The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.

In machining processes, monitoring the condition of the tool is a crucial aspect for high productivity and quality of the product. Using different ML techniques in Tool Condition Monitoring (TCM) enables a better analysis of the large amount of data of different signals acquired during the machining processes. Different tool wear conditions were considered during the experimentation. A comprehensive statistical analysis of the data and feature selection using decision trees was conducted, and the KNN algorithm was used to perform classification. Hyperparameter tuning of the model was done to improve the model’s performance. In this research for the first time, a model agnostic approach to increase the interpretability and get an in-depth understanding of decision making is done. This research paper presents a KNN-based white box model, which allows us to dive deep into how the model performs the classification and how it prioritizes the different features included. This approach helps in detecting why the tool is in a certain condition and allows informed decision-making.

Service Oriented Architecture A Revolution for Project Management Software has changed the way projects today are moving on the fly with the help of web services booming the industry. Service oriented architecture improves performance and the communication between the distributed and remote teams. Web Services to Provide Project Management software the visibility and control of the application development lifecycle-giving a better control over the entire development process, from the management stage through development. The goal of Service Oriented Architecture for Project Management Software is to produce a product that is delivered on time, within the allocated budget, and with the capabilities expected by the customer. Web Services in Project management Project management software is basically a properly managed project and has a clear, communicated, and managed set of goals and objectives, whose progress is quantifiable and controlled. Resources are used effectively and efficiently to produce the desired product. With the help of service oriented architecture we can move into the future without abandoning the past. A project usually has a communicated set of processes that cover the daily activities of the project, forming the project framework. As a result, every team member understands their roles, responsibilities and how they fit into the big picture thus promoting the efficient use of resources.