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Due to significant requirement of energy, water, material, and other resources, the manufacturing industries significantly impact environmental, economic, and social dimensions of sustainability (triple bottom-line). In response, today's research is focused on finding solution towards sustainable manufacturing. In this regard, sustainability assessment is an essential strategy. In the past, a variety of tools was developed to evaluate the environmental dimension. Because of this fact, previous review studies were grounded mostly on tools for green manufacturing. Unlike previous review articles, this study was aimed to review and analyze the emerging sustainability assessment methodologies (published from 2010 to 2020) for manufacturing while considering the triple bottom-line concept of sustainability. In this way, the paper presents a decade review on this topic, starting from 2010 as the guidelines for the social dimension became available in 2009. This paper has analyzed various methods and explored recent progress patterns. First, this study critically reviewed the methods and then analyzed their different integrating tools, sustainability dimensions, nature of indicators, difficulty levels, assessment boundaries, etc. The review showed that life cycle assessment and analytic hierarchy process-based approaches were most commonly used as integrating tools. Comparatively, still, environmental dimension was more commonly considered than economic and social dimensions by most of the reviewed methods. From indicators' viewpoint, most of the studied tools were based on limited number of indicators, having no relative weights and validation from the experts. To overcome these challenges, future research directions were outlined to make these methods more inclusive and reliable. Along with putting more focus on economic and social dimensions, there is a need to employ weighted, validated, and applicable indicators in sustainability assessment methods for manufacturing.

Research shows that mechanical properties of parts produced using fused deposition modeling (FDM) are inferior when compared to parts produced using conventional techniques such as injection molding. Efforts have been made in recent years to improve mechanical properties by reinforcing the parts with high strength fibers. This has been achieved by either modifying FDM setups to extrude fibers with thermoplastics and fabricate continuous fiber reinforced thermoplastic composites (CFRTPCs) or employing manual techniques subsequent to part production. Existing CFRTPCs fabrication procedures have limitations of fiber exposure to environment, no direct control method for volume fraction, and poor surface finish. This research work is focused on improving the process of producing CFRTPCs by addressing these limitations using a dual extruder FDM setup. The process developed was tested for its feasibility using Kevlar fiber as reinforcement for commercially available ABS, PLA, PLA-C, and PLA-Cu thermoplastic fibers. Taguchi L16 orthogonal array was used to design experiments, while tensile and flexural testing was performed to determine mechanical properties achieved. Tensile strength was improved up to 3 times the baseline value of thermoplastics, while flexural strength was improved up to 1.6 times. This technique can further the goal of developing CFRTPCs, on industrial level, using FDM with better control over volume fraction and fiber layup.

In modern manufacturing industries, the importance of multi-objective optimization cannot be overemphasized particularly when the desired responses are differing in nature towards each other. With the emergence of new technologies, the need to achieve overall efficiency in terms of energy, output, and tooling is on the rise. Resultantly, endeavor is to make the machining process sustainable, productive, and efficient simultaneously. In this research, the effects of machining parameters (feed, cutting speed, depth of cut, and cutting condition including dry, wet, and cryogenic) were analyzed. Since sustainable production demands a balance between production quality and energy consumption, therefore, response parameters including specific cutting energy, tool wear, surface roughness, and material removal rate were considered. Taguchi-gray integrated approach was adopted in this study. Multi-objective function was developed using gray relational methodology, and its regression analysis was conducted. Response surface optimization was carried out to optimize the formulated multi-objective function and derive the optimum machining parameters. Concurrent responses were optimized with best-suited values of input parameters to make the most out of the machining process. Analysis of variance results showed that feed is the most effective parameter followed by cutting condition in terms of overall contribution in multi-objective function. The proposed optimum parameters resulted in improvement of tool wear and surface roughness by 30% and 22%, respectively, whereas specific cutting energy was reduced by 4%.

