Subhas Chandra Mondal

Modelling robustness for manufacturing processes: a critical review

‘Robustness’ is an important concept used in quality engineering for the improvement of quality in a manufacturing process. A process which is insensitive to noise variation is called a robust process. The robustness is modelled by several researchers and practioners for its design and implementation in a manufacturing process. A review of all these approaches is essential in order to assess their strengths, limitations and applicability under different process conditions and constraints. Over the years, many of these approaches have found widespread application in measuring, assessing and modelling of process robustness in manufacturing and other industries. In this paper, an attempt has been made to review critically the existing approaches as proposed and applied for measuring and evaluating robustness of manufacturing processes. Based on the critical appraisal, the key issues are identified and a generic framework for modelling and measuring of process robustness in single- and multi-stage manufacturing processes is presented.

Modelling robustness in serial multi-stage manufacturing processes

The study of robustness in single-stage manufacturing has been explored by a large number of researchers and practitioners. However, modelling of robustness in multi-stage manufacturing using multivariate data is seldom used. The aim of this paper is to develop a methodology to model process robustness in a serial multi-stage manufacturing system. Combining statistical regression, Taylor series expansion, the root-sum-squares method and a variation model, the methodology proposes a measurement system for robustness. The resulting metric, while quantifying robustness, measures absorbed and transmitted variations across the stages of a manufacturing process. Using the methodology in a serial two-stage worm gear manufacturing process, the levels of robustness and both absorbed and transmitted variations are determined, thus identifying significant variations across manufacturing stages. The details of this application with the types of corrective actions as required for minimisation of process performance deterioration are presented.

Development of a measurement metric for manufacturing process robustness

Literature on the study on manufacturing process robustness is limited. Very often, process robustness is dealt with single-stage process and considers a single response variable. Moreover, the effect of operators and equipment on the overall process robustness is seldom studied. These shortcomings have led to the development of a new measurement metric for evaluating manufacturing process robustness which is presented in this study. The proposed metric considers the effects of input raw material, process operating conditions and equipment and operator performances in its development. The metric can be used for analysing both univariate and multivariate data. Partial contribution indices and multivariate S/N ratio are also developed. Three case studies were conducted in centrifugal casting, heat treatment and forging shops. From the results, it is also clear that when the robustness of a process increases, the multivariate S/N ratio increases and the transmitted variation reduces.

Multi-objective Optimization of Welding Parameters in MMAW for Nano-structured Hardfacing Material Using GRA Coupled with PCA

Hardfacing is one of the most productive and practical approaches to cut down operating expense on the maintenance front and at the same time to improve performance and reliability of the equipment. Presence of nano-particles in hard facing materials significantly enhances surface area to volume ratio and accordingly it improves conductivity, hardness, heat and wear resistant properties. The main objective of this paper is to efficiently apply manual metal arc welding process for hardfacing of nano-structure based electrode. The most important process variables that have been considered in conducting the experiments are welding current, arc voltage and welding speed; while the response parameters include weld bead width, reinforcement and bead hardness, respectively. Taguchi’s (L25) orthogonal array has been used to perform the experimental runs. A combination of grey relational analysis coupled with principal component analysis has been applied to identify optimal settings of the input process parameters. Moreover, the exact input and output welding parameters have been examined with the help of genetic algorithm. Finally, confirmation test has also been carried out with the optimal welding process parameters to validate the experiment result.

Optimization of Process Parameters in Submerged Arc Welding Using Multi-objectives Taguchi Method

Submerged arc welding (SAW) is one of the oldest automatic welding processes to provide high quality of weld. The quality of weld in SAW is mainly influenced by independent variables such as welding current, arc voltage, welding speed, and electrode stick out. The prediction of process parameters involved in SAW is very complex process. Researchers attempted to predict the process parameters of SAW to get smooth quality of weld. This paper presents an alternative method to optimize process parameters of SAW of IS: 2062, Gr B mild steel with multi-response characteristics using Taguchi’s robust design approach. Experimentation was planned as per Taguchi’s L8 orthogonal array. In this paper, experiments have been conducted using welding current, arc voltage, welding speed, and electrode stick out as input process parameters for evaluating multiple responses namely weld bead width and bead hardness. The optimum values were analyzed by means of multi-objective Taguchi’s method for the determination of total normalized quality loss (TNQL) and multi-response signal-to-noise ratio (MRSN). The optimum parameters for smaller bead width and higher bead hardness are weld current at low level (12.186 A), arc voltage at low level (12.51 V), welding speed at low level (12.25 mm/min), and electrode stick out at low level (12.29 mm). Finally, confirmation experiment was carried out to check the accuracy of the optimized results.

