An Automatic Yogurt Filling System Built from Scratch Based on Industry 4.0 Concept

The movement to digitally transform Saudi Arabia in all sectors has already begun under the focused “Vision 2030” programme. Consequently, the task of renovating and standardizing manufacturing and other production industries to compete with global challenges is essential. The fourth industrial revolution (Industry 4.0), triggered by the development of information and communications technologies (ICTs), provides a basis for smart automation using decentralized control and advanced connectivity (e.g., Internet of Things). In the areas where the spirit of Industry 4.0 has been embraced, some industries have already implemented these ideas and yielded gains. Learning the principles of Industry 4.0 and having knowledge of this digital industrial revolution is essential for future engineering graduates. In this paper, a case study on building a learning factory from scratch based on the concept of Industry 4.0 is introduced. The initial achievement of a fully automated production line is presented. In doing so, a system with a smart design having smart control and smart monitoring is developed

Prediction of Surface Roughness and Power in Turning Process Using Response Surface Method and ANN

This paper examines the influence of three cutting parameters (cutting speed, cutting depth and feed rate) on surface roughness and power in the longitudinal turning process of aluminium alloy. For the analysis of data gathered by experiments, two methods for prediction of responses were employed, namely Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The research has shown that the ANN gives a better prediction of surface roughness than the RSM. In the modelling of the power, the average error value obtained by the ANN does not differ significantly from its value obtained by the RSM. This research is conducted to reveal the rigidity of the machine tool in order to select an appropriate spindle motor for retrofit purpose. The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit. Therefore, the servo motor with smaller power than the original motor is selected which is cost-effective and it will not cause inappropriate strong vibrations that lead to the unexpected surface roughness and excessive noise inside the Learning Factory environment in which the machine tool is used

Application of AHP, Fuzzy AHP and PROMETHEE Method in Solving Additive Manufacturing Process Selection Problem

Additive manufacturing is a technology of making a three-dimesional solid object of any shape from a digital model. Today on the global market exist various additive manufacturing processes. All of these processes build parts by applying material layer by layer. In a wide range of different processes there is a problem of selecting an adequate process for a user or company interested in additive manufacturing technology. Solving of such a problem is possible by using multicriteria decision methods which result in ranking of alternatives. Thus the user or company can easily select one of the available additive manufacturing processes. In this paper basic methodology of application of three different multicriteria decision methods in solving the mentioned problem was shown. These methods are: Analytic hierarchy process (AHP), Fuzzy AHP and Preference ranking organization method (PROMETHEE). Available alternatives are processes: 3D printing, Fused Deposition Modeling, Selective Laser Sintering and Photopolymer Jetting

Single-objective and multi-objective optimization using the HUMANT algorithm

When facing a real world, optimization problems mainly become multi-objective i.e. they have several criteria of excellence. A multi-criteria problem submitted for multi-criteria evaluation is a complex problem, as usually there is no optimal solution, and no alternative is the best one according to all criteria. However, if a metaheuristic algorithm is combined with a Multi-Criteria Decision-Making method then, instead of submitting all solutions, only near-optimal solutions are submitted for multi-criteria evaluation, i.e. compared and ranked using a priori decision-maker preferences. It is called an a priori approach to multi-objective optimization. This paper presents this approach using a specially designed HUMANT (HUManoid ANT) algorithm derived from Ant Colony Optimization and the PROMETHEE method. The preliminary results of this optimization algorithm are presented for the Single-Objective Traveling Salesman Problem (TSP), Shortest Path Problem (SPP) and the Multi-Objective Partner Selection Problem (PSP). Additionally, the multi-objective approach of the HUMANT algorithm to single-objective optimization problems is presented using the Shortest Path Problem (SPP)

Utilization of Industry 4.0 Related Equipment in Assembly Line Balancing Procedure

In recent decades, production in high-volume/low-variety batches is replaced with low-volume/high-variety production type. This type of production demands excessive flows of both material and information. Recent advances in information and communication technologies (ICT), together with the concept of cyber-psychical system (CPS) enable the concept of Industry 4.0 (I4.0). In this paper, the performance of I4.0 related equipment implementation is presented in iterative assembly line balancing (ALB) process of a gearbox assembly line. Largest candidate rule method through spreadsheet simulation was used for tasks reallocations, with the objective to minimize the cycle time when the number of stations is fixed. Utilization of human analysts using snap back method for manual data gathering process still shown advantage over I4.0 equipment utilization in manual ALB. The assembly process is performed in the learning factory environment, and it is considered as very close to real industry process. The major conclusion is that I4.0 is excellent in process data monitoring and product tracking, but activities to be performed to effectively exploit I4.0 is demanding for task reallocations during the balancing procedure. Nevertheless, future enhancements of I4.0 system are listed to bridge this gap and to increase I4.0 system usefulness in the manual assembly line balancing process

