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Defective parts in manufacturing is a serious issue faced by every manufacturer. Even after proper care in design, material selection and manufacturing of product, there exists a defective part. The purpose of this paper is to explore the quality of the manufacturing, and find the use of effective quality tools to reduce the part defect rate in an electrical parts manufacturing unit, thereby, reducing the replaced cost of defective parts.

With the help of quality initiatives, like total quality management (TQM) and Lean Six Sigma (LSS), the firms can produce quality product in each stage of production. The paper focuses on the primary data collected from the XYZ electric manufacturer.

The main finding of this case analysis is that by the effective use of quality tools, the defective part return rate can be reduced, because of which the firm can observe reduction in replaced cost of almost INR24 lakh. In addition, 10A switch part contributes more in replacement cost. Further, it adds to the 35 percent of the overall part rejection.

The study is more focused on particular type of switch product and can extend to other types of products. In addition, the analysis reveals the results of only 88 percent of the defective products.

The study provides results of the improved quality by effective use of quality tools and discusses the different types of defects in the electrical parts manufacturing. Introducing TQM and LSS to manufacturing can reduce the customer return rate to 1,300 parts per million (PPM) and even to 1,000 PPM in future.

The paper discusses the quality issues in the electrical manufacturer. Moreover, the case analysis briefs effective ways to improve the product quality and reduce the rejection rate.

This study aims to utilize DMAIC methodology along with value stream mapping and other Lean Six Sigma tools in a major automobile light manufacturing industry to reduce defect rates and increase production capacity in their manufacturing line. The study also proposes a modified framework based on lean principles and FlexSim to identify and reduce waste in the selected industry.

A Lean Six Sigma modified framework has been deployed with DMAIC to reduce the defect rate and increase the production rate. Various tools like value stream mapping, brainstorming, Pareto charts, 5S, kanban, etc. have been used at different phases of DMAIC targeting wastes and inventory in the production line. Also, a simulation model has been utilized for the automobile light manufacturing industry to improve the machine utilization time with varying batch sizes.

The results of the study indicated a 53% reduction in defect rates. Thus, there would be an expected improvement in sigma value from 3.78 to 3.89 and a reduction in defects per million opportunities (DPMO) from 11,244 to 8,493. Additionally, simulation model using FlexSim was developed, and the optimum ordering batch size of raw material was obtained. It was also analyzed that idle time for various stations could be reduced by up to 30%.

The utilized framework helps identify defects for managers to increase production efficiency. The workers, operators and supervisors on the production line also need to be trained regularly for identifying the areas of improvement.

The modified Lean Six Sigma framework used in this study includes FlexSim simulation to make the framework robust, which has not been used with LSS tools in the literature studied. Also, the LSS finds very less application in the manufacturing domain, considering which this study tends to add value in existing literature taking a case of an automobile light manufacturing industry.

This article targeted to experimentally examine the impact of several considered process parameters namely, applied voltage (AV), tool feed rate, electrolyte concentration and pulse frequency (PF), on the material removal rate (MRR) and radial overcut (ROC) while performing shaped tube drilling of aviation grade Inconel 625 super alloy through electrochemical machining principle. Further, an attempt has also been made to develop mathematical models for the process responses along with advanced optimization with evolutionary methods.

The central composite rotatable design matrix was used to scheme out the experiments in the present study. The consistency and accuracy of the developed mathematical models were confirmed through statistical results. Additionally, a field emission scanning electron microscope analysis was conducted to assess and analyze the microstructure of the machined work samples. The study also seeks to optimize the selected process inputs for MRR and ROC through the implementation of the desirability method, particle swarm optimization (PSO) and Teaching Learning-Based Optimization (TLBO).

The ROC is significantly influenced by the input parameters, specifically the PF and AV. Less ROC values were observed when the high PF with moderate AV. The minimum and maximum values of ROC and MRR were obtained as; 135.128 µm and 380.720 µm; 1.37 mg/min and 2.3707 mg/min, correspondingly. The best optimized confirmatory results were obtained through the TLBO approach, with an MRR value of 3.1587 mg/min and a ROC of 71.9629 µm, in comparison to the PSO and desirability approaches.

