Hybrid manufacturing technology has enabled manufacturers to combine advantages of mainly subtractive and additive manufacturing technologies. A single machine supports producing products with complex geometry, at high quality, and with a high degree of automation. To benefit from these advantages, decisions taken in the process planning stage of such a sophisticated manufacturing system should be optimized. The objective of this paper is to determine the optimal process plan considering both the engineering and manufacturing aspects of the hybrid technology. A comprehensive process planning model is proposed. The model specifies the optimal sequence of additive and subtractive features that minimizes the production cycle time. In addition, the model sets the optimal part orientations such that the time needed for building support structures, performing post-processing and inspection operations, changing cutting tools and printing nozzles, and unclamping the part is minimized. The model is comprehensive as it considers productive and non-productive times, precedence, technological, quality, and manufacturing restrictions imposed on hybrid manufacturing systems. The proposed model is nonlinear; due to this nonlinearity, the model is intractable. A linearization scheme is applied to formulate an equivalent linear model that is solvable to optimality by commercial solvers. Case studies on test and industrial parts are provided to evaluate the computational performance of the proposed model. Integrating the proposed model in hybrid manufacturing (HM) systems ensures adopting the HM technology in its optimal direction. HM technology is an enabler of establishing a smart manufacturing system which is one of the pillars of Industry 4.0.

An automated transfer line balancing problem is investigated in this paper. The line produces a complex part at a high volume, such as cylinder heads, and incorporates identical machines that can be operated under different cutting conditions. The selection of cutting conditions significantly influences productivity and the cost of the transfer line. The production of cylinder heads requires machining different operations within a given takt time and satisfying precedence, inclusion, and exclusion relationships. The operations are located on different faces of the cylinder head and are processed by different cutting tools. The line balancing problem is studied to identify the optimal cutting conditions, number of machines and tools, and machining sequence of operations. The objective is to design a balanced transfer line at a minimum machining and tooling cost, and also with minimum nonproductive time. The problem is represented by a goal-programming model and solved for small transfer line configurations through linearization. An improved cuckoo search algorithm via Levy flight is developed to solve large-scale instances. Results of the cuckoo algorithm are promising since it reached optimal and close-to-optimal solutions to small problems and surpassed the results of a random local search approach for instances of medium and large problems.

Purpose: In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.Design/methodology/approach: Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.Findings: Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.Originality/value: The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.