Predicting potential customer needs and wants for agile design and manufacture in an industry 4.0 environment

Manufacturing is currently experiencing a paradigm shift in the way that products are designed, produced and serviced. Such changes are brought about mainly by the extensive use of the Internet and digital technologies. As a result of this shift, a new industrial revolution is emerging, termed “Industry 4.0” (i4), which promises to accommodate mass customisation at a mass production cost. For i4 to become a reality, however, multiple challenges need to be addressed, highlighting the need for design for agile manufacturing and, for this, a framework capable of integrating big data analytics arising from the service end, business informatics through the manufacturing process, and artificial intelligence (AI) for the entire manufacturing value chain. This thesis attempts to address these issues, with a focus on the need for design for agile manufacturing. First, the state of the art in this field of research is reviewed on combining cutting-edge technologies in digital manufacturing with big data analysed to support agile manufacturing. Then, the work is focused on developing an AI-based framework to address one of the customisation issues in smart design and agile manufacturing, that is, prediction of potential customer needs and wants. With this framework, an AI-based approach is developed to predict design attributes that would help manufacturers to decide the best virtual designs to meet emerging customer needs and wants predictively. In particular, various machine learning approaches are developed to help explain at least 85% of the design variance when building a model to predict potential customer needs and wants. These approaches include k-means clustering, self-organizing maps, fuzzy k-means clustering, and decision trees, all supporting a vector machine to evaluate and extract conscious and subconscious customer needs and wants. A model capable of accurately predicting customer needs and wants for at least 85% of classified design attributes is thus obtained. Further, an analysis capable of determining the best design attributes and features for predicting customer needs and wants is also achieved. As the information analysed can be utilized to advise the selection of desired attributes, it is fed back in a closed-loop of the manufacturing value chain: design → manufacture → management/service → → → design... For this, a total of 4 case studies are undertaken to test and demonstrate the efficacy and effectiveness of the framework developed. These case studies include: 1) an evaluation model of consumer cars with multiple attributes including categorical and numerical ones; 2) specifications of automotive vehicles in terms of various characteristics including categorical and numerical instances; 3) fuel consumptions of various car models and makes, taking into account a desire for low fuel costs and low CO2 emissions; and 4) computer parts design for recommending the best design attributes when buying a computer. The results show that the decision trees, as a machine learning approach, work best in predicting customer needs and wants for smart design. With the tested framework and methodology, this thesis overall presents a holistic attempt to addressing the missing gap between manufacture and customisation, that is meeting customer needs and wants. Effective ways of achieving customization for i4 and smart manufacturing are identified. This is achieved through predicting potential customer needs and wants and applying the prediction at the product design stage for agile manufacturing to meet individual requirements at a mass production cost. Such agility is one key element in realising Industry 4.0. At the end, this thesis contributes to improving the process of analysing the data to predict potential customer needs and wants to be used as inputs to customizing product designs agilely

Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Self-organizing tool for smart design with predictive customer needs and wants to realize Industry 4.0

Following the first three industrial revolutions, Industry 4.0 (I4) aims at realizing mass customization at a mass production cost. Currently, however, there is a lack of smart analytics tools for achieving such a goal. This paper investigates this issues and then develops a predictive analytics framework integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a self-organizing map (SOM) is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The selection of patterns from big data with SOM helps with clustering and with the selection of optimal attributes. A car customization case study shows that the SOM is able to assign new clusters when growing knowledge of customer needs and wants. The self-organizing tool offers a number of features suitable to smart design that is required in realizing Industry 4.0

Benchmarks for evaluating optimization algorithms and benchmarking MATLAB derivative-free optimizers for practitioners’ rapid access

