An investigation of a damaged crankshaft from a horizontal, six-cylinder, in-line diesel engine of a public bus was conducted after several failure cases were reported by the bus company. All crankshafts were made from forged and nitrided steel. Each crankshaft was sent for grinding, after a life of approximately 300,000 km of service, as requested by the engine manufacturer. After grinding and assembling in the engine, some crankshafts lasted barely 15,000 km before serious fractures took place. Few other crankshafts demonstrated higher lives. Several vital components were damaged as a result of crankshaft failures. It was then decided to send the crankshafts for laboratory investigation to determine the cause of failure. The depth of the nitrided layer near fracture locations in the crankshaft, particularly at the fillet region where cracks were initiated, was determined by scanning electron microscope (SEM) equipped with electron-dispersive X-ray analysis (EDAX). Microhardness gradient through the nitrided layer close to fracture, surface hardness, and macrohardness at the journals were all measured. Fractographic analysis indicated that fatigue was the dominant mechanism of failure of the crankshaft. The partial absence of the nitrided layer in the fillet region, due to over-grinding, caused a decrease in the fatigue strength which, in turn, led to crack initiation and propagation, and eventually premature fracture. Signs of crankshaft misalignment during installation were also suspected as a possible cause of failure. In order to prevent fillet fatigue failure, final grinding should be done carefully and the grinding amount must be controlled to avoid substantial removal of the nitrided layer. Crankshaft alignment during assembly and proper bearing selection should be done carefully.

No abstract

No abstract

No abstract

Purpose -The purpose of this paper is to present numerical experimentation of three constraint detection methods to explore their main features and drawbacks in infeasibility detection during the design process.Design/methodology/approach -Three detection methods (deletion filter, additive method and elasticity method) are used to find the minimum intractable subsystem of constraints in conflict. These methods are tested with four enhanced NLP solvers (sequential quadratic program, multi-start sequential quadratic programing, global optimization solver and genetic algorithm method).Findings -The additive filtering method with both the multistart sequential quadratic programming and the genetic algorithm solvers is the most efficient method in terms of computation time and accuracy of detecting infeasibility. Meanwhile, the elasticity method has the worst performance.Research limitations/implications -The research has been carried out for only inequality constraints and continuous design variables. This research work could be extended to develop computer-aided graphical user interface with the capability of including equality constraints and discrete variables.Practical implications -These proposed methods have great potential for finding and guiding the designer to detect the infeasibility for ill-posed complex design problems.Originality/value -The application of the proposed infeasibility detection methods with their four enhanced solvers on several mechanical design problems reduces the number of constraints to be checked from full set to a much smaller subset.