* Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

Toyota production system : beyond large-scale production

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

It is considered by many that for industry to prosper into the 1990s it must adopt the principles of total quality control. These principles offer the opportunity to tap previously unused brainpower within an organisation, and to achieve what our potential overseas customers expect of us, that is, a guaranteed 100 per cent good product. This paper presents one company's ideas and methods of implementation. Tqc is very much a people thing, and requires employees at all levels to become familiar with, and practise basic charting and problem solving techniques. Economics do not allow us to load up our organisations with service departments. Quality must be controlled by the people at the workface (a).

Total quality control

The geometric error in the surface grinding process is mainly affected by the thermal effect and the stiffness of the grinding system. For minimizing the geometric error, the selection of grinding parameters is very important. This paper presented an application of Taguchi and response surface methodologies for the geometric error. The effect of grinding parameters on the geometric error was evaluated and optimum grinding conditions for minimizing the geometric error were determined. A second-order response model for the geometric error was developed and the utilization of the response surface model was evaluated with constraints of the surface roughness and the material removal rate. Confirmation experiments were conducted at an optimal condition and selected two conditions for observing accuracy of the developed response surface model.

Application of Taguchi and response surface methodologies for geometric error in surface grinding process

Prognostics and health management is an emerging discipline to scientifically manage the health condition of engineering systems and their critical components. It mainly consists of three main aspects: construction of health indicators, remaining useful life prediction, and health management. Construction of health indicators aims to evaluate the system’s current health condition and its critical components. Given the observations of a health indicator, prediction of the remaining useful life is used to infer the time when an engineering systems or a critical component will no longer perform its intended function. Health management involves planning the optimal maintenance schedule according to the system’s current and future health condition, its critical components and the replacement costs. Construction of health indicators is the key to predicting the remaining useful life. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. Because it is difficult to measure and quantify the health conditions of bearings and gears in many cases, numerous vibration-based methods have been proposed to construct bearing and gear health indicators. This paper presents a thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning. This review paper will be helpful for designing further advanced bearing and gear health indicators and provides a basis for predicting the remaining useful life of bearings and gears. Most of the bearing and gear health indicators reviewed in this paper are highly relevant to simulated and experimental run-to-failure data rather than artificially seeded bearing and gear fault data. Finally, some problems in the literature are highlighted and areas for future study are identified.

Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators

Service system design process is complex in nature and involves many stakeholders. The process becomes more critical in the context of machine tools due to market competition, complexity of services, competency requirements and cost constraints. The objective of this paper is to propose a generic approach for the design of a service system for machine tools which can support a variety of business models along with meeting customers' demand for flexibility. The proposed approach makes use of fundamental service design steps and offers flexibility to the customers to choose the services, service mechanisms, service levels and service frequencies as per their requirements considering the economic aspects, and thus underlines the involvement and participation of customers in the service design process. It also facilitates service designers to visualize the roles of stakeholders involved in the service process and highlights the importance of customer involvement for customer service assurance and better service experience. Application of the approach is discussed through a case study conducted in association with a leading Indian machine tool manufacturing group.

Designing flexible service systems: application to machine tools

Supportability for a user is the ability of the support provider to execute all the required activities for upkeep of the system in the most effective, efficient and timely manner throughout the system’s operating life. From the support provider’s perspective, design, maintenance services and logistics along with appropriate contract help in delivering the required support in a profitable manner. However, from the user’s point of view, apart from uninterrupted availability of the product, the life cycle cost (LCC) associated with the product is of immense importance. Hence, a tradeoff between the supportability and the corresponding LCC is to be achieved.

Although a vast amount of literature is available on the subject, yet an integrated approach that considers all parameters affecting supportability and LCC in obtaining an optimal solution needs to be developed. This paper presents a framework for analysing supportability along with a proposed methodology that can help in arriving at the optimal solution.

A conceptual framework for analysing, improving and optimising supportability of mechanical systems

Condition-based maintenance (CBM) involves taking decisions on maintenance or repair based on the actual deterioration conditions of the components. The long-run average cost is minimised by choosing the right maintenance action at the right time. In this study, the CBM decision-making problem is modelled as a continuous semi-Markov decision process (CSMDP). It consists of a chain of states representing various stages of deterioration, a set of maintenance actions, their costs and scheduled inspection policy. The application of a reinforcement learning (RL) algorithm based on the average reward for CSMDPs in CBM is described. The RL algorithm is used to learn the optimal maintenance decisions and inspection schedule based on the current health state of the component.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cim.2020.0022

Reinforcement learning for optimal policy learning in condition-based maintenance

The quality of a continuously cast product depends on the process condition, which, in turn, depends on process parameters To be able to set the correct parameter values, it is important that behavior of the steel being cast under different casting conditions is well understood This will need the knowledge of the mechanical behavior of steels at temperatures near the solidus This paper presents, in detail, how these properties were modeled as a function of steel grade, temperature, and stresses that arise during casting These models have been used to develop an integrated system that is capable of simulating various process conditions that may be encountered during continuous casting The details of how these models were developed, verified, and used to develop the integrated system are presented in this paper

A System of Process Models for Estimating Parameters of Continuous Casting Using Near Solidus Properties of Steel

Industry 4.0 consisting of smart, connected and autonomous manufacturing systems is fast becoming a reality. In view of this, industries are making radical changes in their manufacturing systems to adapt to new and evolving climate of manufacturing. Secondly, the Industry 4.0 implementation is bridging the differences between product design and design of product service systems (PSS). One can see a strong and inevitable coupling among product design, process design and production system design. All these aspects call for revisiting new product development (NPD) processes and to modify the same in the context of new manufacturing settings. This paper identifies key changes needed in NPD process and proposes a new structure to derive value accrued by embracing Industry 4.0 technologies. The research has been carried out in the context of machine tool industry and proposed changes have been ratified by industry experts.

Makarand S. Kulkarni

Papers

Designing flexible service systems: application to machine tools

A conceptual framework for analysing, improving and optimising supportability of mechanical systems

Reinforcement learning for optimal policy learning in condition-based maintenance

A System of Process Models for Estimating Parameters of Continuous Casting Using Near Solidus Properties of Steel

New Product Development (NPD) Process in the Context of Industry 4.0