Continuous fibre-reinforced composites have significant industrial importance and usage. However, they are limited by design considerations and high-cost manufacturing operations. This article presents a way forward to utilize Fused Deposition Modelling – a 3D printing technique – to manufacture continuous carbon fibre-reinforced thermoplastics. Several parameters including number of reinforced layers, material impact and interlayer gap have been investigated and optimized using response surface method. Successful incorporation of modified novel nozzle design in a dual nozzle setup resulted in the realization of controlled manufacturing of continuously reinforced composites leading to reinforced yet smooth surface finished samples. Several samples were made, and mechanical testing, parameter optimization, strength calculations and fracture analysis were carried out. For polylactic acid (PLA), tensile strength of 112 MPa and flexural strength of 164 MPA were achieved – an almost 3 times increase from pure PLA printing. The approach presented in this article can forward continuous fibre-reinforced composites for industrial usage with its controlled fibre layup and programmable thread orientation features.

Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system&rsquo, s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode.

Comfortable and conducive class environment is very important for students and teachers for effective teaching and learning. Good interaction between teachers and students create positive relationships in classroom and contribute to effective learning. The purpose of this study was to develop a methodology for evaluating thermal comfort, ergonomic and human factors effect on learning performance in university classrooms. Conceptual framework is constructed, and study methodology is developed based on literature.

Productivity and economy are key elements of any sustainable manufacturing system. While productivity is associated to quantity and quality, economy focuses on energy efficient processes achieving an overall high output to input ratio. Machining of hard-to-cut materials has always posed a challenge due to increased tool wear and energy loss. Cryogenics have emerged as an effective means to improve sustainability in the recent past. In the present research the use of cooling conditions has been investigated as an input variable to analyze its effect on tool wear, specific cutting energy and surface roughness in combination with other input machining parameters of feed rate, cutting speed and depth of cut. Experimental design was based on Taguchi design of experiment. Analysis of Variance (ANOVA) was carried out to ascertain the contribution ratio of each input. Results showed the positive effect of coolant usage, particularly cryogenic, on process responses. Tool wear was improved by 33 % whereas specific cutting energy and surface roughness were improved by 10 % and 9 % respectively by adapting the optimum machining conditions.

The use of titanium based alloys in aerospace and biomedical applications make them an attractive choice for research in micro-machining. In this research, low speed micro-milling is used to analyze machinability of Ti-6Al-4V alloy as low speed machining setup is not expensive and it can be carried out on conventional machine tools already available at most machining setups. Parameters like feed per tooth, cutting speed and depth of cut are selected as machining variables and their effect on burr formation is analyzed through statistical technique analysis of variance to determine key process variables. Results show that feed per tooth is the most dominant factor in burr formation (81 % contribution ratio). The effect of depth of cut was found to be negligible. It was also observed that micro-milling at optimum process parameters showed minimum burr formation. In terms of burr formation, as compared to high speed machining setup, better results were achieved at low speed machining setup by varying machining parameters.

Lung cancer has a five-year survival rate of 17.7% which increases to 54.4% when it is diagnosed at early stages. Automated detection techniques have been developed to detect and diagnose nodules at early stages in computer tomography (CT) images. This paper presents a systematic analysis of the recent nodules detection techniques with the goal to summerize current trends and future challenges. The relevant papers are selected from IEEEXplore, science direct, PubMed, and web of science databases. Each paper is critically reviewed in order to summarize its methodology and results for further analysis. Our analyses reveal that several methods show potential progress in the field but still require an improvement to overcome many challenges like, high sensitivity with low false positive (FP) rate, detection of different nodules based on their size, shape, and positions, integration with electronic medical record (EMR) and picture archiving and communication system (PACS), and providing robust techniques that are successful across different databases. To overcome these challenges and developing a robust computer aided detection (CADe) system, it is believed that collaborative work is required among the developers, clinicians and other relating parties in order to understand particular issues and needs of a CADe system and develop automatic techniques to overcome these challenges with high processing speed, low cost of implementation and with software security assurance.

