Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

Augmented Reality Assisted Assembly Training Oriented Dynamic Gesture Recognition and Prediction

Augmented reality assisted assembly training (ARAAT) is an effective and affordable technique for labor training in the automobile and electronic industry. In general, most tasks of ARAAT are conducted by real-time hand operations. In this paper, we propose an algorithm of dynamic gesture recognition and prediction that aims to evaluate the standard and achievement of the hand operations for a given task in ARAAT. We consider that the given task can be decomposed into a series of hand operations and furthermore each hand operation into several continuous actions. Then, each action is related with a standard gesture based on the practical assembly task such that the standard and achievement of the actions included in the operations can be identified and predicted by the sequences of gestures instead of the performance throughout the whole task. Based on the practical industrial assembly, we specified five typical tasks, three typical operations, and six standard actions. We used Zernike moments combined histogram of oriented gradient and linear interpolation motion trajectories to represent 2D static and 3D dynamic features of standard gestures, respectively, and chose the directional pulse-coupled neural network as the classifier to recognize the gestures. In addition, we defined an action unit to reduce the dimensions of features and computational cost. During gesture recognition, we optimized the gesture boundaries iteratively by calculating the score probability density distribution to reduce interferences of invalid gestures and improve precision. The proposed algorithm was evaluated on four datasets and proved to increase recognition accuracy and reduce the computational cost from the experimental results.

Pelatihan Perakitan Komputer Pada CV. Rifanta Tanjungbalai

Abstrack : The training carried out at CV. Rifanta Tanjungbalai is a tridharma of community service in understanding computer assembly for employees of CV. Rifanta Tanjungbalai in installing computer hardware and software, where the quality of employees is very influential on human resources that can be used as a benchmark in mastery of assembly. As is known CV. Rifanta Tanjungbalai is engaged in trade and services and this is a benchmark for employees in knowing or understanding the main parts of a computer. The computer assembly training activities are presented in the form of explanations, video screenings, discussions and practice in the field. The benefits derived from this activity include being able to assemble computers and computer parts, so that employees work according to their field of expertise and can master proper assembly techniques. It shows the employees CV. Rifanta Tanjungbalai is very proficient in recognizing hardware and software, so that consumers are satisfied with the performance of employees.Keyword : computer assembly; hardware; softwareAbstrak : Pelatihan yang dilaksanakan di CV. Rifanta Tanjungbalai merupakan tridharma pengabdian kepada masyarakat dalam pemahaman perakitan komputer untuk karyawan CV. Rifanta Tanjungbalai dalam melakukan instalasi perangkat keras dan perangkat lunak komputer, dimana kualitas karyawan sangat berpengaruh pada sumber daya manusia yang dapat dijadikan sebagai tolak ukur dalam penguasaan perakitan. Sebagaimana diketahui CV. Rifanta Tanjungbalai bergerak dalam perdagangan dan jasa dan ini merupakan tolak ukur karyawan dalam mengetahui atau pun memahami tentang bagian-bagian utama komputer. Kegiatan pelatihan perakitan komputer tersebut disajikan dalam bentuk penjelasan, pemuataran video, diskusi dan praktek dilapangan. Manfaat yang diperoleh dari kegiatan ini anatara lain dapat melakukan perakitan komputer dan bagian-bagian komputer, sehingga karyawan bekerja sesuai dengan bidang keahlian dan dapat menguasai teknik merakit secara tepat. Ini menunjukkan para karyawan CV. Rifanta Tanjungbalai sangat mahir dalam mengenal perangkat keras dan perangkat lunak, sehingga konsumen merasa puas dengan kinerja para karyawan.Keyword : perakitan komputer; perangkat keras; perangkat lunak

PKM PENERAPAN MAXIMUM POWER POINT TRACKER (MPPT) PADA SISTEM POMPA AIR SOLAR PHOTO VOLTAIC (SPV) UNTUK RW8 KELURAHAN ROWOSARI KOTA SEMARANG

Rowosari Urban Village is a village located in Tembalang District, Semarang City. The Rowosari Urban Village has an area of ​​719,577 Ha at an altitude of 47 m above sea level with an average rainfall of 2,000 mm / year, an average temperature of 300C. According to Purwoko, SH, Head of Rowosari Village, when met by the Community Partnership Program Team (PKM) in the Rowosari Kelurahan office, problems in Rowosari Kelurahan, especially in RW 8, one of which was the high electricity bill, especially for public facilities such as prayer rooms and low human resources (HR) ), therefore the purpose of this PKM is to reduce electricity bills and increase the human resources of the Rowosari Kelurahan, Tembalang District, Semarang City through training in the Implementation of the Maximum Power Point Tracker (MPPT) in the Water Pump System. To overcome the problems of partners especially the priority that must be addressed, the University of Semarang PKM team used a method with 3 stages of activities namely HR Enhancement through Solar Photo Voltaic (SPV) assembly training, Assistance in Solar Photo Voltaic (SPV) assembly, and Installation of electric pumps that produced from Solar Photo Voltaic (SPV). The results of this PKM activity are water pumps in wells in the RT.02 / RW mosque. 8 Kelurahan Rowosari has used a Solar Photo Voltaic Water Pump (SPV).