The various challenges associated with the productive machining of aviation grade Inconel 625 superalloy have been explored experimentally. The conducted experimentation has been performed on the in-house fabricated micro-electrochemical setup capable of performing a variety of advanced machining operations at the miniaturized level. Further, the application of shaped tube drilling while processing aviation grade Inconel 625 superalloy has been explored with the developed micro-ECM set-up. Moreover, the performed microstructure analysis of the machined work samples has elaborated and explored the various associated surface integrity aspects which are quite crucial when it comes to real-life aerospace-related applications. The utility of designed experiments has further made the attempted experimental analysis more fruitful and qualitative too.

A nut bolt joint is a primary device that connects mechanical components. The vibrations cause bolted joints to self-loosen. Created by motors and engines, leading to machine failure, and there may be severe safety issues. All the safety issues and self-loosen are directly and indirectly the functions of the accuracy and precision of the fabricated nut and bolt. Recent advancements in three-dimensional (3D) printing technologies now allow for the production of intricate components. These may be used technologies such as 3D printed bolts to create fasteners. This paper aims to investigate dimensional precision, surface properties, mechanical properties and scanning electron microscope (SEM) of the component fabricated using a multi-jet 3D printer.

Multi-jet-based 3D printed nut-bolt is evaluated in this paper. More specifically, liquid polymer-based nut-bolt is fabricated in sections 1, 2 and 3 of the base plate. Five nuts and bolts are fabricated in these three sections.

Dimensional inquiry (bolt dimension, general dimensions’ density and surface roughness) and mechanical testing (shear strength of nut and bolt) were carried out throughout the study. According to the ISO 2768 requirements for the General Tolerances Grade, the nut and bolt’s dimensional examination (variation in bolt dimension, general dimensions) is within the tolerance grades. As a result, the multi-jet 3D printing (MJP)-based 3D printer described above may be used for commercial production. In terms of mechanical qualities, when the component placement moves from Sections 1 to 3, the density of the manufactured part decreases by 0.292% (percent) and the shear strength of the nut and bolt decreases by 30%. According to the SEM examination, the density of the River markings, sharp edges, holes and sharp edges increased from Sections 1 to 3, which supports the findings mentioned above.

Hence, this work enlightens the aspects causing time lag during the 3D printing in MJP. It causes variation in the dimensional deviation, surface properties and mechanical properties of the fabricated part, which needs to be explored.

This work aims to describe the face milling analysis on Inconel X-750 superalloy using coated carbides. The formed chips and tool wear were further analyzed at different cutting parameters. The various impact of cutting parameters on chip morphology was also analyzed. Superalloys, often referred to as heat-resistant alloys, have exceptional tensile, ductile and creep strength at high operating temperatures and good fatigue strength, and often better corrosion and oxidation resistance at extreme heat. Because of these qualities, these alloys account for more than half of the weight of sophisticated aviation, biomedical and thermal power plants today. Inconel X-750 is a high-temperature nickel-based superalloy that is hard to machine because of its extensive properties. At last, the discussion regarding the tool wear mechanism was analyzed and discussed in this article.

The machining parameters for the study are cutting speed, feed rate and depth of cut. One factor at a time approach was implemented to investigate the effect of cutting parameters on the cutting forces, surface roughness and material removal rate. The scatter plot was plotted between cutting parameters and target functions (cutting forces, surface roughness and material removal rate). The six levels of cutting speed, feed rate and depth of cut were taken as cutting parameters.

The cutting forces are primarily affected by the cutting parameters, tool geometry, work material etc. The maximum forces Fx were encountered at 10 mm/min cutting speed, 0.15 mm/rev feed rate and 0.4 mm depth of cut, further maximum forces Fy were attained at 10 mm/min cutting speed, 0.25 mm/rev feed rate and 0.4 mm depth of cut and maximum forces Fz were attained at 50 mm/min cutting speed, 0.05 mm/rev feed rate and 0.4 mm depth of cut. The maximum surface roughness value was observed at 40 mm/min cutting speed, 0.15 mm/rev feed rate and 0.5 mm depth of cut.

The effect of machining parameters on cutting forces, surface roughness, chip morphology and tool wear for milling of Inconel X-750 high-temperature superalloy is being less researched in the present literature. Therefore, this research paper will give a direction for researchers for further studies to be carried out in the domain of high-temperature superalloys. Furthermore, the different tool wear mechanisms at separate experimental trials have been explored to evaluate and validate the process performance by conducting scanning electron microscopy analysis. Chip morphology has also been evaluated and analyzed under the variation of selected process inputs at different levels.