In view of the current error separation method cannot successfully separate the dissimilar systematic error components from the whole surface machining data, a method for machining errors decomposition based on blind signal separation is proposed which can distinguish those dissimilar systematic error components. Firstly, the error transfer model to describe the errors synthesizing from the single error component caused by corresponding individual error source to the synthesis machining surface error is conducted. To determine the number of the systematic error components, the principal component analysis (PCA) method is used. Then according to the theory of blind source separation, negative entropy based fixed point algorithm is proposed to fulfill the machining error components separation, which can realize the separation of the systematic error components even in close frequency scale. Finally, a shaft surface finish turning data and a flat surface milling data are used as examples to verify the proposed method. The result shows that the proposed error separation method can effectively realize the machining error separation in close frequency scale.

Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain.

In engineering optimization, surrogate model (SM) is widely used to replace the involved time expensive model, due to the expensive model is complex and high precise requirement caused a long calculation cycle. In traditional process of engineering optimization, the separation of the surrogate model static construction stage and dynamic optimization stage depresses the optimization accuracy and efficiency. Moreover, in order to ensure the accuracy of the surrogate model, expensive model had to be intensively invoked to get enough representative samples in the design space for the SM training. In this paper, a surrogate model adjoint refine based global optimization method combining with the multi-stage fuzzy clustering space reduction strategy (MFCPR-SGO) is proposed to improve the optimization accuracy and efficiency. Firstly, the optimal Latin hypercube design method (OLHD) is used to sample in design space to assure the initial sample set with strong space filling property. Then, the design space is subdivided into three tiered subspaces by using the space reduction strategy of multi-stage fuzzy clustering, which has the ability of space focusing, space reduction and jumping out of local optimum. On this basis, the hierarchical optimization method with ADAM gradient descent is proposed to quickly and accurately search the local minimum value of the objective function in each subspaces. At the same time, combined with the extremum sampling and the gaussian process sampling, a dynamic sampling algorithm is given to realize the synchronization of optimization and surrogate model update. Finally, the benchmark test problems in 12 different dimensions are used to verify the proposed method. The results show that the optimization accuracy can be improved by 21.3% and expensive model invoking times are reduced by 31.5% compared with other three heuristic optimization methods and the three recent surrogate-based optimization (SGO) algorithms. It indicated that the optimization precision and efficiency can be greatly improved by synchronizing the dynamic updating of the surrogate model with the engineering optimization search.

Incremental Sheet Forming (ISF) is a novel sheet forming process. Being characterized by severe deformation, the process is likely to induce high residual stresses in the sheet metal components. These stresses in turn can affect the lifetime and integrity of the components. The present study, therefore, systematically analyzes the state of in-plane residual stresses as the function of technological parameters. The Cu/Steel bonded laminates are deformed and the hole drill tests are performed to determine the residual stresses in the laminates. The results show that the in-plane principal residual stresses are compressive in nature. Further, their magnitude decreases from one end to the other end of thickness section (i.e., from contact surface to non-contact surface). The in-plane maximum residual shear stress is found to be a way smaller than either of the in-plane principal residual stresses. The Analysis of Variance is performed to identify the role of technological parameters on the state of residual stresses. It is found that the tool diameter and wall angle are the most influential parameters while the rotation is the least influential parameter in this regard. The detailed analysis of response surfaces reveal that the combination of low diameter, high angle, middle step size, medium rotation, high feed rate and rolled condition of material (i.e., no annealing) promote the development of higher residual stresses. The relationship between the equivalent residual stress and the post-ISF yield stress shows that the former quantity is generally smaller than the latter one. Finally, empirical models are proposed to predict the state of in-plane principal residual stresses in the Cu/Steel components produced through the ISF process.

