Comparative study of selected indoor concentration from selective laser sintering process using virgin and recycled polyamide nylon (pa12)

Additive manufacturing (AM) stands out as one of the promising technologies that have huge potential towards manufacturing industry. The study on additive manufacturing impact on the environment and occupational exposure are attracting growing attention recently. However, most of the researcher focus on desktop and fused deposition modelling type and less attention given to the industrial type of AM. Usually, during the selective laser sintering process, recycle powder will be used again to reduce cost and waste. This article compares the PM 2.5, carbon dioxide (CO2) and total volatile organic compound (TVOC) concentration between virgin and recycles powder using polyamide-nylon (PA12) towards indoor concentration. Four phases of sampling involve during air sampling accordingly to the Industry Code of Practice on Indoor Air Quality 2010 by DOSH Malaysia. It was found that PM 2.5 and CO2 concentration are mainly generated during the pre-printing process. The recycle powder tended to appear higher compared to virgin powder in terms of PM 2.5, and CO2. The peak value of PM 2.5 is 1452 μg/m3 and CO2 is 1218 ppm are obtained during the pre-printing process during 8 hours of sampling. TVOC concentration from recycling powder is slightly higher during the post- printing phase where confirm the influence of the powder cake and PA12 temperature from the printing process. In summary, this work proves that elective laser sintering (SLS) machine operators are exposed to a significant amount of exposure during the SLS printing process. Mitigation strategies and personal protective equipment are suggested to reduce occupational exposure

Performance Evaluation of Continuous and Discrete Particle Swarm Optimization in Job-Shop Scheduling Problems

The Particle Swarm Optimization (PSO) is an optimization method that was modeled based on the social behavior of organisms, such as bird flocks or swarms of bees. It was initially applied for cases defined over continuous spaces, but it can also be modified to solve problems in discrete spaces. Such problems include scheduling problems, where the Job-shop Scheduling Problem (JSP) is among the hardest combinatorial optimization problems. Although the JSP is a discrete problem, the continuous version of PSO has been able to handle the problem through a suitable mapping. Subsequently, its modified model, namely the discrete PSO, has also been proposed to solve it. In this paper, the performance of continuous and discrete PSO in solving JSP are evaluated and compared. The benchmark tests used are FT06 and FT10 problems available in the OR-library, where the goal is to minimize the maximum completion time of all jobs, i.e. the makespan. The experimental results show that the discrete PSO outperforms the continuous PSO for both benchmark problems