A methodology for modeling and monitoring of centrifugal casting process

Purpose – The purpose of this paper is to build a monitoring scheme in order to detect and subsequently eliminate abnormal behavior of the concerned casting process so as to produce worm wheels with good quality characteristics. Design/methodology/approach – In this a study, a process monitoring strategy has been devised for a centrifugal casting process using data-based multivariate statistical technique, namely, partial least squares regression (PLSR). Findings – Based on a case study, the PLSR model constructed for this study seems to mimic the actual process quite well which is evident from the various performance criteria (predicted and analysis of variance results). Practical implications – The practical implication of the study involves development of a software application with a back-end database which would be interfaced with a computer program based on PLSR algorithm for estimation of model parameters and the control limit for the monitoring chart. It would help in easy and real-time detection ...

Investigation of electro-thermal property of Cu-MWCNT-coated 316L stainless steel

ABSTRACT Stainless steel is a material with high toughness, high ductility and easy formability but it suffers from low electrical and thermal conductivity. In this paper, an attempt has been made for deposition of copper-based multi-walled carbon nanotubes (MWCNTs) composite coatings on the surface of stainless steel plate by the direct current electrodeposition method to enhance the electrical and thermal properties. Cetyl tri-methyl ammonium bromide, a cationic dispersing agent, is applied in the electrolytic bath to prevent aggregation of carbon nanotubes. The microstructure and compositional analysis of the developed Cu-MWCNT coating were investigated by scanning electron microscopy and energy-dispersive X-ray spectroscopy. The electrical conductivity of Cu-MWCNT composite-coated stainless-steel specimen is found to be 183% greater than the uncoated stainless-steel specimen and 58% high compared with the copper-coated stainless steel specimen. Thermal conductivity of Cu-MWCNT composite-coated stainless-steel specimen also improved compared with copper-coated and uncoated stainless-steel.

Optimisation of wire electric discharge machining process: a review and reflection

Wire electric discharge machining (WEDM) process is a potential electro-thermal non-conventional machining process which is useful for machining difficult-to-cut electrically conductive materials. In addition, the development of newer and more exotic materials has challenged the viability of the WEDM process in the future manufacturing environment. Hence, continuous improvement needs to be made to the current WEDM in order to extend the machining capability and increase the productivity and efficiency. Optimisation methods in machining processes, considered being a vital tool for continual improvement of output quality in products and processes. This paper presents a review of several modelling and optimisation techniques in wire electric discharge machining (WEDM). A generic framework for optimisation of process parameters in WEDM process is also suggested for the selection of appropriate WEDM process parameters and improvement of the process performance. The paper also discusses the future trend of research work in the same area.

Process Capability — A Surrogate Measure of Process Robustness: A Case Study

A robust process is insensitive to the effect of noise variables. Noise variables are the main source for producing variation. Noise variables are included in the outer array in robust design experiment for enhancing robustness. The approach of robust design is to make the process robust (insensitive) to variation due to noise variables. The effect of noise factors can be modelled in a response surface model which helps to determine the settings of the design factors that neutralize the effects of the noise factors and improve robustness. In experimental design the noise factors are assumed fixed value whereas in real world manufacturing noise factors vary randomly. Again for a large scale manufacturing, it is extremely difficult to study robustness using experimentation as there are chances of stoppage of production. In such a situation a simulation-based model can be developed using industrial data to study robustness of a real manufacturing process. This paper proposed a method (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant. The process capability indices (both univariate and multivariate) are determined based on the model responses. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart). The results show that noise factors in hardening and tempering process are insensitive to manufacturing variation and process capability indices act as a surrogate measure of process robustness.Copyright

Statistical Analysis and Optimization of Process Parameters in Wire Cut Machining of Welded Nanostructured Hardfacing Material

Wire electric discharge machining (WEDM) is a nontraditional machining technique to cut hard and conductive material with the assistance of a moving electrode. Nanostructured hardfacing material is a hard alloy with high hardness and wear resisting property. The motivation behind this research is to explore the impact of parameters on material removal rate, surface roughness and machining time for WEDM using welded nanostructured hardfacing material as work piece. The hardfacing layer was prepared by manual metal arc welding (MMAW). Taguchi’s L25 orthogonal array was utilized to design the investigational runs. Different hardfaced layer thicknesses were examined to bring out the influence of hardfacing on WEDM performances. Moreover, Multi-objective optimization was carried out using TOPSIS and PCA to recognize optimal process parameters. Optimum combination of input process parameters for the multiple performance characteristics should be preferred as A1B5C5D5E5 (brass wire) and A2B3C4D5E1 (Zinc coated brass wire).