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This paper aims to investigate the influences of process parameters on part quality, electrical energy consumption. Moreover, the relationship between part quality and energy consumption of UTR9000 photosensitive resin fabricated by stereolithography apparatus (SLA) was also assessed.

Main effect plots and contour maps were used to analyze the interactions and effects of various parameters on energy consumption and part quality, respectively. Then, a growth rate was used defined as the percentage of the value of energy consumption (or the part quality) of the sample compared to the minimum value of the energy consumption (or the same part quality), to jointly analyze relationships between part quality and energy consumption on a specific process parameter.

The part qualities can be improved with increased energy consumption via adjusting layer thickness, without further increasing energy consumption through adjusting laser power, over-cure and scanning distance. Energy consumption can be highly saved while slightly decreasing the tensile strength by increasing layer thickness from 0.09 mm to 0.12 mm. Energy consumption and surface roughness can be decreased when setting laser power near 290 mW. Setting an appropriate over-cure of about 0.23 mm will improve tensile strength and dimensional accuracy with a little bit more energy consumption. The tensile strength increases nearby 5% at a scanning distance of 0.07 mm compared to that at a scanning distance of 0.1 mm while the energy consumption only increases by 1%.

In this research, energy consumption and multiple part quality for SLA are jointly analyzed first to accelerate the development of sustainable additive manufacturing. This can be used to assist designers to achieve energy-effective fabrication in the process design stage.

The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under complex uncertainty.

An improved chance-constrained method is developed, in which confidence level of uncertain parameters is used to process uncertainty, and based on this, the reliability of the constraints is measured. Then, this study proposes a robust reconstruction method to transform the chance-constrained model into a deterministic model that is easy to solve, in which the robust transformation methods are used to deal with constraints with uncertainty on the right/left. Then, experimental studies using a real-world production data set provided by a gearbox synchronizer factory of an automobile supplier is carried out.

This study has demonstrated the merits of the proposed approach where the inventory of products tends to increase with the increase of confidence level. Due to a larger confidence level may result in a more strict constraint, which means that the decision-maker becomes more conservative, and thus tends to satisfy more external demands at the cost of an increase of production and stocks.

Joint decisions of production lot-sizing and scheduling widely applied in industries can effectively avert the infeasibility of lot-size decisions, caused by capacity of lot-sing alone decision and complex uncertainty such as product demand and production cost. is also challenging.

This study provides more choices for the decision-makers and can also help production planners find bottleneck resources in the production system, thus developing a more feasible and reasonable production plan in a complex uncertain environment.

This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.

Four components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM.

The presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase.

This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering.

The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process.

This paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.