Simulation of production process reorganized with Value Stream Mapping

Filozofija Lean proizvodnje nudi široku paletu prednosti. Lean koncept proizvodnja predstavlja ostvarenje Lean principa kroz učinkovitu primjenu metoda i alata, za brže i učinkovitije otkrivanje gubitaka i pogrešaka u proizvodnim sustavima. Ovaj članak sadrži prikaz korištenja simulacije proizvodnog procesa koji je prethodno analiziran i reorganiziran pomoću Mapiranja toka vrijednosti. Model je primijenjen za jednu proizvodnu liniju u hrvatskoj brodograđevnoj industriji. Trenutno stanje proizvodne linije je simulirano s varijabilnim zahtjevima tržišta. Korištenjem alata Lean proizvodnje, razvijeno je i simulirano buduće stanje toka vrijednosti. Simulacija omogućava dobivanje i analizu dinamičkih modela s promjenjivim performansama proizvodnog sustava, za razliku od primjene samo alata Mapiranja toka vrijednosti. Tijekom simulacije, korištena je prilagodba proizvodnog procesa variranjem pridodavanja resursa radnoj stanici, koja je služila za ispunjavanje zahtjeva tržišta na vrijeme.The philosophy of Lean production offers a wide variety of advantages. Lean production concept is understood as the realization of Lean principle through the efficient application of methods and tools, with faster and more effective discovering of wastes and mistakes in the production systems. This article contains a presentation of the simulation application on production process previously analyzed with Value stream mapping tool. The model was applied to a production line in the shipbuilding industry in Croatia. The production line current state is simulated with varying market requirements. Using Lean management tools, future improved state was developed and simulated. The simulation provides dynamic models with varying production performance results, unlike by applying only a Value stream mapping tool. During simulation runtime, adaptation of production process by varying resources assignment to continuous working cell was done to fulfill order requirements on time

Optimal Scheduling of Rainwater Collection Vehicles: Mixed Integer Programming and Genetic Algorithms

Due to climate change, some areas in the world witnessed higher levels of heavy rain than the capacity of the wastewater system of the streets. Therefore, water tankers are used for the dewatering process to take the extra rainwater from the streets to keep a smooth flow of vehicles and to use the water in agriculture and industry. Water is taken to a water treatment plant. Performing the dewatering process as fast as possible, especially in crowded streets, was ignored by researchers. In this study, at first, the problem was solved using two mixed integer programming (MIP) models. A new variant of identical parallel machine scheduling with job splitting is proposed for the first time, where one or at most two tankers can work at the same flood location at the same time. This is performed in the second model. However, the first model considers dividing the dewatering processes into two phases, where the first one, which is more urgent, is to reduce the amount of floodwater. The second one is for dewatering the rest of the water. Then two genetic algorithms (GAs) were used to solve faster the two MIP models, which are NP-hard problems. At first, the MIP and GA models were applied to small-sized problems. Then GA was used for large practical data sets. Results showed that for small problems, MIP and GA gave optimal solutions in a reasonable number of iterations, while for larger problems, good solutions were obtained in a reasonable number of iterations

Selection of the basic Lean tools for development of Croatian model of Innovative Smart Enterprise

Cilj istraživanja u sklopu projekta Inovativno pametno poduzeće (INSENT) bio je unaprijediti znanstvenu spoznaju o trenutnom stanju hrvatske prerađivačke industrije kroz empirijska istraživanja, na razini poduzeća, o tehnološkim i ne-tehnološkim procesima i organizacijskoj inovaciji. Cilj je bio razumjeti na koji način prerađivačka poduzeća u Hrvatskoj usvajaju nove proizvodne tehnologije, integriraju IC tehnologiju unutar svojih procesa, usvajaju nove organizacijske koncepte u proizvodnji kao što je rad u skupinama ili relokacija proizvodnje, kako razvijaju nove proizvode proizašle iz procesa i inovativnosti organizacije kao što su proizvodno orijentirane usluge, te ostale zahtjeve Industrije 4.0. U svrhu razvoja hrvatskog modela inovativnog pametnog poduzeća (HR-ISE model), napravljena je analiza globalnih i lokalnih poduzeća utemeljena na istraživanju literature i upitnicima. Napravljen je odabir šest osnovnih alata, te su postavljeni temelji generičke konfiguracije HR-ISE modela. U daljnjem istraživanju, intervjui s menadžerima vodećih hrvatskih proizvodnih poduzeća pomoći će bolje definirati HR-ISE model.The research within project Innovative Smart Enterprise (INSENT) was conducted in order to improve scientific understanding of the current state of Croatian manufacturing industry by promoting empirical, enterprise-level research on technological and non-technological processes and organizational innovation. The aim was to understand how manufacturing enterprises in Croatia acquire new manufacturing technologies, ICT integration within processes, new organizational concepts in production such as group work or relocation of production, new products that emerge from process and organizational innovation such as production-related services, and other demands related to Industry 4.0. In order to develop Croatian model of Innovative Smart Enterprise (HR-ISE model), analysis of global and local enterprises, based on literature review and questionnaires, has been made. A selection of six basic Lean tools is made, and foundations of generic configuration of HR-ISE model are defined. In further research, interviews with CEOs of leading Croatian manufacturing enterprises should help creating completely defined HR-ISE model