Process Simulate Tecnomatix provides digitalised manufacturing capabilities that empower you to realise innovation of your products and manufacturing processes. The software combines all manufacturing activities with product engineering to help you plan, simulate and validate entire manufacturing operations before the physical environment is created. Streamline your processes using Process Simulate Tecnomatix planning functionalities that include work instructions, layout and dimensional quality. Simulate real-life scenarios of how your production will perform from the assembly line, robotics and logistics, to human interaction.
Process Simulate Tecnomatix enhances production of your workflow processes and assists with achieving cost, quality and delivery targets through issue tracking, shop floor and build quality, and virtual commissioning. By creating a digital twin of your manufacturing system you are able to test and analyse various scenarios to optomise production. In recent times organisations have been using the software to implement social distancing to keep employees safe and adhere to new guidelines.
Tecnomatix Process 27
Process Simulate Tecnomatix uses Teamcenter to provide you with powerful product and production integration. This will drive innovation, better decision making and ensuring your products get to market fast. Process Simulate Tecnomatix is used in many industries and allows you to configure data structures, work processes and business rules to meet your own requirements.
Many companies adapt to survive, but leaders are seizing the opportunity to be more flexible, productive and competitive. They are reaping the benefits of a complete PLM strategy that includes Tecnomatix digital manufacturing as a vital component. Tecnomatix solutions optimise the business processes that determine your ability to get to market faster. From product development through delivery, Tecnomatix aligns manufacturing capacity and capability with design intent, thereby reducing long lead-time processes, supporting price premiums, capturing market share and increasing brand value.
Siemens Manufacturing Software Solutions enable manufacturers to define and execute a wide range of traditional and new manufacturing processes. This includes Nesting, CNC machining, bending, cutting, welding, molding, assembling, and additive manufacturing. Any manufacturing instructions created in CAD can easily be updated to reflect any changes in the underlying design. CAM solutions work on part, sheet metal and assembly models to help ensure efficient and accurate manufacturing processes.
USD is agnostic about the way material properties are represented. NVIDIA is working to let artists be able to author materials for cinema-quality rendering using an automated process that produces simpler but still high-quality shaders in real time.
Plant Simulation is a Material flow simulation Software (Discrete Event Simulation; DES Software). Using simulation, complex and dynamic enterprise workflows are evaluated to arrive at mathematically safeguarded entrepreneurial decisions. The Computer model allows the user to execute experiments and to run through 'what if scenarios' without either having to experiment with the real production environment or, when applied within the planning phase, long before the real system exists. In general, the Material flow analysis is used when discrete production processes are running. These processes are characterized by non-steady material flows, which means that the part is either there or not there, the shift takes place or does not take place, the machine works without errors or reports a failure. These processes resist simple mathematical descriptions and derivations due to numerous dependencies. Before powerful computers were available, most problems of material flow simulation have been solved by means of queuing theory and operations research methods. In most cases, the solutions resulting from these calculations were hard to understand and were marked by a large number of boundary conditions and restrictions which were hard to abide by in reality.
Lately material flow simulation gains growing importance through the increasing use for considering the sustainability of industrial production processes. Here the characteristics of sustainable manufacturing are simulated and analyzed beforehand and then integrated into the investment decision process.Plant Simulation is also used for research and development purposes at a great number of universities and universities of applied science.
So, the main objectives of our research are 1) to establish a standard time for the manual process, 2) to increase working time and reduce idle time, 3) to increase line efficiency, and 4) to test the feasibility of layout by computer simulation. This paper is organized into 5 sections, Section-1 cover introduction, Section-2 literature review, Section-3 methodology, Section-4 case study, and Section-5 cover conclusion and all of these are described below.
Most of all industries typically follow the ancient assembly line flow of production. Various sub-assembly processes are used for the assembly of product parts. Chan et al. (1998) have stated that the entire assembly process is a set of workstations where specific work is carried out in a particular sequence, with minimum number of employees and thousand types of sub-assemblies producing different styles. Cooklin et al. (1991) have stated that the process components along with the production process are considered as the major labor-intensive part of manufacturing [5] . Helgeson et al. were the first to propose the assembly line balancing problem (ALBP) and was the first to explain the problem in its mathematical form. One main challenge concerning the development of an Assembly Line is to arrange the task to be performed. During the first forty years of the Assembly Line invention, only trial and errors were used to balance the line [4] . In cell all processes are need to set according to product specification. All workloads need to level across all processes in a cell. Bottlenecks, excess capacity can be improved [6] . Assembly Line Balancing or simply Line Balancing is the problem of allocating operations in such a way that the assignment to be optimal in some sense. Line Balancing has been an optimization problem of significant industrial importance. The first article was published by Salveson et al. (1955) where he used integer programming model [7] . In the line balancing problems, the elements of task is to distribute the tasks to workstations such that a certain objective (number of workstations, idle time production rate etc.) is optimized and precedence constraints is not violated. The workstation time must not exceed the given cycle time. The processing time of tasks are also given. The cycle time of an assembly line is predetermined by a desired production rate [8] . Grzechca et al. (2011) have mentioned that the cycle time of an assembly line is predetermined by a desired production rate in a way that the desired amount of end product is produced. In this regard, one of the main issues is how to arrange the tasks in the production line to be performed. An effective way to achieve this goal is to balance the assembly lines [9] . Although there are quite a lot of heuristic methods, some basic ones are: Ranked positional weight method (Helgeson-Birnie), enumeration method (Jackson), Hoffman method, Moddie-Young method, COMSOAL method (Arcus), dynamic programming method, Kilbridge-Wester method, candidate matrix method (Salveson), probabilistic assembly line balancing method (Elsayed-Boucher), grouping method (Tonge), shortest path method (Klein-Gutjahr), Raouf-Tsui-Elsayed method, related activity method (Agrawal), and basic heuristic method [10] . Becker & Scholl et al. (2006) have stated that assembly lines configurations for single and multiple products are divided by three types, single-model, mixed-model and multi-model. Single-model assembles only one product, and mixed-model assembles multiple products, whereas a multi-model produces a sequence of batches with intermediate setup operations [8] . Hop used an old logic that is fuzzy logic for solving line balancing problem like mixed model problems. By using fuzzy logic cycle time as well as processing time can be reduced. For solving line balancing problem like ALBP this technique shows outstanding performance [11] . Like fuzzy logic various technique can be used for solving line balancing problem. Algorithms also can be used for line balancing problem. Such algorithms are Neutral Networks, Ant Colony Optimization, and Genetic Algorithm (GA). But among these GA is best for solving [12] . This paper focuses on single model line balancing problem with real application in garment manufacturing industry. Naresh stated that, as sewing department involves manual labor, the process often resulted in a high cycle time and low productivity. There are lots of different operations done manually and sewing operations needs high skill as well as quality work [13] . Since sewing process is related to manual labor, without material costs, the cost structure of the sewing process is also important. Tyler et al. (1991) have stated that sewing process is of critical importance and needs to be planned more carefully [14] . Each operator is needed to carry workloads properly thus asynchronous flow is gained throughout the entire production line.
data. Table 1 Represent the production data. The line consists of 27 number of workstations. Also, man power whom was involved in line-5. Individual capacity of each process was also presented in this table for the same line. This whole data is of production line-5.
Current non-balanced data (through-put) are shown in Figure 3 by using tecnomatix simulation software. Figure 4 represent the through-put per day after balancing the production line. These figures are given below. 2ff7e9595c
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