A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing

In the current industry 4.0 scenario, good quality cutting tools result in a good surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays a crucial role in manufacturing quality components. In addition to tool monitoring, wear prediction assists the manufacturing systems in making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model to predict the turning tool wear. Cutting forces, the surface roughness of a specimen, and flank wear of tool insert are measured for corresponding spindle speed, feed rate, and depth of cut. Those turning test datasets are applied in machine learning for tool wear predictions. The test was conducted using SNMG TiN Coated Silicon Carbide tool insert in turning of EN8 steel specimen. The dataset of cutting forces, surface finish, and flank wear is extracted from 200 turning tests with varied spindle speed, feed rate, and depth of cut. Random forest regression, Support vector regression, K Nearest Neighbour regression machine learning algorithms are used to predict the tool wear. R squared, the technique shows the random forest machine learning model predicts the tool wear of 91.82% of accuracy validated with the experimental trials. The experimental results exhibit flank wear is mainly influenced by the feed rate followed by the spindle speed and depth of cut. The reduction of flank wear with a lower feed rate can be achieved with a good surface finish of the workpiece. The proposed model may be helpful in tool wear prediction and making tool-changing decisions, which leads to achieving good quality machined components. Moreover, the machine learning model is adaptable for industry 4.0 and cloud environments for intelligent manufacturing systems

Exploring the Potential of Integrating Machine Tool Wear Monitoring and ML for Predictive Maintenance - A Review

This research review article explores the potential of integrating machine tool wear monitoring and ML algorithms for predictive maintenance. It synthesizes the latest research in the field, while discussing the benefits and challenges of various approaches. Specifically, this review examines the applications of sensors in machine tool condition monitoring, the use of ML algorithms to detect wear patterns and predict maintenance needs, and the potential of integrating ML and predictive maintenance. The article also evaluates the potential of using ML algorithms in conjunction with sensor data to improve tool performance and reduce maintenance costs. Finally, the article provides scope for future research to expand the potential of ML for predictive maintenance in machine tools. Overall, this review highlights the potential of integrating ML with predictive maintenance for machine tool applications

Digital Twin Technology for Tool Condition Monitoring: A Review of Recent Research

This research review article examines the use of Digital Twin technology (DT) in Tool Condition Monitoring (TCM). DT is a powerful technology that enables the creation of an exact digital replica of real-world entities, such as machines and tools, providing an integrated representation of various physical and virtual components. By combining real-time data with digital models of the tools, DT can be used to monitor tool condition and detect potential issues before they become serious. This review article surveys recent research on the use of DT in TCM and discusses the challenges that need to be addressed in order to make DT a viable solution for industrial tool monitoring. It also provides insight into future directions for research in this field. The results of this review suggest that DT has great potential to revolutionize tool monitoring in the manufacturing industry

SPARE PARTS INVENTORY OPTIMIZATION FOR AUTO MOBILE SECTOR

In this paper the objective is to determine the optimal allocation of spares for replacement of defective parts on-board of a usage. The minimization of the total supply chain cost can only be achieved when optimization of the base stock level is carried out at each member of the supply chain. A serious issue in the implementation of the same is that the excess stock level and shortage level is not static for every period. This has been achieved by using some forecasting and optimization techniques. Optimal inventory control is one of the significant tasks in supply chain management. The optimal inventory control methodologies intend to reduce the supply chain cost by controlling the inventory in an effective manner, such that, the SC members will not be affected by surplus as well as shortage of inventory. In this paper, we propose an efficient approach that effectively utilizes the Genetic Algorithm for optimal inventory control. This paper reports a method based on genetic algorithm to optimize inventory in supply chain management. We focus specifically on determining the most probable excess stock level and shortage level required for inventory optimization in the supply chain so that the total supply chain cost is minimized . So, the overall aim of this paper is to find out the healthy stock level by means of that safety stock is maintained throughout the service period. Keywords: genetic algorithm, optimization, Inventor

Awareness and practices on eye effects among people with diabetes in rural Tamil Nadu, India.

Background: Recently eye effects of Diabetes Mellitus (DM) are an important concern due to increase in its trend especially in developing countries. Objectives: To assess the awareness related to eye effects of DM and its prevention practices among people with diabetes. Methods: This cross sectional study was conducted from January 2013 to April 2013 in Villupuram district of Tamil Nadu, India. All 105 people with diabetes from the service area of two sub-centres were included. Data on socio demographic details, history of DM, awareness on systemic complications of DM, effects of DM on eyes, practice on regular blood check-up, eye examination and source of information were collected by interview technique using a structured questionnaire. Univariate and multiple logistic regression analysis were done to assess the association of awareness of eye examination with socio-demographic variables. Results: Mean age of the study population was 56.7 years. About 93 people with diabetes (88.6%) tested their blood sugar at least once in every 3 months. About 80 people with diabetes (76.2%) were aware of at least one systemic complication of DM. Although 78 (74.3%) people with diabetes were aware that DM could affect the eyes, majority of this group (68, 87.2%) did not know the specific effects of DM on eyes. In this group, about 28(35.9%) people with diabetes were not aware of the reasons for eye effects, while others mentioned that persistent high blood sugar level (n=26, 33.3%), longer duration of DM (n=14, 17.9%) and lifestyle (n=10, 12.8%) were the reasons for the eye effects of DM. Only 31 (29.5%) of them knew that their eyes must be regularly examined. People with diabetes who had post-secondary and above (>10th standard) level of education had significantly higher awareness on examination of eye (Adjusted OR=19.63). Conclusion: Although awareness of people with diabetes on systemic effects of DM was more, their awareness on specific eye effects and need for regular screening was low. Systematic efforts are required to increase awareness on eye effects and importance of regular screening in this population

A Survey on Decentralized Access Control Strategies for Data Stored in Clouds

ABSTRACT: This paper details about various methods prevailing in literature of anonymous authentication mechanisms for data stored in clouds. It is a Decentralized access of system in which every system have the access control of data . The Cloud which is a Secured storage area where the anonymous authentication is used, so that only the permitted users can be accessed. Decrypting of data can be viewed only by a valid users and can also stored information only by Valid users. This Scheme prevents Replay attack which mean Eaves Dropping can be avoided, Support Creation of data inside storage, Modifying the data by unknown users , and Reading data stored in Cloud. User can revocate the data only by addressing through the cloud. The authentication and accessing the Cloud is Robust, Hence Overall Communication Storage are been developed by comparing to the Centralized approaches. This paper would promote a lot of research in the area of Anonymous Authentication

Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Background: The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly. Results: In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions: Many current genome assemblers produced useful assemblies, containing a significant representation of their genes and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another