Problems in vision that are originated by diabetes are known as Diabetic Retinopathy (DR).The requirement for a inclusive and automatic method of DR test is raised by doctors. Various methods such as classification, recognition of images are in the past used for DR detection. Automated detection techniques used for early detection can save eyes of diabetic patients. Two deep learning models using Convolution Neural Network are designed and trained on images. The models are tested on dataset collected from Kaggle and APTOS repository, comprising 3662 training and 1928 testing images. The accuracy of designed models is calculated it is approximately 93–95% for given dataset. The model results are cross verified by expert ophthalmologist. The novelty of research is design of new models for prior diagnosis of diabetic retinopathy in patients for early precautionary measures. The models are rapid in classification for complex images.In future; image dataset size can be increased to get more accurate results.

COVID-19 (Coronavirus disease) has made world stand still. Detection of COVID-19 positive case immediately is requirement for prevention of its spread and save lives. X-ray images comprises substantial data about the spread of infection through virus in lungs. Advanced assistive tools using machine learning overcome the problem of lack of medical facilities in remote places. In this research, CvDeep, a model for COVID-19 detection using X-ray images as resource is designed. The images are preprocessed for final diagnosis with pertained models. It is observed that it is difficult to detect COVID-19 in early stage using images analysis, but if pre trained deep learning models are used, it can improve the accuracy of detection. This model provides accuracy of 95% for COVID-19 cases. The models used for prediction are AlexNet, SquzeeNet, ResNet and DenseNet. The data set can be shared online to assist radiologists. Patients with COVID-19 (+ ve) can be given instant hospitalization without waiting for lab test result so that survival rate can be increased. Model is evaluated by expert radiologists.

The research work done for improvement in carbon weighing compliance of mixing system of banburry machine is presented in this paper. As the carbon is one of the essential raw agents used in process of tire manufacturing. The carbon handling and transporting system used in banburry machine mixing process is one of the critical processes. Carbon weighing compliance is also known as efficiency of carbon usage. Plant optimization pillars are used for process improvements, new product developments, reducing breakdowns and improving product quality. This research work is mainly focused on the application of DMAIC phases (Define, Measure, Analyze, Improve, Control) along with terotechnology to improve the value of carbon weighing compliance of mixing system by reducing the carbon breakdowns which occur repetitively. The top five carbon breakdowns were identified by analyzing the data of carbon breakdowns of year 2017 thoroughly. Application of DMAIC phases helped in creating new inspection checklists for the effective preventive maintenance of the carbon handling and transporting system of banburry machine. The average value of carbon weighing compliance is increased by 7.5542% (from 89.35 to 96.91%) and the reduction in average downtime is 16.92% (from 490.33 to 407.33 min).

Age hardenable, high-strength aluminium alloys are used majorly in aerospace, defence, marine and automobile components because of their excellent strength-to-weight ratio and better corrosion resistance. To join such nonferrous alloys, friction stir welding (FSW) is a special technique, which uses the phenomenon of friction and deformation while joining in the solid state. The joint sections of 2XXX, 6XXX and 7XXX series of aluminium alloys are susceptible to microstructural changes during FSW due to their ageing characteristics. These changes further aggravate the problem of mechanical engineering properties, and especially the corrosion resistance of these alloys. In the present work, focus is kept on the effect of post-weld heat treatments (PWHT) such as retrogression and re-ageing (RRA) and stabilization with double ageing (SDA) on mechanical properties, electrical conductivity and exfoliation corrosion resistance of AA 7075 aluminium alloy FSW joints.

Digitization and intelligentization of manufacturing process is the need for today’s industry. The manufacturing industries are currently changing from mass production to customized production. The rapid advancements in manufacturing technologies and applications in the industries help in increasing productivity. The term Industry 4.0 stands for the fourth industrial revolution which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer requirements. Industry 4.0 is still visionary but a realistic concept which includes Internet of Things, Industrial Internet, Smart Manufacturing and Cloud based Manufacturing. Industry 4.0 concerns the strict integration of human in the manufacturing process so as to have continuous improvement and focus on value adding activities and avoiding wastes. The objective of this paper is to provide an overview of Industry 4.0 and understanding of the nine pillars of Industry 4.0 with its applications and identifying the challenges and issues occurring with implementation the Industry 4.0 and to study the new trends and streams related to Industry 4.0.

