Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

A vibrant crossbreed social spider optimization with genetic algorithm tactic for flexible job shop scheduling problem

Job shop scheduling is one of the major issues in which the scheduling process is associated with the real-time manufacturing industry. A flexible job shop scheduling problem is one of the most important issues among the hardest combinatorial advancement issues. Flexible job shop scheduling is extremely a nondeterministic polynomial combinatorial problem. In this paper, it is proposed that a mixture of improvement demonstrates to make makespan minimization in the flexible job shop scheduling problem issue. This paper includes the hybridization of social spider optimization and genetic algorithm that is effectively controlled by the calculation via optimization techniques. Most of the part in this method is given as the scavenging methodology of social insects, which use the vibrations spread over the bug-catching network to decide the position of the target. These hybridization approaches after arachnid upgrading process hereditary calculation chromosomes are chosen to produce new arrangements nearer to the minimum makespan time. The main objective of this paper is to minimize the makespan time of “ n” jobs and “ m” machines. The proposed algorithms have effectively investigated many benchmark problems and the computational results were compared with existing metaheuristic, including progressive calculations and algorithms for the swarm intelligence in the flexible job shop scheduling problem. </jats:p

Smart Manufacturing through TOC based Efficiency Monitoring System (TBEMS)

The very purpose of business is to devise profitability and enhance it in all possible avenues sustainable. In a manufacturing environment, thus, there had been a number of techniques and concepts adapted to improvise the effectiveness thereby profits continuously. Theory of Constraints (TOC) adopts a unique con-cept exploiting the constraint to deliver the customer needs. TOC is built on the premise that the weakest link determines the strength of the whole chain. With the advent of Industry 4.0, the manufacturing systems could be exploited to the next best level, leveraging the interaction of cyber physical systems and human beings over the internet. This paper deals with a novel idea of implementing TOC concept blended with Internet of Things (IoT), thereby, the speed of implementation could be augmented for early results. Evidently, the smartness of Things is    derived based on the possibility of informed and proactive decisions.  Hence all the productivity improvement techniques and concepts could be complemented with such concurrent information and analytics, thereby the learning and decisions are much smarter and proactive. A real time industrial environment has been chosen to experiment this approach and the results are furnished paving way for future research and improvisation globally on the industrial environment and on many other competing productivity concepts  </jats:p

Performance Analysis of Tyre Manufacturing System in the SMEs Using RAMD Approach

In the recent trends, production plants in the automobile industries all over the world are facing a lot of challenges to achieve better productivity and customer satisfaction due to increasing the passenger’s necessity and demand for transportation. In this direction, the belt, tyre, and tube manufacturing plants act as vital roles in the day-to-day life of the automobile industries. Tyre production plant comprises five major units, namely, raw material selection, preparation, tyre components, finishing, and inspection. The main purpose of this research is to implement the new method to predict the most critical subsystems in the tyre manufacturing system of the rubber industry. As mathematically, any one maintenance parameter among reliability, availability, maintainability, and dependability (RAMD) parameters is evaluated to identify the critical subsystems and their effect on the effectiveness of the tyre production system. In this research, the effect of variation in maintenance indices, RAMD, is measured to identify the critical subsystem of the tyre production system based on the mathematical modeling Markov birth-death approach (MBDA), and the equations of the subsystems are derived by using the Chapman–Kolmogorov method. Besides, it also calculates the performance of certain maintenance parameters concerning time such as mean time between failures (MTBF), mean time to repair (MTTR), and dependability ratio for each subsystem of the tyre production system. Finally, RAMD analysis of the tyre production systems has been executed for predicting the most critical subsystem by changing the rates of failure and repair of individual subsystems with the utilization of MATLAB software. RAMD analysis reveals that the subsystem bias cutting is most critical with the minimum availability of 0.8387, dependability 5.19, dependability ratio 0.8701, and maximum MTTR 38.46 hours of the subsystem. In this implementation of the proposed method, a real-time case study of the industrial repairable system of tyre manufacturing system has been taken for evaluating RAMD indices of the production plant of rubber industry cited in the southern region of Tamil Nadu, India.</jats:p