Aiming at providing an effective mechanism for representation, organization, and reuse of fixture design knowledge, this paper puts forward an intelligent methodology for fixture design based on knowledge component technology. First, the ontology models of fixture elements, the workpiece, and the fixture design case are constructed to organize the statical design knowledge related to each of them. Then, the fixture design process-related knowledge is modeled by knowledge components corresponding to fixture layout planning process and fixture structure design process. Hence, the hierarchy structure of the fixture design knowledge reuse is formed. Under the framework of the fixture layout knowledge component, the association analysis method is used for generating reasoning rules and then the rule-based reasoning (RBR) method is used for the determination of the workpiece-locating mode and the initiative candidate of the locating datum. Further, an entropy weight fuzzy comprehensive evaluation method is employed to select the most suitable locating datum from the candidates. Under the framework of the fixture structure design knowledge component, the selection of fixture elements, element dimension drive, and auto-assemble are fulfilled by the combination of RBR and case-based reasoning (CBR). The method realizes the effective organization and efficient use of fixture design knowledge and improves the efficiency of fixture design. It has been verified by a case of airplane part machining fixture design.

This article presents a layered decomposition method to decompose the machined surface into sub-surfaces with different components in dissimilar scale to identify machining errors. The high-definition metrology-measured data of the surface is first fitted by triangular mesh interpolation method to separate the surface into two sub-surface components, namely, system error caused and random error caused, respectively, whereas the stability of sub-surface entropy is used as the criteria to determine the refined mesh in case the decomposition exists throughout. Then, the sub-surface of system error is further decomposed by bi-dimensional empirical mode decomposition to get the error components varying in scales: surface roughness, waviness and profile, and as a result to identify the machining errors. Finally, self-correlation analysis is applied to each component to verify the decomposition. The result shows that each decomposed component has a distinctive wavelength, which proves that the method can successfully decompose the comprehensive surface topography into different scale components.

Gun chamber pressure is an important parameter in proofing of ammunition to ensure safety and reliability. It can be measured using copper crushers or piezoelectric sensor. Pressure calculations in copper crusher method are based on linear plastic deformation of copper after firing. However, crusher pressure deformation at high pressures deviates from the corresponding values measured by piezoelectric pressure transducers due to strain rate dependence of copper. The nonlinear deformation rate of copper at high pressure measurements causes actual readings from copper crusher gauge to deviate from true pressure values. Comparative analysis of gun chamber pressure was conducted for 7.62 × 51 mm ammunition using Electronic Pressure, Velocity, and Action Time (EPVAT) system with piezoelectric pressure transducers and conventional crusher gauge. Ammunitions of two different brands were used to measure chamber pressure, namely, NATO standard ammunition and non-NATO standard ammunition. The deformation of copper crushers has also been simulated to compare its deformation with real time firing. The results indicate erratic behavior for chamber pressure by copper crusher as per standard deviation and relative spread and thus prove piezo sensor as more reliable and consistent mode of peak pressure measurement. The results from simulation, cost benefit analysis, and accuracy clearly provide piezo sensors with an edge over conventional, inaccurate, and costly method of copper crusher for ballistic measurements due to its nonlinear behavior.

Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

Information organizing principle is one of the key issues of intelligent master model (IMM), which is an enhancement of the master model (MM) based on KBE (knowledge-based engineering). Despite the fact that the core product model (CPM) has been confirmed to be an organizing mechanism for product master model, the key issue of supporting the information organizing for IMM is not yet well addressed, mainly due to the following two reasons, (1) lack of representation of complete information and knowledge with regard to product and process, including the know-why, know-how, and know-what information and knowledge, and (2) lack of semantic richness. Therefore, a multiaspect extension to CPM was first defined, and then an ontology was constructed to represent the information and design knowledge. The extension refers to adding a design process model, context model, product control structure model, and design rationale model to CPM concerning the enhancement of master model, which is to comprehensively represent the reason, process, and result information and knowledge of theproduct. The ontology construction refers to representing the concepts, relationships among these concepts and consistency rules of IMM information structure. Finally, an example of barrel design and analysis process is illustrated to verify the effectiveness of proposed method.