MATLAB ® has built in five derivative-free optimizers (DFOs), including two direct search algorithms (simplex search, pattern search) and three heuristic algorithms (simulated annealing, particle swarm optimization, and genetic algorithm), plus a few in the official user repository, such as Powell's conjugate (PC) direct search recommended by MathWorks ® . To help a practicing engineer or scientist to choose a MATLAB DFO most suitable for their application at hand, this paper presents a set of five benchmarking criteria for optimization algorithms and then uses four widely adopted benchmark problems to evaluate the DFOs systematically. Comprehensive tests recommend that the PC be most suitable for a unimodal or relatively simple problem, whilst the genetic algorithm (with elitism in MATLAB, GAe) for a relatively complex, multimodal or unknown problem. This paper also provides an amalgamated scoring system and a decision tree for specific objectives, in addition to recommending the GAe for optimizing structures and categories as well as for offline global search together with PC for local parameter tuning or online adaptation. To verify these recommendations, all the six DFOs are further tested in a case study optimizing a popular nonlinear filter. The results corroborate the benchmarking results. It is expected that the benchmarking system would help select optimizers for practical applications

The Effect of Interrupted Homogenization on β-Al₅FeSi → α-Alx (Fe and Mn) Si Transformation in the A6063 Aluminum Alloy

The aluminum alloys corresponding to the 6000 series are mainly manufactured by mechanical forming processes. Their properties are enhanced by the homogeneous distribution of intermetallic phases such as β-Al5FeSi or α-Alx (Fe, Mn) Si. By thermal homogenization treatment, the intermetallic compound β-Al5FeSi changes its morphology from a needle type with a monoclinic structure to an acicular form known as α-Al12(Fe, Mn)3Si with an fcc structure. In the present study, samples of the 6063 alloy were subjected to different temperatures of homogenization (798, 823, and 848 K) and treatment times from 0 to 660 min (in intervals of 30 min) to evaluate their effects on the microstructures and morphologies of the intermetallic phases. For the kinetic study, the microstructures of the β and α intermetallic phases were quantified using the Image-Pro software. The results indicate that as the temperature and homogenization time increase, the percentage of phase α also increments. The results of the kinetic analysis revealed that the β → α transformation is controlled by two stages; the first corresponds to the diffusion of Mn atoms from the matrix to the interface of reaction for the formation of the intermetallic phases, while the second corresponds to the nucleation and growth of the iron- and manganese-rich intermetallic phases

The Effect of Interrupted Homogenization on β-Al5FeSi → α-Alx (Fe and Mn) Si Transformation in the A6063 Aluminum Alloy

The aluminum alloys corresponding to the 6000 series are mainly manufactured by mechanical forming processes. Their properties are enhanced by the homogeneous distribution of intermetallic phases such as β-Al5FeSi or α-Alx (Fe, Mn) Si. By thermal homogenization treatment, the intermetallic compound β-Al5FeSi changes its morphology from a needle type with a monoclinic structure to an acicular form known as α-Al12(Fe, Mn)3Si with an fcc structure. In the present study, samples of the 6063 alloy were subjected to different temperatures of homogenization (798, 823, and 848 K) and treatment times from 0 to 660 min (in intervals of 30 min) to evaluate their effects on the microstructures and morphologies of the intermetallic phases. For the kinetic study, the microstructures of the β and α intermetallic phases were quantified using the Image-Pro software. The results indicate that as the temperature and homogenization time increase, the percentage of phase α also increments. The results of the kinetic analysis revealed that the β → α transformation is controlled by two stages; the first corresponds to the diffusion of Mn atoms from the matrix to the interface of reaction for the formation of the intermetallic phases, while the second corresponds to the nucleation and growth of the iron- and manganese-rich intermetallic phases

Symmetric powers of modular representations, Hilbert series and degree bounds

Let G=Z_p be a cyclic group of prime order p with a representation G#->#GL(V) over a field K of characteristic p. In 1976, Almkvist and Fossum gave formulas for the decomposition of the symmetric powers of V in the case that V is indecomposable. From these they derived formulas for the Hilbert series of the invariant ring K[V]"G. Following Almkvist and Fossum in broad outline, we start by giving a shorter, self-contained proof of their results. We extend their work to modules which are not necessarily indecomposable. We also obtain formulas which give generating functions encoding the decompositions of all symmetric powers of V into indecomposables. Our results generalize to groups of the type Z_p x H with vertical stroke H vertical stroke comprime to p. Moreover, we prove that for any finite group G whose order is divisible by p but not by p"2, the invariant ring K[V]"G is generated by homogeneous invariants of degrees at most dim(V) x (vertical stroke G vertical stroke -1). (orig.)SIGLEAvailable from TIB Hannover: RR 1606(99-12) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman