This paper proposes a novel method to check out if the correct item is present in an automated industrial process, after a human input of item ID has been made. Traditional methods such as bar code scanning, RFID, and camera vision are limited, complex or inconsistent in some scenarios. The proposed method utilizes the same robot used in the process, without the need for integrating extra systems. The proposed method is demonstrated in a robotic cleaning station, where the robot verifies the present mold ID by touching down the loaded mold at a few points before beginning the correct cleaning routine. To minimize the number of touchpoints required, the system was mathematically formulated after the classical set covering problem, which was then solved optimally for the case study, offline aside from robot operation, and only once for each mold. Overall, this paper presents an effective and efficient solution to a common industrial problem, with potential applications in a wide range of scenarios.

Designing and fabricating different fixtures are among the main barriers for manufacturing systems to produce a variety of products with different geometries. To overcome the time and costs associated with the frequent changes in fixture design, modular fixtures have been developed. The changeability plan of these fixtures is vital for changeable manufacturing systems, especially in automated systems in which robots are in charge of placing and securing parts in their respective places. In this paper, we are extending the model presented in the literature in two directions to further reduce the fixture setup time in a mid-volume mid-variety automated production system. First, we consider both vertical and horizontal movement of the robot to find the optimal changeability plan. Then, a new fixture design is introduced that improves the fixture modularity to hold more products with different geometries. The results prove that the newly proposed models can significantly reduce fixture setup time.

Learning factories demonstrate applications and technology to students in a real industrial environment. While turnkey changeable learning factories are supplied by many vendors, with some digital twin capabilities, they are mostly a closed box, with very little flexibility to change the underlying architecture or technology, hindering the maximum benefit of student hands-on experience. This paper presents a method to build a simulated changeable learning factory and link it to the physical system to create a digital twin. The studied learning factory (LEAF) is an opensource low-cost changeable automated system. The suggested digital environment is ‘RoboDK’, which is a 3D simulation and offline/online programming environment, mainly for industrial robots, but it also offers an open source ‘Python’ programming library, allowing the extension of the capabilities of the software to adapt to LEAF. The method is also using the opensource Modbus TCP and OPC UA industrial communication protocols to establish the connection between the physical modules and the digital objects. The results show a capable digital system that is accurately mirroring the physical system layout and material flow, with a flexible structure to allow future extensions.

Data is collected from different industrial domains. Organizing that data makes change anticipation more planned and streamlined. This paper introduces a novel holistic model of associating different domains of industrial data. The model establishes a tree graph called cladogram to create a unified classification of data from market segments, product design and manufacturing capabilities and it is expandable beyond these domains. The cladogram is produced by the widely used biological Cladistics analysis, without modification. This approach has a great degree of simplicity without introducing an extra layer of mathematical modelling, while resulting in a data-inclusive graphical representation. A case study of automated and flexible assembly is presented to demonstrate the effectiveness of the model and its simplicity. Model results are significant, since they could reveal associations of the definitions of the objects from different data domains, which were used later in response to future changes in those domains.

In a dynamic production environment, not only the customer's needs change with time, but the economic aspects of that environment, such as energy pricing, also change. Reconfigurable Manufacturing Systems (RMSs) are designed to respond to such changes by reconfiguring system components efficiently. This paper presents a novel mathematical model to maximize energy sustainability of RMS. The novelty aspect of the model is the consideration of energy sustainability concurrently with system configuration and scheduling decisions in each period of the planning horizon. The objective of this mixed integer linear model is to minimize the total cost of energy consumption, system reconfiguration, and part transportation between machines, depending on fluctuations of energy pricing and demand during different periods. Several case studies are solved by GAMS Software to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand to show their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the model in larger scale due to its NP-hardness and compared to GAMS for validation. Results show that the presented GA finds near-optimal solutions in 70% shorter time than GAMS on average.