This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.

Early diagnosis of lung cancer plays crucial role in the improvement of patients' chances of survival. Computer aided detection (CAD) system has been a groundbreaking step in the timely diagnosis and identification of potential nodules (lesions). CAD system starts detection process by extracting lung regions from CT scan images, this step narrows down the region for detection. Hence saving the time consumption and reducing false positives outside the lung regions that results in the improvement of specificity of system. Proper lung segmentation significantly increases the performance of CAD systems. Different algorithms are presented by various researchers to improve segmentation results. An intensity based approach is presented in this paper for the segmentation of parenchyma and the goal is to achieve reasonable segmentation results in less time. Algorithm used in this paper is based on the Intensity based thresholding which is the fastest method for image segmentation. Images used in this research to analyze algorithm's result are taken from Lung Image Database Consortium (LIDC). Twenty random cases were picked, each having different number of slices (128 to 300). Algorithm is implemented using MatlabR2014 and a system with processor of 2.6 GHz and RAM of 4 GB. Total time taken for a single case of 128 images was 6.3 seconds and hence with an average of 49 milli sec/slice.

The aim of this project is to achieve a precise temperature control of boiler and it can be done by fuzzy logic controller. Fuzzy logic controller is computer generated and is easy to implement. Fuzzy logic controller being more efficient than other conventional controllers provide us with better and accurate results. MatLab simulation and Labview experimental results clearly show the amount of overshoot and settling time are modest, it also makes the boiler cost effective by achieving the target temperature in less time.

In order to deeply understand the friction characteristics of tool-chip contact interface, in this paper, a new local friction law was established based on theoretical analysis and experimental verification introducing the distribution of local friction coefficient on the tool rake face to characterize the correlation of local friction coefficient and the local friction parameters. Both global parameters such as cutting forces, contact length, and local distribution of tribological parameters, for instance local friction coefficient, normal stress, and shear stress, were determined analytically by using this friction law. The effect of cutting parameters on the stress distribution, local friction coefficient distribution, cutting forces, and contact length was evaluated. The cutting force and thrust force of several groups of cutting parameters were calculated by adopting the new friction model, and the results were compared with the experimental data. The comparison results indicated that the proposed model has a great prediction and compute capacity.

Product sequencing is one way to reduce cost and improve product quality for multistage manufacturing systems (MMS). However, systematically evaluating the influence of product sequence on quality performance for MMS is still a challenge. By considering the rate of incoming conforming product, manufacturing system quality transition between batch to batch, and quality propagation along stages, this paper investigates the appropriate batch policies and product sequencing for MMS so that satisfied quality performance can be achieved. A model to analyze the relationship between the product sequencing and quality performance is conducted just by using the quality inspection data and the complex engineering knowledge used in the variation method is avoided. Based on Markov Chain processes methodology, quality performance is modeled as a function of transition states jointly determined by multistage condition, product sequencing, incoming part quality, and propagation of the rate of conforming products among multistage. Quality related batch strategies are discussed for optimal quality performance. Two kinds of quality efficiency are put forward to facilitate the modeling and the discussion. The results of the model will lead to guidelines for quality management in multistage manufacturing systems.

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

Knowledge plays a significant role during product development process. The knowledge service mechanism is the key stage for the organization and usage of design knowledge. This paper approaches the mechanism by BOM (Bill of material) technology to organize and reuse design knowledge. Firstly, the knowledge BOM is put forwards and constructed based on the product BOM, hence the effective organization of knowledge is realized. Secondly, according to the refinement and evolution process of product BOM, a knowledge task meta model is constructed to describe the design task, and the mechanism of knowledge integration into the task is formulized to achieve the knowledge service for product development. Finally, an aircraft part, a wing part structural design process is taken as an example to verify the feasibility of the proposed method.