The purpose of this study is to find the best geometries among the cylindrical, enamel and honeycomb geometries based upon the mechanical properties (tensile test, compression test and shear test). Further this obtained geometry could be used to fabricate products like exoskeleton and its supporting members.

The present research focuses on the mechanical testing of cylindrical, enamel and honeycomb-shaped parts fabricated through multi-jet printing (MJP) process with a wall thickness of 0.26, 0.33, 0.4 and 0.66 mm. The polymer specimens (for tensile, compression and shear tests) were fabricated using a multi-jet fusion process. The experimental results were compared with the numerical modelling. Finally, the optimal geometry was obtained, and the influence of wall thicknesses on various mechanical properties (tensile, compression and shear) was studied.

In comparison to cylindrical, enamel structures the honeycomb structures required less time to fabricate and had lower tensile, compressive and shear strengths. The most efficient geometry for fully functional parts where tensile, compressive and shear forces are present during application – cylindrical geometry is preferred followed by enamel, and then honeycomb. It was found that as the wall thickness of various geometries was increased, their ability to withstand tensile, compressive and shear loads also enhanced. The enamel shape structure exhibits greater strain energy storage capacity than other shape structures for compressive loads, and the strength to resist the compressive load will be lower. In the case of cylindrical geometries for tensile loading, the resisting area toward the loading will be higher in comparison to honeycomb- and enamel-based structures. At the same time, the ability to store the stain energy is less. The results of the tensile, compression and shear load finite element analysis using ANSYS are in agreement with those of the experiments.

From the insight of literature review, it is found that a wide range of work is done on fused deposition modeling (FDM) process. But in comparison to FDM, the MJP provide the better dimensional accuracy and surface properties (Lee et al., 2020). Therefore, it is observed that past research works not incorporated the effect of wall thickness of the embedded geometries on mechanical properties of the part fabricated on MJP (Gibson, n.d.). Hence, in this work, effect of wall thickness on tensile, compression and shear strength is considered as the main factor for the honeycomb, enamel and cylindrical geometries.

This study aims to experimentally investigate the influence of considered process parameters, i.e. pulse on time, pulse off time, peak current and gap voltage, on tool wear rate (TWR) in electrical discharge machining (EDM) of iron (Fe)-based shape memory alloy (SMA) through designed experiments. The parametric optimization for TWR has also been attempted using the desirability approach and genetic algorithm (GA).

The response surface methodology (RSM) in the form of Box–Behnken design has been used to scheme out the experiments. The influence of considered process inputs has also been observed through variance analysis. The reliability and fitness of the developed mathematical model have been established with test results. Microstructure analysis of machined samples has also been evaluated and analyzed using a scanning electron microscope (SEM). SEM images revealed the surface characteristics such as micro-cracks, craters and voids on the tool electrode surface. SEM images provide information about the surface integrity and type of wear on the surface of the tool electrode.

The input parameters, namely, pulse on time and pulse off time, are major influential factors impacting the TWR. High TWR has been reported at large pulse on time and small pulse off time conditions whereas higher TWR is reported at high peak current input settings. The maximum and minimum TWR values obtained are 0.073 g/min and 0.017 g/min, respectively. The optimization with desirability approach and GA reveals the best parametric values for TWR i.e. 0.01581 g/min and 0.00875 g/min at parametric combination as pulse on time = 60.83 µs, pulse off time = 112.16 µs, peak current = 18.64 A and gap voltage = 59.55 V, and pulse on time = 60 µs, pulse off time = 120 µs, peak current = 12 A and gap voltage = 40 V, correspondingly.

Proposed work has no limitations.

SMAs have been well known for their superior and excellent properties, which make them an eligible candidate of paramount importance in real-life industrial applications such as orthopedic implants, actuators, micro tools, stents, coupling, sealing elements, aerospace components, defense instruments, manufacturing elements and bio-medical appliances. However, its effective and productive processing is still a challenge. Tool wear study while processing of SMAs in EDM process is an area which has been less investigated and of major concern for exploring the various properties of the tool and wear in it. Also, the developed mathematical model for TWR through the RSM approach will be helpful in industrial revelation.

