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The purpose of this paper is to develop a mathematical model for delamination during drilling by using a response surface methodology (RSM) and also to determine how the input parameters (tool diameter, spindle speed and feed rate) influence the output response (delamination) in machining of fiber metal laminates.

Three factors and a three-level central composite design in RSM are used to carry out the experimental investigation. A video measuring system is used to measure the width of maximum damage of the machined FML composite. The “Design Expert 7.0” is used to analyze the data collected graphically. Analysis of variance is carried out to validate the model and for determining the most significant parameter.

The response surface model is used to predict the input factors influencing the delamination on the machined surfaces of the ARALL composite at different cutting conditions with the chosen range of 95 percent confidence intervals. Analysis of the influences of entire individual input machining parameters on the delamination has been carried out using RSM.

The effect of delamination on drilling of ARALL composites with solid carbide tools of various diameters has not been analyzed yet using RSM.

The purpose of this paper is to optimise the process parameters, namely, fibre orientation angle, helix angle, spindle speed, and feed rate in milling of glass fibre-reinforced plastic (GFRP) composites using grey relational analysis (GRA) and desirability function analysis (DFA).

In this work, experiments were carried out as per the Taguchi experimental design and an L27 orthogonal array was used to study the influence of various combinations of process parameters on surface roughness and delamination factor. As a dynamic approach, the multiple response optimisation was carried out using GRA and DFA for simultaneous evaluation. These two methods are best suited for multiple criteria evaluation and are also not much complicated.

The process parameters were found optimum at a fibre orientation angle of 15°, helix angle of 25°, spindle speed of 6,000 rpm, and a feed rate of 0.04 mm/rev. Analysis of variance was employed to classify the significant parameters affecting the responses. The results indicate that the fibre orientation angle is the most significant parameter preceded by helix angle, feed rate, and spindle speed for GFRP composites.

An attempt to optimise surface roughness and delamination factor together by combined approach of GRA and DFA has not been previously done.

The purpose of this paper is to develop a mathematical model for delamination through response surface methodology (RSM) and analyse the influences of the entire individual input machining parameters (cutting speed, depth of cut and feed rate) on the responses in milling of glass fibre reinforced plastics (GFRP) composites with solid carbide end mill cutter coated with polycrystalline diamond (PCD).

Three factors, three levels face-centered central composite design matrix in RSM is employed to carry out the experimental investigation. Shop microscope is used to examine the delamination of GFRP composites. The “Design Expert 8.0” software was used for regression and graphical analysis of the data collected. Analysis of variance is used to check the validity of the model and for finding the significant parameters.

The developed second-order response surface model is used to calculate the delamination of the machined surfaces at different cutting conditions with the chosen range of 95 per cent confidence intervals. Analysis of the influences of the entire individual input machining parameters on the delamination has been carried out using RSM.

Influence of solid carbide end mill coated with PCD on delamination of bi-directional GFRP composite during milling has not been analysed yet using RSM.

The primary aim of this article is to ascertain the modalities of leveraging Lean Six Sigma (LSS) for Industry 4.0 (I4.0) with special reference to the process industries. Moreover, it intends to determine the applicability of simulation-based LSS in the automation of the mineral water industry, with special emphasis on the robust design of the control system to improve productivity and performance.

This study adopts the action research methodology, which is exploratory in nature along with the DMAIC (define-measure-analyze-improve-control) approach to systematically unearth the root causes and to develop robust solutions. The MATLAB simulation software and Minitab statistical software are effectively utilized to draw the inferences.

The root causes of critical to quality characteristic (CTQ) and variation in purity level of water are addressed through the simulation-based LSS approach. All the process parameters and noise parameters of the reverse osmosis (RO) process are optimized to reduce the errors and to improve the purity of the water. The project shows substantial improvement in the sigma rating from 1.14 to 3.88 due to data-based analysis and actions in the process. Eventually, this assists the management to realize an annual saving of 20% of its production and overhead costs. This study indicates that LSS can be applicable even in the advent of I4.0 by reinforcing the existing approach and embracing data analysis through simulation.

The limitation of this research is that the inference is drawn based on a single case study confined to process industry automation. Having said that, the methodology deployed, scientific information related to optimization, and technical base established can be generalized.

This article is the first of its kind in establishing the integration of simulation, LSS, and I4.0 with special reference to automation in the process industry. It also delineates the case study in a phase-wise manner to explore the applicability and relevance of LSS with I4.0. The study is archetype in enabling LSS to a new era, and can act as a benchmark document for academicians, researchers, and practitioners for further research and development.