Mass Customization Production Research Articles

Influencing the forming zone by altering the contact pressure in a bending process

In a modern production environment, flexible manufacturing methods are important because an overall trend towards mass customization and on-demand production is observed. Kinematic and incremental forming methods with generic tools can provide a large product variation but a deeper understanding of the forming mechanisms is required for process modelling, e.g. Incremental Swivel Bending (ISB). Particularly crucial is to identify the influences on the forming zone in order to purposefully control the material flow of a forming process. For a bending process where the bending moment is transmitted by clamping tools, this paper presents a method to alter the contact pressure distribution in order to affect the angular size and strain gradient of the forming zone. In the light of these results, the presented method can be deployed for a tooling with adaptive contact pressure to directly influence material flow, in particular using generic tools to overall provide a better control of flexible forming methods.

Fast development cycle for the design of industrial grippers

Recent trends, such as Supply Chain agility, just in time delivery, and mass customization of products, are pushing automated production processes in Industry 4.0 towards increasing flexibility. Although the entire set of devices is already on the market and can be selected according to the needs, the element that regularly has to be redesigned is the robotic end effector, mainly a gripper. Therefore, we established a Fast Development Cycle to accelerate the design and test process of new industrial grasping devices. The cycle consists of the three main steps: Build, Test, and Learn. The most fundamental aspect of the methodology is to decompose a gripper idea into its essential uncoupled constituents and convert it into a gripper pretotype that is solely oriented to validate its basic principles. This procedure ensures a quick initial building phase and allows to enter testing early. Test results are used to evaluate the initial idea, to enhance knowledge and to create the input for the next turn of the cycle. During each turn the design evolves further while reducing uncertainties. Contrary to traditional product development, this method enables to test the feasibility of a device in the earliest possible stage with the least possible amount of time and money. The single steps of the methodology are illustrated in this paper in detail based on real cases from industry. The successful development of industrial grippers from these real cases through the Fast Development Cycle demonstrates its applicability. Instead of optimizing complex systems, the methodology generates simple solutions with a high potential for cost savings in the design and production process of the devices themselves and during their operation in automated production processes in several industries. The methodology applies regardless of the underlying physical principles and encounters the use of TRLs as KPIs to measure technology maturity.

Mass customization based on generic technology and modular production method

Mass customization combines mass production with personalized customization, which avoids high cost, limited batch and long delivery time in customized production, and low flexibility and poor market reaction ability in mass production. However, due to the customization of its production strategy, mass customization production has disadvantages in cost and time in the production process. Aiming to these problems, this paper studies the promoting role of generic technology and modular customization methods in mass customization production mode. Through modular organization, modular design, modular manufacturing and modular assembly in industrial chain resources, to maximizes production capacity and achieve the goal of reducing enterprise production costs and improving enterprise production efficiency.

Research on the Practical Application of Mass Customization Balance Theory from the Perspective of Supply Chain

Mass customization is the main direction of the transformation and upgrading of manufacturing mode. Based on the theory and perspective of supply chain, this paper reinterprets the content of the mass customization balance model, and summarizes the key technical conditions for the selection and implementation of the best dividing point between the balance point and the balance area in the mass production and customization production modes, and analyzes the actual cases, so as to break through the gap between the various theoretical knowledge modules. The purpose is to simplify the complex problems, which can be used for reference and guidance for enterprises in various industries.

Mass Customization Demonstrator at an Industry 4.0 Learning Factory

The industry has been working to increase product variety while maintaining economies of scale for many years already. Mass customization strategies, product platforms, and modularity have provided the conceptual fundaments for increased product variety, targeting segmented customer needs in the last decades. Recently, technologies related to Industry 4.0 promise the needed production flexibility to support higher levels of product customization. Although the required concepts and technologies are already available, their integration for demonstration and teaching is still challenging. In this context, we discuss which technologies need to be integrated into a Learning Factory to create a comprehensive mass customization demonstrator. We also discuss how to teach mass customization leveraging the Learning Factory demonstrator. Therefore, a case study has been conducted. In order to create the mass customization demonstrator, a customizable modular product has been selected. The case product is a modular skateboard. Customization features include the wheel colors, options (rails and IoT connectivity), and the shock absorber levels (continuous scale from soft to hard). Based on an Industry 4.0 technology framework from the literature, the required technologies have been identified. The range of technologies includes: product configurator, ERP, MES, automatic computational vision quality control for the assembly, automatic torque wrench, augmented reality for assembly instructions, and additive manufacturing for the low scale, high variety production of options. Next, the technologies have been integrated. In the integration, the ERP and MES systems play a key role in managing the parts production and product assembly based on the product configurator’s individual customer orders. The demonstrator has been applied in teaching in a short-term continuing education executive program and undergraduate engineering courses. The results explore the potential benefits and limitations of the mass customization demonstrator based on Industry 4.0 technologies for supporting knowledge and technology transfer to the industry.