Educational and research manufacturing systems, such as learning factories, provide an environment to learn, test and implement new product and business solutions, research ideas and system paradigms. When learning factories become physically changeable, they are called Changeable Learning Factories (CLF), and can be used for investigating and teaching the effects of change of product design and production planning on manufacturing system layout and control. However, changeability requires a high level of system granularity and complexity, accompanied to a tendency to prevent students and trainees from developing deeper understanding of the underlying technology, or being able to change the physical system components on the machine level, especially for turnkey solutions. This paper introduces a model that selects the best system design from a pool of learning factory configuration alternatives, such as different types of material handling systems, individualized vs. clustered components, number of material routes and decision-making nodes, etc. This paper uses a selection model based on system structure complexity that changes with system granularity level. Results show that highly granular modular learning factories, and their complete opposite, low granular integrated learning factories have higher complexity than middle level granular learning factories, that are operationally changeable while being simple to understand and physically being able to change on the machine level.

Modular assembly systems are a category of changeable manufacturing systems, which can handle the rapid change in customer demands, product design change and market fluctuations. On the operational level, jigs and fixtures are fundamental elements of assembly systems. They are used to hold parts and subassemblies in place, and directly affect assembly cost, quality and time. Therefore, modular fixtures that can adapt to different geometries are becoming a very important enabler for changeable manufacturing. In this paper, two mathematical models are presented to optimise the use of a passive modular assembly fixture plan in an automated assembly system by considering different production scenarios and constraints. These models optimise the changeability plan of the modular fixture by minimising the number of dowel replacements between different part geometries assuming that the candidate dowels locations for each part have been determined using existing methods in the literature by considering different assembly requirements. The first model, LRTE, considers all possible part rotations and translations on the fixture to minimise setup time. In addition, the second model, SLRTE, enables the system to simultaneously optimise job sequence. This paper presents various examples in different sizes, and the results show that the model can effectively reduce the fixture setup time up to %50.

This paper presents a new methodology for designing modular products taking into account assembly complexity and Design for Assembly. DFA encourages components integration to reduce assembly time, while modularity promotes partitioning and decomposition of product architectures into components and modules to facilitate interchangeability. A product granularity level determines the details of its architecture. The best product architecture granularity level, and size and number of product modules are determined using Cladistics - a hierarchical classification tool - along with the Design Structure Matrix (DSM) and assembly insertion and handling complexity indices. A case study using a DC vibration motor is used to demonstrate the application of the presented methodology. It is observed that increasing assembly complexity promotes coarser product architecture granularity and more modularity results in finer product granularity. Use of the presented design methodology achieves the best trade-off between products modularity and integration.

Manufacturing systems in Industry 4.0 are changeable, smart, connected and more autonomous. The structure of a changeable manufacturing system allows for physical reconfiguration, however, reprogramming controllers has been always performed manually for each new system configuration. The presented model combines different ladder logic codes corresponding to different system configurations, modularizes them and produces smaller pieces of code, which automatically get merged and downloaded to the different system controllers. The model uses Cladistics and Design Structure Matrix (DSM) to prepare the modular codes. A case study of a changeable robotic assembly system is presented.

A new design synthesis model is introduced to enhance manufacturing systems energy sustainability. It uses Design Structure Matrix (DSM) to represent relationships between system components, Cladistics analysis to produce an architectural map of the system at different granularity levels as well as energy consumption data of individual system modules. It synthesizes a system architecture for minimizing its energy consumption. A changeable assembly system is used for demonstration and validation. Results showed that energy consumption of manufacturing systems can be minimized throughout the production planning horizon by system design. Manufacturing systems design for energy sustainability compliments other energy use reduction methods.

Increasing product varieties is beneficial for companies in terms of expanding the market and harmful in terms of increasing manufacturing costs. Designing and fabricating different fixtures for producing different products with different geometries is a significant portion of the manufacturing costs. To overcome to this problem in a mid-volume mid-variety robotic assembly system, an optimization model is developed to minimize hole-pattern modular fixtures’ preparation time and efforts. Using this model, the best locations for placing different products and jigging-pins are determined, considering all possible part's translations and rotations on the holder. The model is solved by GAMS using the BARON solver for different examples to prove the efficacy of the proposed model.

Changeable Manufacturing Systems have the capability to adapt to varying production plans and different product designs by changing their configuration and layout. This paper presents a new linear mixed integer mathematical model to maximize sustainability of Changeable Manufacturing Systems based on the daily varying energy pricing. The daily production demand of several product variants has to be satisfied by corresponding configuration of the manufacturing system. System configuration planning consists of machine arrangement and job sequencing for each planning day. The proposed linear mixed integer mathematical model is solved by CPLEX solver in GAMS software for nine different problem sizes. The new LMI model finds the optimum configuration plan and job sequence in a reasonable time, which illustrates the efficiency and practicality of the proposed model.

