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This paper seeks to evaluate the influence of tellurium content on the machinability of the microalloyed pearlitic steel (DIN 38MnS6). Two grades of steels were used, one with high (27 times greater) tellurium content and one with a low tellurium content. Machinability of the steel was determined by the number of holes drilled by the tool before undergoing severe deformation. The drilling test matrix was prepared using a fractional factorial design with five input variables studied at two levels (25‐1). Other variables investigated include cutting speed (45 and 60 m/min), feed rate (0.15 and 0.25 mm/rev), geometry of the twist drills and use of minimum quantity lubrication (MQL) at the flow rates of 30 and 100 ml/h. Statistical analysis of the results revealed that composition of the work material was most influential on tool performance. Addition of tellurium to the steel significantly improved machinability, increasing the number of drilled holes by over 100 per cent. The MQL flow rate was the least influential as increase in the flow from 30 to 100 ml/h reduced drill life only by about 9 per cent.

The drilling tests were carried out in the vertical position, up‐down, without pre‐holes (full drilling). Cutting speeds of 45 and 60 m/min and feed rates of 0.15 and 0.25 mm/rev were employed. Drills with two sharpening types were tested. Cutting fluid used was vegetable based and applied using the MQL technique at flow rates of 30 and 100 ml/h. The rejection criterion adopted was severe deformation of the drills and the number of machined holes was used to measure the machinability of the material.

Of all the variables investigated in this study, the least influential on drill performance is the MQL flow rate. Increase in the flow rate from 30 to 100 ml/h reduced drill performance by 9 per cent, contrary to expectation. This is a result of the cooling‐lubricant action balance promoted by the cutting fluid applied in low quantities (MQL). The most influential variable on drill performance is addition of Te to the work material which gave over twofold (103 per cent) improvement in drill performance at the cutting conditions investigated. The Te particles act at the chip‐tool interface, reducing the work necessary to shear the material during chip formation. Increase in both the cutting speed and the feed rate both lowered drill performance during machining due to associated increase in cutting temperature which tended to accelerate thermally related wear mechanisms.

This work was conducted to evaluate the machinability of a novel alloyed steel employed in the automobile industry. Drilling was considered as most automobile components especially the engine block is designed with many holes which require drilling process.

The purpose of this study is to offer a business process management (BPM) framework with information and insights on designing, implementing, using and assessing business processes for continuous improvement towards operational excellence.

An action research is carried out over two and a half years on the BPM evolution of a Brazilian entertainment organisation.

Research provides a novel procedural framework towards improving the understanding of how a complete lifecycle approach for BPM can be implemented for continuous improvement, embracing the critical success factors for each lifecycle phase to achieve operational excellence. Information technology and project management are critical success factors resulting in project barriers. Strategic alignment, top management support, collaborative environment, methods and methodology, and focus on users and culture are acknowledged as main enablers. Findings reinforce the importance of an organisation analysis phase to begin the BPM development and highlight the assessment and improvement phase to respond to organisational environment dynamism.

Practitioners can benefit from the lessons learnt and the proposed framework, which serves as a rigorous methodology to achieve operational excellence in their real-world settings.

This paper goes beyond the well-known design and analysis phases of BPM development, generally studied with an individual focus, offering a complete lifecycle approach for continuous improvement, analysing each phase, from the drawing board to its use and evaluation. It counts on an original longitudinal study rather than a single-time assessment study.

The mine sequencing problem is NP-hard. Therefore, simplifying it is necessary. One way to do this is to employ clusters as input instead of individual blocks. The mining cut clustering problem has been little addressed in the literature, and the solutions used are almost always heuristic. We solve the mining cut clustering problem, which is NP-hard, through single- and multi-objective optimization, finding results that are local optima in acceptable computational time.

We first elaborate an ILP-based model to address the mining cut clustering problem. We employ a mono-objective approach and two multi-objective approaches, solving all these models by constraint programming. To choose the best solutions generated by multi-objective approaches, we employ two multi-criteria decision analysis approaches, considering different weight configurations. We developed a case study using real data.

We verified that the approaches based on multi-objective optimization performed better than the mono-objective approach for the economic return criterion. The weighted-sum multi-objective approach presented the best results considering all objective functions used. Once viable solutions were obtained through multi-objective optimization, multi-criteria decision analysis approaches almost always selected the same solution. We obtained solutions that are local optima in acceptable computational time.

This study solves an instance with 80 blocks. Consequently, it is aimed at short-term mine planning. The methodology has not yet been evaluated in large instances related to medium- and long-term mine planning.

This is the first time that multi-objective optimization has been employed to solve the mining cut custering problem. Even other problems related to mine planning were, at most, solved by goal programming, so that multi-objective optimization is a knowledge that is not widespread among mining researchers. The results are consistent, and the study achieves the objective of finding quality solutions to an NP-hard problem in an acceptable computational time.