Resource Assignment for Machining Shop in a Mass Customization Production Environment

Resource Assignment for Machining Shop in a Mass Customization Production Environment

Operator support in human–robot collaborative environments using AI enhanced wearable devices

Nowadays, in order to cover the needs of market for product mass customization, industries have started to move to hybrid production cells, involving both robots and human operators. Research has been done during previous years to promote and improve the collaboration between humans and robots, trying to address topics such as safety, awareness and cognitive support in form of Augmented Reality based instructions. Results of previous research show bottlenecks related to the way of interaction of the operators with such supportive systems though. Direct interaction approach with the use of push buttons or indirect-gesture based interaction, which are most often adopted by the researchers, require operators to constantly occupy their hands performing the relevant button presses or gestures. Moreover, previous approaches are hardware dependent and need a lot of customization to work with different hardware. This work tries to address these bottlenecks proposing the usage of wearable devices enhanced with AI in order to support the interaction of human operators with robots in human-robot collaborative environments in a seamless and non-intrusive way, wrapped around a framework called “Operator Support Module” (OSM). Among others, OSM supports a variety of hardware to easily fit in various industrial scenarios. Two case studies will be presented to demonstrate the approach.

Efficient virtual garment fit evaluation infrastructure based on synthetic avatar target customer groups for MtM application

Customization becomes more and more popular and influences the product development process in apparel industry. In addition to individualized products, the fit of garments is very important for the customization. Numerous tools are used to take the right measurements, to transport individual posture information and to implement these data correctly into a product pattern based on a predefined construction system. Unfortunately, in most cases the mass customization process takes place without a fitting session. Usually fit and design will be checked in the last process step, when the product is already manufactured. Virtual product development is a powerful tool to change this process getting an early fit and design check. By using a test population representing the target group, it is possible to check the sizing and to screen the fit of a product on individual bodies and postures in a short time. In a joint project between the Virtual Lab of Niederrhein University of Applied Sciences and Avalution GmbH, a practical approach for the implementation of a fitting session to a mass customization product development process was developed. The entire process has a three-level structure: First, the avatar population is built up using garment specific body measurements. Connected to a 3D simulation program, an automatic process of determining the made-to-measure (MtM) values, carrying out the MtM grading and the fitting on the selected avatar are initiated. In a special application, the digital try-ons are finally output as images in different physical aspects for evaluation.

A hierarchical parallel multi-station assembly sequence planning method based on GA-DFLA

In recent years, the parallel assembly sequence planning (PASP) is put forward to accommodate the development of greater variety in the mass-customization production. Although many researchers have made contributions to assembly sequence planning (ASP), how to construct parallel multi-station ASP model to effectively solve the integrated ASP and assembly line balancing (ALB) problem need to be further investigated. In this paper, a hierarchical parallel multi-station assembly sequence planning method based on the genetic algorithm and discrete frog leaping algorithm (GA-DFLA) is proposed to solve the integrated parallel multi-station ASP and ALB problem. The assembly information datasets of the model based definition (MBD) including parts information, hierarchical information, matrix information and resource information are defined firstly. Then the hierarchical structure tree is constructed according to the divided assembly units. The hierarchical parallel multi-station assembly sequence planning is carried out from bottom to top in hierarchical structure tree model using the GA-DFLA. The fitness function with feasibility index, time cost index and assembly line balance index, is proposed to determine a more appropriate solution. Finally, the rear independent suspension is taken as an example to validate this method. The results show that the time of parallel multi-station model is at least 35.27% less than the time of serial multi-station model.

Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence

Smart manufacturing offers a high level of adaptability and autonomy to meet the ever-increasing demands of product mass customization. Although digitalization has been used on the shop floor of modern factory for decades, some manufacturing operations remain manual and humans can perform these better than machines. Under such circumstances, a feasible solution is to have human operators collaborate with computational intelligence (CI) in real time through augmented reality (AR). This study conducts a systematic review of the recent literature on AR applications developed for smart manufacturing. A classification framework consisting of four facets, namely interaction device, manufacturing operation, functional approach, and intelligence source, is proposed to analyze the related studies. The analysis shows how AR has been used to facilitate various manufacturing operations with intelligence. Important findings are derived from a viewpoint different from that of the previous reviews on this subject. The perspective here is on how AR can work as a collaboration interface between human and CI. The outcome of this work is expected to provide guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future.