This paper presents a design methodology to modularize integrated fixtures, such as automotive framing systems, to be quickly and cost effectively reconfigured to accommodate a variety of products. Automotive assembly framing systems are used to accurately position and spot-weld the loosely pre-assembled body-in-white (BIW) car body parts. Auto-assembly systems can handle many car body styles; however, the used model-specific BIW framing systems are large, expensive, and the changeover to accommodate different car models takes considerable time. The proposed modularization design methodology aggregates a set of design structure matrices (DSMs) to represent the required changes in the fixtures, the spatial relationships between the used tools and fixtures, and the flow of exchanged information between them. The best granularity level of the modular fixture design architecture is determined using "Cladistics": a hierarchical biological classification tool. Different tools within the framing system are combined into switchable modules, which allows these integrated systems to be easily reconfigured between different car body styles (product variants). A case study involving four car body styles is used for illustrating the presented design methodology. Results show the validity of the proposed methodology and demonstrate the obtained design of new modular automotive BIW framing system and the methods used for postprocessing and redesigning to improve the framing system's changeability.

Experiential teaching and research facilities such as learning factories provide a favorable environment and tools to develop, test and implement new products and manufacturing systems concepts and solutions. Learning factories that are geared towards the capabilities and requirements of changeable manufacturing systems (CMS) are used for validating new products, their variants and changeable production systems design, planning and control methods. Existing approaches of developing new products are reviewed, and a new approach to develop products for changeable learning factories is presented and validated by a case study. The new development approach is dedicated to changeable learning manufacturing systems by choosing a product line among many candidates to suite the learning system capabilities and desired learning outcomes. Designs for product variants that suite the learning factory and planned learning scenarios and training experience. This paper focuses on learning factories for changeable manufacturing systems, which are constructed to possess the necessary changeability enablers such as mobility, modularity, scalability, universality and compatibility. It is challenging to associate learning and research to a changeable manufacturing system.

Changeable manufacturing systems offer a high level of adaptability and agility in response to product and market changes. They are characterized by modularity and scalability, which are derivatives of system granularity. Determining the best granularity level of a changeable system helps maximize its ability to change throughout its planned utilization horizon. A new model and two case studies are presented to show: (1) new changeability design structure matrix (CDSM) to express all planned system configurations, (2) cladistics analysis to hierarchically cluster CDSM into component modules, and (3) new granularity index (GI) to determine the best system granularity level which balances the merits of manufacturing system modularity with integration.

The structural complexity of a manufacturing system results primarily from the complexity of its equipment and their layout. The balance between both complexity sources can be achieved by searching for the best system granularity level, which yields a manufacturing system with the least overall structural complexity. A new system granularity complexity index is developed to sum up and normalize the complexity resulting from the system layout complexity and the equipment structural complexity. A previously developed layout complexity index together with a code-based structural complexity assessment are used to determine the structural complexity of standalone pieces of equipment and to arrive at a balance between the two sources of complexity. Cladistics analysis is used to hierarchically cluster required pieces of equipment and bundle them into more integrated equipment and machines and demonstrate the possible different system granularity levels. The new developed model is a useful tool to create specific system configuration and layout alternatives based on system components adjacency, and then select the system design with the least overall structural complexity among those alternatives. The results of the presented case study clearly demonstrated this trade-off where decomposing manufacturing systems into a highly granular configuration with standalone machines maximizes system layout complexity and minimizes equipment complexity, while at a low level of granularity pieces of equipment are bundled into complex integrated machines, lines or cells but with a very simple system layout.

It is a global trend nowadays for manufacturing and service firms to create and increase customer value either during initial design of a product/service or by modifying their existing products/service. When a product already exists, customer value can be increased by adding new qualities/features to a traditional product that would add much needed services while keeping price competitive. Qualities, such as foldability and mobility when product is not in use, are examples of creating and improving customer value. This paper presents a design model that helps designers incorporate foldability, mobility and personalization in a regular product design.