The present study aims to explore the barriers to Lean Six Sigma (LSS) implementation in the healthcare sector and develop the ranking of finalized barriers using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach under a fuzzy environment.

The LSS barriers are identified through the literature review and validated by the expert's opinion and statistical analysis. A total of 124 experts were identified through the purposive sampling method for conducting this study. A questionnaire survey method is used to collect the data related to identified LSS barriers in the healthcare sector. The screened barriers are ranked through the Fuzzy DEMATEL approach.

In this study, a total of 21 barriers were identified with the help of a systematic literature review and screened 13 significant barriers by the expert opinions of healthcare personnel. The result reveals that “Lack of top management commitment and support, lack of awareness about LSS”, “resistance to culture change and inadequate resources emerges as the most critical barriers”. The prioritization of barriers facilitates the managers to make effective policies and guidelines for LSS implementation in healthcare organizations.

To avoid LSS implementation failure, the practitioners and researchers need to focus on LSS barriers as per suggested ranking more conventionally and make plans and adoption policies accordingly.

This study is unique in terms of investigation and empirical analysis of LSS implementation barriers in the healthcare sector in the Indian context. The outcomes of the present study will help the managers of healthcare organizations to make the strategies and policies for LSS implementation as per the recommended LSS barriers.

This study aims to explore the potential application of blockchain technology in Lean Six Sigma (LSS) project through a proposed blockchain-LSS (BLSS) model. The proposed model can tackle real-time problems in information sharing, transparency and traceability in every stage of the LSS project.

The scoping review approach is used to develop the integrated model of the BLSS approach for operational excellence. The proposed model is validated through expert’s input, which is collected by a questionnaire survey method.

The prime function of the proposed BLSS model is the information sharing among the project team and real-time monitoring, transparency, traceability and immutability in the Define-Measure-Analyze-Improve-Control phase. The proposed model also consists the information about the role of blockchain features at each phase of the LSS project. The project team and industry employees can trace the success of the project at every moment, resulting in trust buildup and the elimination of fake data. Moreover, there would be no disputes among various sections/shops of the plant and employees to share the real information.

This paper provides guidelines to practitioners and managers for integrating the LSS approach and blockchain. The blockchain helps managers and practitioners in better data traceability and transparency, monitoring of data as well as more sustainable LSS project management.

To the best of the authors’ knowledge, this is the first research attempt that developed an integrated model of blockchain and LSS approach to maintaining the immutable records of assets in projects and targeted Industry 4.0.

The purpose of this paper is to investigate the barriers of Lean Six Sigma (LSS) and develop the interrelationship among them using interpretive structural modelling (ISM) and Matriced Impact Croises Multiplication Appliquee a un Classement (MICMAC).

Using systematic literature review and expert’s opinions, 26 LSS barriers have been extracted and finalized through statistical analysis, that is importance-index analysis and corrected item minus total correlation methods. The statistical analysis of purified 22 LSS barriers has been carried out and consistency of finalized barriers has been checked through reliability statistical test in Statistical Package for the Social Sciences software. Finally, the contextual relationship among finalized LSS barriers is developed using ISM and MICMAC approach.

The ISM model indicates that insufficient management commitment and involvement, lack of resources, lack of training and education, lack of strategic thinking, lack of training funds are strategic factors; improper project selection, poor selection of employee for belt training, lack of total employee involvement, lack of awareness of about LSS are prudent factors; unclear vision, high implementation cost, resistance to culture change, weak supplier linkage, poor alignment between company’s goal and customer demand are burst factors. Furthermore, MICMAC analysis is splitting the LSSBs in four clusters according to their driving power and dependency. These results provide a clear mind-set to engineering manager for focusing more on LSS barriers according to their driving power and dependency.

There may be biasness in making pairwise comparison matrix of barriers due to involvement of expert’s opinion as human error.

The outcome of this paper provides robust practical implication for LSS researchers and practitioners. The researcher and practitioners must consciously concentrate on the identified LSSBs more conventionally during LSS implementation, and they need to plan strategically to avoid any implementation failure.

For successful implementation of LSS in any organization, it is necessary and permeable to make strategy for controlling LSS barriers at initial stage. So this paper is a leading attempt to highlight main LSS barriers and interrelate them using ISM and MICMAC approach. It provides a clear path for tackling LSS barriers to engineering managers, researchers and consultants.