Fast Industrial Product Design Method and its Application Based on 3D CAD System

With the rapid development of science and technology and the emergence of new technologies, the speed of product upgrading has accelerated, and the life cycle has been significantly shortened, leading to increasingly fierce market competition. Through the analysis of the enterprise product development process, combined with CAD-based product rapid design technology and parametric design technology, this paper proposes a product configuration editor based on three-dimensional software with a visual interface, and combines it with the PDM system. A software system that can be applied to actual product development is formed. Finally, the feasibility of a vehicle steering gear product design process is proved in practice. The research of this article mainly has the following two aspects of academic significance: this article describes the process of product development and design by enterprises under the mass customization production mode. After analyzing the application of CAD technology in the product design process, we studied how to use advanced design knowledge to combine specific three-dimensional CAD software for rapid product design, which can guide manufacturing companies to better carry out product design work. It is proposed to improve and perfect the initial configuration results of products in a specific three-dimensional design environment, and it can communicate with data management systems such as PDM. The configuration results can be edited and modified in the 3D design environment, and when specific parts need to be modified and designed, the parametric module embedded in the 3D design software can be used to design directly, avoiding multiple heterogeneous systems switching back and forth between, shorten the development cycle. In addition, for enterprises, the research in this article is also of great application significance.

On the development of a collaborative robotic system for industrial coating cells

For remaining competitive in the current industrial manufacturing markets, coating companies need to implement flexible production systems for dealing with mass customization and mass production workflows. The introduction of robotic manipulators capable of mimicking with accuracy the motions executed by highly skilled technicians is an important factor in enabling coating companies to cope with high customization. However, there are some limitations associated with the usage of a fully automated system for coating applications, especially when considering customized products of large dimensions and complex geometry. This paper addresses the development of a collaborative coating cell to increase the flexibility and efficiency of coating processes. The robot trajectory is taught with an intuitive programming by demonstration system, in which an icosahedron marker with multicoloured LEDs is attached to the coating tool for tracking its trajectories using a stereoscopic vision system. For avoiding the construction of fixtures and allowing the operator to freely place products within the coating work cell, a modular 3D perception system was developed, relying on principal component analysis for performing the initial point cloud alignment and on the iterative closest point algorithm for 6 DoF pose estimation. Furthermore, to enable safe and intuitive human-robot collaboration, a non-intrusive zone monitoring safety system was employed to track the position of the operator in the cell.

A Framework for Designing the Customer-Order Decoupling Point to Facilitate Mass Customization

Abstract In the age of Industry 4.0, manufacturing enterprises are under pressure to improve mass customization to satisfy evolving demands in different markets. One challenge is to fulfill orders swiftly at an acceptable cost, while maintaining service quality. To do this, the customer-order decoupling point (CODP), where value-adding activities take place, should be designed and adapted to changing market demands. We propose a formulation-exploration framework to make decisions on customer-order decoupling point positioning and improve the supply chain to support mass customization. A test problem of auto parts manufacturing is used to illustrate the efficacy of our framework. The formulation-exploration framework can be used to design a supply chain to facilitate the mass customization of products, especially when information is incomplete and inaccurate (including uncertainties), and goals conflict with each other. In this paper, we focus on the method embodied in the framework rather than the results per se.

Process optimization through closed-loop Kaizen with discrete event simulation: A case study in China

This article presents a closed-loop Kaizen system with discrete event simulation in the stage of design and reengineering of mass-customization production system. This approach contains four steps: current process analysis, production line design, discrete event simulation to solve the dynamic event questions, and implements and evaluation. The current process analysis and the production line design refer to the value stream mapping and the productivity analysis based on Kaizen principle, respectively. Production line designs are the modeling basis of discrete event simulation, and discrete event simulation feeds back results to the decision-makers to reduce waste and cost and improve the quality and productivity by reforming the production line. Therefore, by connecting the closed-loop Kaizen with discrete event simulation, the production line can be continuously and automatically improved. To demonstrate the potentials of the closed-loop Kaizen system, a case study was applied based on the original production layout and economic benefits were achieved. Moreover, this article indicates a direction for the full automation of process optimization.

Monitoring of Assembly Process Using Deep Learning Technology.

Monitoring the assembly process is a challenge in the manual assembly of mass customization production, in which the operator needs to change the assembly process according to different products. If an assembly error is not immediately detected during the assembly process of a product, it may lead to errors and loss of time and money in the subsequent assembly process, and will affect product quality. To monitor assembly process, this paper explored two methods: recognizing assembly action and recognizing parts from complicated assembled products. In assembly action recognition, an improved three-dimensional convolutional neural network (3D CNN) model with batch normalization is proposed to detect a missing assembly action. In parts recognition, a fully convolutional network (FCN) is employed to segment, recognize different parts from complicated assembled products to check the assembly sequence for missing or misaligned parts. An assembly actions data set and an assembly segmentation data set are created. The experimental results of assembly action recognition show that the 3D CNN model with batch normalization reduces computational complexity, improves training speed and speeds up the convergence of the model, while maintaining accuracy. Experimental results of FCN show that FCN-2S provides a higher pixel recognition accuracy than other FCNs.