This article deliberates that thin-walled parts are more easily deformed in the machining process, due to low stiffness, which not only creates elevated machining errors and reduces machining precision. This paper presents a systematic methodology to analyze and control the machining errors caused by machining deformation. With the minimum modulus principle, the kinematics model of the workpiece-fixture system is established and contact force (including friction force) between the fixture components and workpiece are calculated, and further, a model to optimize the clamping force is proposed in maintaining the stability of the workpiece-fixture system. Then, the numerical results obtained from the kinematics model are applied as boundary conditions on the finite element model of the workpiece-fixture system; thus, the deformation value is calculated. Then, the machining error model caused by deformation is used to transfer the deformation value to machining errors. The neural network is employed to establish the highly nonlinear relations between the machining error and the cutting parameter, which facilitates the establishment of the optimization model of cutting parameters to improve machining efficiency and ensure the machining precision. Finally, a case study is used to verify the effectiveness of the proposed method.

This research aims to optimize the process parameters of plasma arc welding for welding of dissimilar metals: austenitic stainless steel SS-304 L and low carbon steel A-36. It investigates the effect of welding current and welding speed on the quality of the welded joints. The quality characteristics like bead geometry, microstructure, hardness, ferrite measurement and tensile test are considered for qualification of the welded samples. Welded specimens were prepared both with and without filler material. These specimens were mechanically tested and analyzed using metallographic techniques. Based on the results, suitable welding parameters were found to be 45 A and 2 mm/s for samples prepared with and without filler wire. An all-martensitic weld zone structure was obtained for direct fusion. However, a complex heterogeneous microstructure was obtained by using austenitic stainless steel filler wire E 309 L. Hardness of directly fused sample was observed to be significantly higher compared to filler wire sample.

Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on condition-based maintenance (CBM) may be affected; wherein upkeep of the system is done progressively on the premise of monitored state of the system. In this article, a multicomponent framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA) technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multiobjective problem that is aimed at optimizing the two desired objectives, namely, profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC) simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA and MC) is considered to be more effective due to the employment of “drop-by-drop approach” that permits successful drive of the related search process with regard to the best optimal solutions.

The control of defects and forces is, respectively, necessary to produce robust components and to preserve machine tool and energy. In the present work, the variations in forces with the evolution of various SPIF (single point incremental forming) defects are studied by employing the pyramid geometry. It is found that the forming force increases as the size of pillow and wall defects increases, and contrarily decreases as the size of corner-fold increases. Further, to carry out defect-free forming, the corner requires more force than the straight-wall of pyramid (i.e., F cr > F sw). This fact appears in the form of spikes corresponding to corner location in the force curve. Therefore, online monitoring of the force curve is proposed as a strategy to control defects in SPIF. Finally, following the condition F cr > F sw, force models describing the defect–force–parameter relationship are developed. These models will help the users to simultaneously predict and optimize the force as well as defects.

Use of cooling media contributes in elevation of desired responses owing to its cooling and lubrication effects especially in case of hard to cut materials like Ti-6Al-4V. This study aims to analyze the effect of cooling media in addition to cutting speed on responses including tool wear and surface integrity during turning. Results highlighted the effectiveness of cooling system in particular that of cryogenic media. Use of cryogenic media and cutting fluid enhanced tool life max by 33% and 21% respectively. At lower cutting speed surface roughness was improved by 13% and 8% by cryogenic media and cutting fluid respectively.

