In the increasingly volatile and competitive environment of the mobility industry, introducing new innovative products faster than competitors forms a key success factor for OEMs. To increase strategic flexibility, it is critical to speed up the development process while at the same time reducing the cost per vehicle project. Despite the increasing use of virtual methods in product development and production system design, hardware prototypes are indispensable for the series development today and will remain necessary in the foreseeable future. As these prototypes are very costly and their production tedious, companies are trying to reduce the number of pre-series vehicles required and optimize their production. Therefore, the authors developed a worker assistance system specifically designed for the pre-series production, that aims at increasing efficiency and maximizing the benefit for the series development. This paper reports on a case study that evaluated the application of this assistance system in a real industry environment. The results show that the assessed approach offers a significant potential to speed up the prototype assembly (up to 40% in the examined case), while at the same time contributing to the series assembly system development by systematically testing, correcting and evaluating planned series procedures.
The aim of the paper is to review the trends on low-cost open-source Internet of Thing (IoT) in the Framework of Industry 4.0. We hypothesize that open source IoTs benefits small and medium sized (SME) manufacturing companies to help them tackle economical and technical barriers for technology adoption at factory floors. This research reviews the state of the art of open-source hardware and software and identifies the challenges of open-source IoT by compiling scientific literature and relevant online resources. The methodology is twofold: (i) we use a grey literature review including online information, and (ii) we complement the analysis by a structured keyword search using IEEE scientific repository. The study finds that open source is and will be an essential part of developing Industry 4.0 solutions and its integral constituent, Industrial Internet of Things systems (IIoT). However, there are challenges to overcome, such as interoperability and reliability.
Typically, before constructing an object with an additive manufacturing system, the 3D object must be sent through a process called slicing. Slicing converts a 3D object commonly in the form of an STL file into a set of layers by horizontally intersecting a plane with the object at various heights. At each height, called a layer, multiple 2D polygons can be generated. Each polygon represents a boundary for solid geometry and is called an island. Each island is then comprised of multiple path types in an attempt to optimally fill the polygon. To move between each island and each islands’ paths, travels are inserted. Travels are simply motion by the system to move from one area of construction to another. Travels do not contribute to the construction of the object, and so, are considered wasted motion. In large-scale additive manufacturing, objects can be quite large and the distance between islands can be large as well. As a result, these travels can waste a significant amount of time. Ideally, travels would be as short as possible, however, computing global minimal travel paths is computationally expensive. To combat this problem, researchers at Oak Ridge National Lab developed a GPU-based approach to travel insertion based on a unique factoradic representation. This representation was then utilized by the GPU to solve the Traveling Salesman Problem (TSP). This algorithm was able to compute global minimal travel paths quickly resulting in faster object construction. A general investigation was also carried out to determine when a GPU vs CPU implementation would be beneficial.
The goal of this work was to determine which standard particle size metric derived from optical analysis most closely approximates the sieved weight percent of irregularly shaped powder intended to be used for directed energy deposition. In this investigation, equivalent circle diameter, maximum diameter, minimum diameter, and perimeter were used as metrics to “virtually sieve” the particles in samples of irregularly shaped powder into the following particle size bins: <45 µm, 45 µm – 150 µm, and >150 µm. The percentage in the 45 µm – 150 µm bin were then compared to the weight percent of the powder mechanically sieved into this size range. The absolute difference between the virtually sieved percentage and the mechanically sieved percentage was assessed for 81 samples of mechanically-generated stainless steel 316L powder, all produced under different processing conditions in an oscillation ball mill. This difference was found to be on average, the least with the minimum diameter assessed as an area percentage, followed by the equivalent diameter assessed as an area percentage, and then the maximum diameter assessed as a percentage of the total number of particles ranked third. These findings and the methodology used to obtain them may be used by powder production process engineers and quality assurance personnel to assist in process control as more diverse additive manufacturing feedstocks become utilized.
Although Metal Powder Bed Fusion Additive Manufacturing (PBFAM) process has emerged as an important industrial process, part distortion due to repeated heating and cooling is a major barrier that stands in the way of using this process for mass commercial production. Laser hatch pattern is a critical factor that influences the part distortion and has been widely studied through experimental investigations. In this paper, we describe an approach for hatch pattern optimization to minimize part GD&T errors using Genetic algorithms (GA). A pre-trained Artificial Neural Network (ANN) was adopted to predict the inherent strain of any hatch pattern, while a Backward Interpolation (BI) approach was used to predict the distortion of the sample part based on the inherent strain obtained from ANN. A Genetic Algorithm approach was then used to optimize the hatch angle of each layer to minimize the flatness form error of a flat feature of a sample part. Three types of island strategies were investigated for laying hatch patterns, and the results show that the increased number of scan islands in the hatch pattern results in less distortion and minimization of flatness error. The flatness values of four other benchmark hatch patterns were also investigated and compared with the optimal hatch pattern results. The comparison showed that the flatness of the sample part with the optimized hatch pattern performs better than the benchmark hatch patterns. The overall computational time for hatch pattern optimization was found to be reasonable, considering the large number of distortion simulations performed during the optimization process.
