A generalised model for optimising an end milling operation

Parent, L.; Songmené, Victor; Kenné, Jean‐Pierre

doi:10.1080/09537280701292501

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Table 3 summarizes the data used in this numerical sample case 2. This example was adapted from the work of Parent, Songmene and Kenné [11] and modified to suit machining setup proposed in this study. A typical end milling operation of an alloyed steel part (4140, 4340), with a four-flute uncoated high speed steel (HSS) end mill is proposed for evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…Their approach generated means to estimate the cost of titanium milling based on actual machining and pre-manufacturing conditions. Furthermore, due to tools supply unpredictability and market demand variability, numerous researchers have sought to design models which would be used to maximize the efficiency in tool management to mitigate the adverse costs incurred in shortage conditions [9] [10] [11].…”

Section: Recent Studies On Cost Optimization In Machiningmentioning

confidence: 99%

“…The parametric constants defined in this model need to be obtained through experimentation due to the fact that the machining conditions such as the tool material-make, workpiece, cutting fluid, rigidity of the assembly and the system vibration amongst other factors, vary significantly from one machine to the other. These can be determined using a statistical design of experiment (DOE) [11].…”

Section: Objective Functionmentioning

confidence: 99%

“…The objective function formed to minimize the production cost that involves the operation cost, shortage cost, holding cost, material cost and ordering cost is shown in the Equation (11). The cost of labor is not included in this function.…”

Section: Basic Objective Function For Single Operationsmentioning

confidence: 99%

“…Schematic representation of tool quantity per order cycle needed at minimum total cost.in Equation(11). From the figure, the main contributors to the total production costs were identified as the ordering and machine operations cost.…”

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Development of a Tool Cost Optimization Model for Stochastic Demand of Machined Products

Pantoja¹,

Songmené²,

Kenné³

et al. 2018

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Cutting tool management in manufacturing firms constitutes an essential element in production cost optimization. In order to optimize the cutting tool stock level while concurrently minimizing production costs, a cost optimization model which considers machining parameters is required. This inclusive modeling consideration is a major step towards achieving effectiveness of cutting tool management policy in manufacturing systems with stochastic driven policies for tool demand. This paper presents a cost optimization model for cutting tools whose utilization level is assumed to be optimized in respect of the machining parameters. The proposed cost model in this research incorporated the effects of diversified machining costs ranging from operational through machining, shortage, holding, material and ordering costs. The machining of parts was assumed to be a single cutting operation. Holt-Winters forecasting technique was used to create a stochastic demand dataset for a test scenario in the production of a high-end automotive part. Some numerical examples used to validate the developed model were implemented to illustrate the optimal machining and tool inventory conditions. Furthermore, a sensitivity analysis was carried out to study the influence of varying production parameters such as: machine uptime, demand and cutting parameters on the overall production cost. The results showed that a desired low level of tool storage and holding costs were obtained at the optimal stock levels. The machining uptime had a significant influence on the total cost while tool life and cutting feed rate were both identified as the most influential cutting variables on the total cost. Furthermore, the cutting speed rate had a marginal effect on both costs and tool life. Other cost variables such as shortage and tool costs had significantly low effect on the overall cost.

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Section: Resultsmentioning

confidence: 99%

Section: Recent Studies On Cost Optimization In Machiningmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

Section: Basic Objective Function For Single Operationsmentioning

confidence: 99%

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confidence: 99%

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