Inline Inspection with an Industrial Robot (IIIR) for Mass-Customization Production Line.

Robots are essential for the rapid development of Industry 4.0. In order to truly achieve autonomous robot control in customizable production lines, robots need to be accurate enough and capable of recognizing the geometry and orientation of an arbitrarily shaped object. This paper presents a method of inline inspection with an industrial robot (IIIR) for mass-customization production lines. A 3D scanner was used to capture the geometry and orientation of the object to be inspected. As the object entered the working range of the robot, the end effector moved along with the object and the camera installed at the end effector performed the requested optical inspections. The detailed information about the developed methodology was introduced in this paper. The experiments showed there was a relative movement between the moving object and the following camera and the speed was around 0.34 mm per second (worst case was around 0.94 mm per second). For a camera of 60 frames per second, the relative moving speed between the object and the camera was around 6 micron (around 16 micron for the worst case), which was stable enough for most industrial production inspections.

An Approach of Similarity Measurement for Heterogeneous and Unordered Mass Customization Products

The heterogeneous and unordered characters of mass customization products cause a huge challenge to analyze the similarity among them. This paper mainly deals with the similarity issues among heterogeneous and unordered mass customization products. Experts’ scoring and local differences are introduced to alleviate the difficulties of evaluating the changes caused by structural operations. And the quantitative correlations between local differences and product level characters such as function or/and performance are established through deep learning. On the basis of these, an approach of similarity analysis for heterogeneous and unordered mass customization products is proposed. It provides practical methods for designer to divide heterogeneous and unordered mass customization products into reasonable groups effectively.

Synergistic effect of lean practices on lead time reduction: mediating role of manufacturing flexibility

PurposeThis study scrutinized the synergistic effects of lean manufacturing (LM) on lead time reduction (LR) while investigating the mediating role of manufacturing flexibility (MF) in that relationship within the context of batch and mass customization manufacturers.Design/methodology/approachThis cross-sectional survey involved 160 large batch and mass customization manufacturers in Indonesia. Data were analyzed by using the PLS path modeling approach and multigroup analysis.FindingsThe positive synergistic direct effects of LM on LR and MF were revealed in both process types. In mass customization, MF mediates the effect of LM on LR. However, such a mediating effect was not found in the batch process due to the insignificant effect of MF on LR.Practical implicationsThe findings offered theoretical and practical insights supporting the manufacturers to grasp potential benefits through the holistic LM implementation as well as the suitable strategies to improve MF and reduce lead time by considering the types of the production process.Originality/valueThis study bridged the gaps regarding the comparison of LM implementation and its influence on MF and LR in mass customization and batch production.

Streaming information integration unit for mass customization

With the development of the information industry, there are a large number of software systems in enterprises. Due to the lack of top-level design, the data assets in these systems have formed data silos. Under the trend of mass customization production, it is of great significance to combine factory manufacturing data with user data to connect traditional information silos into user-centric intelligent production lines. Traditional offline batch processing tools cannot real-time and automated decision-making in mass customization. Based on the streaming programming model and distributed computing technology, this paper proposes a streaming information integration unit for mass-customized industrial Internet platforms. This unit provides information sharing capabilities in the process of enterprise information management, eliminates information blind spots in the service and workshop layers, and integrates internal information resources of the enterprise. Through the realization of two-way real-time transmission of production information, the user experience is improved and the delivery cycle of mass customization is shortened. In addition, data acquisition tests are performed using factory line temperature sensor data. The experimental results show that compared with the traditional batch processing scheme, the acquisition delay of this scheme is much lower than that of traditional batch processing tools, which provides a feasible solution for information integration in mass customization systems.

An Entropy-Based Formulation for Assessing the Complexity Level of a Mass Customization Industry 4.0 Environment

It has been stated that Industry 4.0’s goal is, among others, the sustainable success in a market characterized by exigent and informed consumers demanding personalized products and services, where the level of manufacturing complexity increases with level of product customization. Even though different manufacturing complexity measures have been developed, there seems to be a lack of a comprehensive metric that address both the mass customization variety-induced complexity, and the complexity derived from the adoption of the Industry 4.0 paradigm. The main original contribution of this paper is the development of an entropy-based (entropic) formulation to address this last issue. Its validity and usefulness is put to the test via a discrete-event simulation study of a mass customization production system operating within an Industry 4.0 context. Our findings show that the entropic formulation acts as a fairly good trend indicator of the system’s performance parameter increase/decrease, but not as an estimator of the final values. A discussion of the managerial implications of the obtained results is offered at the end of the paper.