Production Planning of a Failure-Prone Manufacturing/Remanufacturing System with Production-Dependent Failure Rates

This paper deals with the production-dependent failure rates for a hybrid manufacturing/remanufacturing system subject to random failures and repairs. The failure rate of the manufacturing machine depends on its production rate, while the failure rate of the remanufacturing machine is constant. In the proposed model, the manufacturing machine is characterized by a higher production rate. The machines produce one type of final product and unmet demand is backlogged. At the expected end of their usage, products are collected from the market and kept in recoverable inventory for future remanufacturing, or disposed of. The objective of the system is to find the production rates of the manufacturing and the remanufacturing machines that would minimize a discounted overall cost consisting of serviceable inventory cost, backlog cost and holding cost for returns. A computational algorithm, based on numerical methods, is used for solving the optimality conditions obtained from the application of the stochastic dynamic programming approach. Finally, a numerical example and sensitivity analyses are presented to illustrate the usefulness of the proposed approach. Our results clearly show that the optimal control policy of the system is obtained when the failure rates of the machine depend on its production rate.

Ultrafine and fine particle emission in turning titanium metal matrix composite(Ti-MMC)

Titanium metal matrix composite(Ti-MMC)has excellent features and capabilities which can be considered a potential candidate to replace commercial titanium and superalloys within an extensive range of products and industrial sectors.Regardless of the superior features in Ti-MMC,however,referring to several factors including high unit cost and existence of rigid and abrasive ceramic particles in the generated matrices of the work part,the Ti-MMC is grouped as extremely difficult to cut with a poor level of machinability.Furthermore,adequate process parameters for machining Ti-MMCs under several lubrication methods are rarely studied.Therefore,adequate knowledge of this regard is strongly demanded.Among machinability attributes,ultrafine particles(UFPs)and fine particles(FPs)have been selected as the main machinability attributes and the factors leading to minimized emission have been studied.According to experimental observations,despite the type of coating used,the use of higher levels of flow rate led to less UFPs,while no significant effects were observed on UFPs.Under similar cutting conditions,higher levels of FPs were recorded under the use of uncoated inserts.Moreover,cutting speed had no significant influence on UFPs;nevertheless,it significantly affects the FPs despite the type of insert used.

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

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. The output trend showed that the feed rate is the most significant cutting parameter in the machining operation, hence influencing the cost the most. Also, machine uptime and demand significantly influenced the total production cost.

Effects of machining conditions on specific surface of PM_2.5emitted during metal cutting

Indoor air quality has become an important matter for health and safety. Most manufacturing processes generate aerosols. In the metal cutting industry, dry machining processes are accompanied by dust emission (fogs, fine chips and metallic dust in both micrometers and nanometers scales) that has impacts on workers’ health. This research work aimed to understand and reduce the harmful impacts of the machining process on the occupational safety. In this study, an experimental investigation was carried out on fine and ultrafine metallic dust emission during slot milling of 2024-T351, 6061-T6 and 7075-T6 aluminum alloy in dry conditions. It was confirmed that the cutting conditions influence significantly the specific surface area of ultrafine particles. It was also found that the cutting speed is a determinant factor for specific surface area of ultrafine particles and control during the slot milling process.

Experimental investigation on PM2.5 particle emission during polishing of granite

Particle emission during manufacturing processes, whether chemical, physical or mechanical can represent a serious danger for environment and for occupational safety. Especially machining processes, particle emission could have an important impact on shop floor air quality and might jeopardise workers’ health. It is therefore important to find ways of reducing the particle emission at the source of manufacturing processes. To do so, there is a need to know the size, the quantity and the distribution of particles produced by processes currently used in industry. In this study, investigations are done to compare the particle emission (PM2.5) when polishing two granites (black and white). The black granite contained low Si concentration (about 10% Si) and the white granite contained high Si concentration (about 50% Si). Particle emission was monitored using the DustTrak II equipment with 2.5 μm impactor. The particle grain size was evaluated using X-ray diffraction techniques. Machining conditions leading to the generation of finer particles were identified.