The first job of the manufacturing workforce as they get novel drawings is to carry out the process planning. This task, once finished, usually direct both the organization and manufacturing setups. Process planning in manufacturing setup offers a specific and clear chronological path regarding how the product should be running and fabricated in a manufacturing system. In highly developed manufacturing setups, this would persuade, how the setup will be planned and laid out in grounding for the novel product. In this research work, fundamental process plan is developed for a side plate manufacturing together with all design and user requirements. Mathematically modeling is done using progressive closed loop approach. Research then searches the capabilities of optimization techniques like DSM (dependency structure matrix) and a novel stochastic search to provide the best approximate process planning solution. Finally the global optimization is analysed in both the techniques and one technique reaches at optimum solution.

Six Sigma is being Implemented all over the World as a successful Quality Improvement Methodology. This article provides a description of Six Sigma Project implemented at Local manufacturing Company. The Company Manufactures Pump Casings where it was receiving high nonconformance rate that resulted in to Rejection of Product. This study deals with Six Sigma DMAIC methodology implementation and gives a frame work of how the non-conformance rate was first monitored and then brought in to acceptance limits. A complete Coverage of the statistical analysis performed during the study is given and results are shown to describe that how Six Sigma helped the Project members to Improve Quality of Pump casings at manufacturing facility. Keywords: Statistical Process Control (SQC), Process Capability Indices, Six Sigma, Variable control chart, Pareto charts, Standard deviation, Critical to Quality (CTQ), Analysis of Variance (ANOVA), DMAIC, Total quality Management (TQM)

Background/Objectives: Each structure tends to vibrate at particular frequencies, called resonant or natural frequencies. When a structure is excited by dynamic load with frequency coinciding one of its natural frequencies the structure experiences stresses and large displacements. In this paper effective mass participation factor criterion is used to solve the vibration problem in the ship mast. Methods/Statistical Analysis: The effective mass participation factor provides a measure of the energy contained within each resonant mode. Vibration problem originated when one of the antenna at top of mast was replaced by a new antenna with greater mass at same location. The overall mast structure started vibrating because of the resonance of natural frequencies of the mast structure with natural frequencies of rotary equipment. Findings: It caused interruption in sensitivity of equipment installed on the mast structure. Instead of fabricating the new mast structure, some alteration has been carried out on the basis of results obtained from modal analysis. Application/Improvements: The study is very effective to overcome the vibration problems in ship mast.

The finite element method (FEM) has been used to model high speed turning processes with orthogonal cutting conditions. In most of the situations, continuous chip formation is used to analyze the turning process due to its stability and allowing many conditions to simplify the process. However with the increasing applications of high speed turning, serrated chip formation is becoming a more common phenomenon in metal cutting. Serrated chips usually occur in machining of difficult to cut materials at or above a threshold speed. An updated Lagrangian formulation has been used in this study which works with element deletion technique based on a failure criterion. The Johnson Cook strain-hardening thermal-softening material model is used to model serrated chip formation. In addition high speed turning experiments were conducted on AISI H13 tubes using PCBN to analyze serrated chip phenomenon. The chips were analyzed after surface treatment using scanning electron microscope. It has been found that the length of cuts in the chip increases with the cutting speed and the chip changes from serrated to discontinuous. Different process variables like cutting forces, chip morphology, stress, strain and temperature distributions are predicted at different process parameters using FEM. The results show cyclic variation in the cutting forces at high cutting speeds due to varying chip load.

Mechanical loading equipments are wildly used in transportation system. Positioning precision is one of the basic functions of machine tools, and structure design is the key to ensuring accuracy. According to the assembly process of tubular–structure, the paper analyzes motion modes to achieve tubular–structure loading. In terms of assembly tolerance of tubular–structure, a tolerance model to guide structure design of loading equipment is formulated. Based on tolerance analysis of each motion mode, the maximum available size of the tubular–structure is calculated under different linear rolling guide, and the minimum available size of the interior rail in the cover box is worked out under different ball screws, trapezoidal screw threads, worm and worm gears. To meet the requirement of tolerance in tubular–structure assembly, mechanisms for all motions are defined. The design of loading equipment is tested and assessed by experiments, and the result shows the design is highly qualified for its assembly.