This paper is presented on project work carried out on critical machine Twin Screw Roller Die which is part of Banbury Mixer. To identify the failures, machine deterioration and reduce change over time, plant optimization technique is implemented to restore equipment to its proper condition as well as proper training was given to operators about the technique The main objective of the paper is to improve key performance indicator by increasing MTBF and decreasing MTTR using Reliability Excellence pillar of plant optimization. Repeated breakdown were identified and analyzed along with critical parts. The root cause of machine part breakdown were identified and analyzed by method of why-why analysis. This turn helped to develop new preventive maintenance checklist for machine. Root cause analysis method is used to prevent failure of equipment before occurs.

Due to severe competition in the market, a manufacturer may need to redesign the product warranty so as to get an edge over the competitors. However, to be able to do that, a systematic approach is required that allows one to consider multiple objectives. In this paper, a multi-objective optimization approach is presented considering warranty type, warranty duration, component alternatives and support level as decision variables. The multiple-objectives are in the form of targets to be achieved for warranty attractiveness index, warranty cost, spare parts cost to the end user over the useful life of the product and mean time between failures. In this paper a weighted goal programming approach for solving this problem is presented, where the weights are derived for the different objectives using the criteria importance through intercriteria correlation method and the objective function is formulated using the loss function approach. The warranty optimization approach is illustrated using a real life example of an automobile engine manufacturer and is solved using a multi-objective genetic algorithm with elitism strategy.

Purpose – The purpose of this paper is to propose a warranty-based bilateral automated multi-issue negotiation approach. Design/methodology/approach – A methodology for bilateral automated negotiation process is developed considering the targets such as warranty attractiveness, warranty cost, mean time between failures, spare parts cost to the end user over the useful life of the life. The negotiation methodology is explained using different cases of negotiation. The optimization for each negotiation step is carried out using genetic algorithm with elitism strategy. Findings – The result after optimization indicates that the desired target values are achieved and manufacturer obtained desired profit margin. Practical implications – Application of automated negotiation model is illustrated using a real life case of an automobile engine manufacturer. The proposed approach helps the manufacturer of any product to develop a methodology for carrying out the negotiation process. The approach also results into taking warranty-related decisions at the design stage. Originality/value – This paper contributes in proposing a generalized methodology for warranty-based negotiation in which the negotiation is carried out between the manufacturer and the customer.

Purpose – The purpose of this paper is to develop an attractiveness index-based warranty cost model considering decision variables as design alternatives, warranty duration and support level. Design/methodology/approach – A warranty optimization approach is illustrated using a real life example of an automobile engine with Mean Time Between Failures and Warranty Attractiveness Index as constraints. Findings – It will help to improve the customer satisfaction by giving a more attractive warranty compared to that being offered by the competitors. Practical implications – Approaches that consider the effect of decision variables on attractiveness of a warranty policy in a quantitative manner have received relatively less attention. The paper attempts to capture the attractiveness of warranty from the manufacturer as well as customer point of view. Originality/value – The proposed approach will help manufacturers to take appropriate decisions related to warranty parameters and component selection at the design stage.

Purpose - The purpose of the paper is to develop a conceptual framework that integrates the technology and commercial issues early at the design stage to minimize warranty costs in the most effective and efficient manner and also to develop a model for optimization of warranty with specific focus on reliability and warranty policies.Design/methodology/approach - The critical issues in warranty are addressed which affect the warranty cost. An optimization model to achieve multiple goals like minimization of the warranty cost and improving the reliability of the product is developed using genetic algorithm as a solution methodology. The model is illustrated with a real case of automobile engine.Findings - The results of the optimization show improvement in mean time between failures (MTBF) which results due to improvement in the product reliability and also the targeted warranty cost is achieved.Research limitations/implications - The model developed needs to be further extended with inclusion of additional decision variable such as support level offered and more objectives such as attractiveness of the warranty from the customer's view point and spares cost to the customer.Originality/value - The paper provides the help to the designers at the design stage to take the decisions related to warranty in deciding the warranty parameters.

Warranty logistics deals with various issues relating to the servicing of warranty. Proper management of warranty logistics is needed not only to reduce the warranty servicing cost but also to avoid customer dissatisfaction since it has a negative impact on sales and revenue. Controls should be instituted to minimize the total cost of logistics rather than minimizing each component of the total cost. Warranty logistics mainly deals with the transportation of spares, warehousing, and warranty claims settlement and reverse logistics of the failed components. This paper illustrates a real life case study of an automobile engine manufacturer. There are two players; one of them is an engine manufacturer who supplies these engines to an automobile OEM. Various issues involved in forward and reverse logistics of the spare parts and failed components respectively have been studied. Issues faced by manufacture are discussed and solutions to overcome these problems are suggested.