e. one type is a pristine extruded surface and the other two types are ground surfaces made with 60 grit and 400 grit abrasive sanding bands respectively. A Dremel 4000 hand held power tool and resin bonded alumina sanding bands were used for fabricating the ground surfaces. All grinding operations were

Experimental results & discussion

The confocal images of extruded and ground surfaces using areal and profile analyses are shown in Fig. 3. It is obvious that the extruded surface contains a large number of grooves and pits throughout the surface. The roughness of ground surfaces produced by #60 grit is in the same range as the extruded one. But instead of inordinate pits/grooves like on the extruded surface, it contains plowed material. Textures are denser and smoother for the ground surface with the higher grit size #400.

Choosing significant roughness parameters

Advanced and sustainable manufacturing requires efficient and cost-effective measurements and prediction of surface textures to generate functional surfaces. One shortcoming of current metrology implementation is the limited availability of texture parameters. It is often quite difficult for a metrologist to choose the most appropriate and relevant texture parameters for monitoring whether the desired surface properties are met. The urgency of establishing a subset of surface parameters to

Conclusion

Electronics and automotive industries invest a lot of money to improve product quality for mass production. Therefore, implementing robust techniques for choosing correct selection parameters to categorize between good parts and bad parts is very advantageous for practical application. This paper describes the correlation between different surface roughness parameters with surface topographical features and presents a systematic approach to identify significant roughness parameters as a process

Acknowledgements

The authors want to thank the Engineering Fabrication Laboratory (EFL) and Advanced Materials Characterization and Testing (AMCaT) facilities of University of California Davis for use of their machine equipment and confocal microscope facilities. The authors express their sincere thanks to Prof. Dr.-Ing. Jörg Seewig, Professor of the University of Kaiserslautern, Germany for his extended cooperation. Part of this work evolved in collaborative research within the International Research Training

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Research PaperEvaluation and systematic selection of significant multi-scale surface roughness parameters (SRPs) as process monitoring index

Abstract

Introduction

Section snippets

Background

Workpiece materials and surface generation

Experimental results & discussion

Choosing significant roughness parameters

Conclusion

Acknowledgements

Measurement

Thin Solid Films

Wear

CIRP Ann. – Manuf. Technol.

Proc. CIRP

CIRP Ann. – Manuf. Technol.

Int. J. Mach. Tools Manuf

J. Mater. Process. Technol.

Food Control

Int. Biodeterior. Biodegrad.

Int. J. Fatigue

CIRP Ann. – Manuf. Technol.

J. Mater. Process. Technol.

Measurement

Med. Image Anal.

J. Manuf. Processes

Wear

Tribol. Int.

Corros. Sci.

LWT – Food Sci. Technol.

Research Paper
Evaluation and systematic selection of significant multi-scale surface roughness parameters (SRPs) as process monitoring index